The widespread adoption of Internet of Things (IoT) devices increases the need for trust management systems that adapt to dynamic conditions and maintain reliability under diverse threats. This paper introduces StackTrust, a trust management framework designed for scalable and precise IoT security. The framework integrates decision trees, support vector machines, and random forests within a logistic regression meta-learner to enhance classification robustness. A central feature is the adaptive weighting mechanism, which periodically adjusts the influence of each base model according to current performance metrics. To further stabilize predictions, a logarithmic historical-trust function incorporates long-term behavioral evidence while reducing sensitivity to short-term fluctuations. The combined trust score converges to a stable equilibrium under bounded model outputs. StackTrust supports both centralized and decentralized architectures and is validated through NS-3 simulations across multiple datasets and attack scenarios. Results on 45,000 instances confirm precision, recall, and F1-scores of 0.99, with computational complexity ofO(N×T) andO(M×T) to ensure efficiency for resource-constrained IoT environments.

This paper explores the use of reconfigurable intelligent surface (RIS) in the integrated terrestrial-space network (ITSN) to mitigate co-channel interference between low earth orbit (LEO) satellite systems and terrestrial base stations (BS), with a focus on minimizing total power consumption. An alternating optimization framework is developed, decomposing the problem into three subproblems: BS beamforming design solved via second-order cone programming (SOCP) with interior-point methods or zero-forcing (ZF) method with water-filling algorithm, RIS phase-shift optimization addressed through semi-definite relaxation (SDR) combined with Gaussian randomization to handle non-convex unit-modulus constraints, and LEO satellite transmit signal power optimization addressed by water-filling algorithm. Simulation results show that compared to conventional non-RIS architectures, the proposed system achieves significant power reductions: 14–16 dB for BS and 8.68 dB for satellites. Compared to the WMMSE-based method, it reduces interference at BS users by 4–6 dB and at SU by 15–20 dB. When the satellite power is fixed, it still reduces the power consumption of the BS by 6-8 dB. The proposed methodology enables harmonious coexistence between terrestrial BSs and LEO satellite service zones under strict power budgets through enhanced interference decoupling capabilities.

The Unmanned Aerial Vehicle (UAV)-assisted networks play a vital role in disaster circumstances for quick rescue operations by enabling emergency communication services. Unlike conventional networks, emergency networks have unique challenges, such as encountering Critical Nodes (CNs) that contain vital information. The effectiveness of rescue operations mainly depends on the coverage of these CNs to retrieve essential data for coordinating rescue efforts. In this context, Age-of-Information (AoI) is used to evaluate the timely collection of data from CNs. Voronoi diagram-based partitioning is employed as an adaptive mechanism linked with UAV swarm size and K-means clustering to enable nodes distribution-aware spatial partitioning, ensuring collision-free path planning in disaster scenarios. The distance-optimized CNA trajectory is proposed to optimize UAV swarm paths for coverage maximization and AoI minimization by adapting the scalarization approach. The performance of the proposed algorithm is analyzed based on coverage, AoI, trajectory length, and total flight time. Simulation results show that the proposed distance-optimized CNA trajectory outperforms the conventional distance-based and the CNA trajectory by 25.27% and 41.20%, respectively. It improves the CNs coverage and AoI of distance-based trajectory by 30% and 10%, priority-based Traveling Salesman Problem (TSP) by 47% and 7%, and CNA trajectory by 13% and 17%, respectively. When the percentage of CNs increases from 10% to 40%, the number of covered CNs increases linearly for a given number of hovering points.

The rapid proliferation of the Industrial Internet of Things (IoT), particularly in smart manufacturing, necessitates intent-driven edge intelligence to provide highly reliable, low-latency computational services for a vast array of connected devices. However, inherent challenges such as the limited computational capacity of IoT devices and unpredictable wireless channels hinder the development of such networks. To address these obstacles, this paper presents an intent-driven optimization framework that integrates reconfigurable intelligent surface (RIS) and wireless power transfer (WPT) into an IoT edge computing architecture. Following the principles of intent-based networking (IBN), our framework targets binary task offloading for energy-constrained devices by translating the specific high-level intent of maximizing the total computational rate into a concrete system-level objective. We formulate this objective as a mixed-integer nonlinear programming problem and propose a Deep Reinforcement Integrated Optimization (DRIO) framework to solve it under dynamic network conditions. DRIO effectively decouples the high-dimensional action space, employing a double deep Q-network for coarse RIS phase control, a combination of gradient search and element-wise local search for fine-tuning RIS phases, and a dedicated deep reinforcement learning component for managing binary offloading decisions. Simulation results confirm that DRIO surpasses traditional and standard reinforcement learning methods by successfully fulfilling the intent of maximizing computational rate, showing adaptability to dynamic environments and energy harvesting fluctuations. This study enhances IBN for industrial IoT through AI-powered resource allocation, thereby boosting scalability and efficiency in next-generation communications.

This letter investigates blocked job offloading in a satellite-ground integrated network, which allows each low-earth orbit (LEO) satellite to execute more jobs on-board instead of sending the raw data to ground users. We propose an approximation offloading model to approximate the satellite-ground integrated network and analyze the average execution time, which is defined as when the user receives the raw data or processed result from the LEO satellite. Furthermore, we propose the maximum equivalent arrival rate and blocked job offloading algorithms. Numerical results validate the approximation model’s effectiveness and the proposed algorithms’ performance advantages and indicate a way to select the proper algorithm.

Wi-Fi Channel State Information (CSI) has become a widely studied modality for device-free sensing as it captures fine-grained wireless channel variations that can be mapped to human motion and presence while avoiding the explicit visual disclosure typical of vision-based systems. CSI-based pipelines have been explored for human activity and gesture recognition, fall detection, gait analysis, pose-related inference, and indoor localization. Despite strong results in controlled settings, practical deployment remains difficult due to measurement noise, sensitivity to environmental dynamics, multi-user interference, and system-level constraints in data acquisition and real-time processing. This article surveys machine learning methods forWi-Fi CSI sensing and analyzes more than 65 representative models, connecting algorithmic design choices with implementable end-to-end system design. We introduce a hierarchical taxonomy that organizes the literature into classical machine learning approaches, deep learning architectures, and hybrid strategies. Beyond modeling, we describe the full sensing pipeline- from hardware and network interface card (NIC) selection to software tools, antenna configuration, and signal conditioning- highlighting the design trade-offs that affect robustness and reproducibility. We further compare methods across major application domains and summarize open challenges in generalization to dynamic environments, multi-user separation, and resource-efficient inference. Finally, we outline research directions toward robust generalization, scalable deployment, and privacy-aware learning to support broader real-world adoption.

Accurate anomaly recognition in video surveillance is critical to ensuring public safety, infrastructure protection, and intelligent security operations. However, existing methods often fail to generalize across diverse and dynamic environments due to their limited capacity to model complex spatio-temporal patterns. In this paper, we propose a novel Graph-Based Temporal Attention Memory Network (G-TAMNet) that significantly advances video anomaly detection by capturing intricate temporal dependencies and spatial relationships. By integrating temporal self-attention into a graph convolutional network (GCN) framework, our model enhances the learning of salient and contextually relevant features that traditional approaches tend to overlook. Furthermore, to enable deployment in resource-constrained settings, such as IoTbased surveillance systems, we incorporate model quantization strategies that reduce computational overhead without compromising detection accuracy. Extensive evaluations of three widely used benchmarks, UCFCrime, LAD-2000, and RWF-2000 the effectiveness of the proposed G-TAMNet model, achieving accuracy, precision, recall, and F1-scores of 54.3% / 61.1% / 59.01% / 61.1%,79.3% / 69.1% / 70.0% / 69.1%,and 94.0% / 94.0% / 94.2% / 94.1%, respectively. These results reflect consistent performance gains of 3.3%, 9.9%, and 0.74% in accuracy over existing state-ofthe-art methods on the corresponding datasets. Such improvements underscore the robustness, scalability, and practical viability of GTAMNet for real-time anomaly detection in resource-constrained IoT surveillance systems. In addition, its reliable generalization across diverse environments highlights the potential of the model to advance intelligent security analytics and transform real-world applications in the domain of information forensics.

Human activity prediction is crucial for enabling intelligent smart home services, yet it is often hindered by the scarcity of high-quality, multi-dimensional datasets. Existing datasets are typically fragmented, capturing either long-term activity sequences or short-term device interactions, but rarely both in a unified manner. Traditional data collection methods are costly and time-consuming, while conventional simulation techniques struggle to generate diverse and logically coherent behavior sequences. To address these limitations, we propose SmartLLM, a novel Large Language Model (LLM)-based simulation framework for automated generation of multi-dimensional smart home datasets. SmartLLM simulates simulated agents with distinct profiles (e.g., old man, remote worker, holiday maker) performing daily activities within configurable home environments, generating temporally aligned sequences across Activity-Device-Sensor dimensions. We generate two months of simulated data for three user profiles and validated their plausibility through activity distribution visualization, statistical perplexity analysis, and case studies. Multi-dimensional feature validation experiments further demonstrate that our multi-dimensional data significantly enhances the accuracy of activity prediction models compared to using single-dimensional features. This work successfully addresses key bottlenecks in smart home data acquisition and provides a scalable, high-quality data foundation for advancing smart home algorithm research. The code is available at https://github.com/HuankeZheng/SmartLLM.

Chaotic waveforms, characterized by their noise-like properties and potential security advantages, have emerged as a promising solution for physical layer security (PLS) in wireless communication schemes. However, conventional differential chaos shift keying (DCSK) schemes suffer from limitations such as low data rate, energy inefficiency, and vulnerability to frequency-selective fading (FSF) channels. To address these challenges, this paper proposes a new matrix index technique for multi-carrier DCSK scheme, referred to asMIT-MC-DCSKscheme, tailored for robust and secure transmission over FSF channels. The proposed MIT-MC-DCSK scheme enables time-frequency diversity to mitigate the negative effect of fading while enhancing bit error rate (BER) performance. Theoretical BER expressions of the proposed scheme are rigorously derived over FSF channels, which are validated by extensive Monte Carlo simulations. Experimental results demonstrate that the proposed MIT-MC-DCSK scheme achieves superior BER performance and higher transmission efficiency than the state-of-the-art benchmark schemes. Furthermore, the proposed MIT-MC-DCSK scheme significantly reduces information leakage and improves secrecy capacity by leveraging matrix reconstruction technique, making it resilient against eavesdropping. Thanks to these advancements, the MIT-MC-DCSK scheme appears to be a robust candidate for secure and reliable communication in FSF scenarios.

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving model training across distributed edge devices, enabling local data utilization without explicit sharing. However, in edge computing environments characterized by heterogeneous resources and intermittent connectivity, FL remains vulnerable to gradient leakage attacks (GLA), where adversaries reconstruct private data from shared model updates. Although the existing defenses, such as differential privacy (DP) and gradient compression, offer partial mitigation, they often result in significant performance degradation or increased communication overhead. In this paper, we analyze that the risk of privacy leakage is highly sensitive to the client-side training configurations and gradient magnitudes. Based on this, we propose a risk-aware FL framework tailored for the edge scenarios, which not only performs per-device privacy risk assessment but also introduces subtractive dithering quantization to the inject controllable Gaussian noise into local models. Additionally, a noise-aware aggregation strategy is presented by adjusting each client’s contribution to preserve the global model utility. Experimental results on FashionMNIST and CIFAR-10 demonstrate that the proposed framework achieves strong defense against the GLA, reduces the communication costs by over 50%, and maintains the competitive accuracy.

In this paper, the covert performance of the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided Terahertz (THz) communication systems with rate-splitting multiple access (RSMA) over the generalized α-μ fading channel is studied, where the discrete phase error and noise power uncertainty are considered. By means of the Gaussian approximation and Gamma distribution, the closed-form outage probability (OP) of the legitimate users is firstly derived. Then, the covert performance of the system is analyzed, and average detection error probability (ADEP) is deduced for the warden, and resultant closed-form ADEP is obtained. By minimizing the ADEP, the optimal detection threshold is derived with closed-form expression. With these results, subject to the constraints of covertness and reliability, the power allocation (PA) coefficients are optimized to minimize the OP of the covert user. Two adaptive PA schemes based on two-dimensional (2D) and one-dimensional (1D) search methods are respectively proposed to achieve the solutions, and resulting lower OP is attained. Simulation results indicate that the theoretical OP and ADEP can agree the corresponding simulations well, and randomizing the noise power of warden can improve the covert performance. Moreover, the warden with the optimized threshold can obtain lower ADEP than that with a fixed threshold, and the system with two PA schemes has a lower OP than that with a fixed PA, especially the scheme based on 1D search has lower complexity while maintaining the superior performance.

As large-scale systems continue to grow in complexity, effectively leveraging the multi-dimensional information contained in semi-structured logs has become a critical challenge for ensuring system stability. Existing methods rely on limited log content, which restricts semantic modeling capability and leads to degraded performance when abnormal samples are scarce or data distributions are imbalanced. To address these challenges, we propose RELog, an efficient and robust anomaly detection framework that comprehensively utilizes multi-dimensional log features. RELog adopts a hierarchical process for efficient log parsing and anomaly detection. During log parsing, Large Language Models are introduced to enhance the semantic understanding of low-confidence templates generated by the heuristic method. For anomaly detection, we design dedicated encoders for templates, parameters, and time features, tailored to the low complexity and high redundancy of logs, enabling discriminative embeddings. Furthermore, we propose an efficient hybrid sequence encoder integrating state-space modeling and attention for capturing continuous log patterns and critical dependencies, complemented by a replacement classification task for imbalanced data. Experimental results demonstrate that RELog achieves high-accuracy log parsing and anomaly detection while maintaining efficiency. Moreover, RELog demonstrates robustness across varying anomaly ratios and dynamic environments, effectively detecting diverse types of anomalies reflected by parameter and time features.

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) schemes with user revocation allow for dynamic updates to users' access rights. However, existing schemes often encounter issues such as low re-encryption efficiency, inflexible access control, and susceptibility to collusion attacks. To address these challenges, we propose UR-CP-ABE, an efficient user revocation scheme built upon a double encryption method. Specifically, UR-CP-ABE stores both valid and revoked users' identity information in a binary tree. This design enables flexible user authority revocation by modifying only the binary tree's relevant secret sub-items. Moreover, the scheme limits the scope of revocation-induced binary tree updates to a single sub-item. This key optimization resolves the critical issue where re-encryption overhead scales linearly with the number of attributes of revoked users. In addition, we eliminate the possibility of attackers constructing secret sub-keys, preventing the collusion attacks. UR-CP-ABE also supports bidirectional revocation, allowing for the revocation and restoration of user rights, which is not available in other related schemes. Our theoretical analysis and experiments demonstrate that UR-CP-ABE has better performance than other schemes, especially in the re-encryption stages. Moreover, UR-CP-ABE has been proved to be secure based on the decisionalq-parallel BDHEhardness assumption in the standard model.

Graph Neural Networks (GNNs) with differential privacy have been proposed to preserve graph privacy when nodes represent personal and sensitive information. However, existing methods ignore that nodes with different importance may yield diverse privacy demands, which may lead to over-protecting some nodes and decrease model utility. In this paper, we study the problem of importance-grained privacy, where nodes contain personal data that need to be kept private but are critical for training a GNN. We propose NAP-GNN, a node-importance-grained privacy-preserving GNN algorithm with privacy guarantees based on adaptive differential privacy to safeguard node information. First, we propose a Topology-based Node Importance Estimation (TNIE) method to infer unknown node importance with neighborhood and centrality awareness. Second, an adaptive private aggregation method is proposed to perturb neighborhood aggregation from node-importance-grain. Third, we propose to privately train a graph learning algorithm on perturbed aggregations in an adaptive residual connection mode over multi-layer convolution for node- wise tasks. The theoretical analysis shows that NAP-GNN can guarantee privacy. Empirical experiments over five real-world graph datasets show that NAP-GNN achieves a better trade-off between privacy and accuracy.

The serverless architecture has attracted significant attention due to its cost-effectiveness and ease of management. However, the serverless framework increases the attack surface of applications, resulting in frequent security incidents. Consequently, conducting comprehensive attack investigation and analysis in serverless applications has become critically important. Current serverless investigation methods face challenges such as dependency explosion (DE), incomplete information records, and lack of user transparency. These challenges lead to inadequate visibility of application interaction behaviors, complicating effective attack investigation and analysis. To mitigate these issues, this paper introducesErinyes, a solution that facilitates request-level attack investigation and analysis in serverless environments through the construction of provenance graph.Erinyesimproves the visibility of serverless applications via three core components. The partition enabling module effectively partitions function operations based on incoming requests; the log collection module is responsible for aggregating audit and network logs pertinent to function operations; and the provenance graph builder consolidates and parses the collected logs into a comprehensive provenance graph.Erinyeshas been evaluated on the OpenFaaS platform in 5 distinct attack scenarios, achieving an average accuracy of 99.6% in execution partition, a completeness of 100% in provenance graph, and an average runtime overhead of 7.05%.

Enhanced by inter-satellite links and satellite direct-to-device capabilities, satellite networks can offer low-latency communication globally. However, limited spectrum resources and the capacity bounds of the Shannon's information theory pose fundamental challenges for supporting bandwidth-intensive multimedia services. Semantic communication (SemCom) offers a promising solution by transmitting compressed semantic representations instead of raw data, thereby alleviating bandwidth pressure. However, it also introduces SemCom-related constraints that render conventional schemes such as contact graph routing inapplicable. To overcome this challenge, we investigate SemCom-compliant path selection and formulate it as a non-NP hard mixed-integer linear programming problem. To address the problem, we develop a graph-based scheme that exploits the special structure of the solution space, the sparsity of SemCom-capable satellites, and the property of Dijkstra's algorithm, thus achieving optimal solutions with polynomial-time complexity. Simulation results on the Starlink constellation confirm that the proposed scheme facilitates SemCom with negligible computational overhead and significant bandwidth reduction. While the bandwidth reduction comes at the cost of increased delay and path hops, these effects are shown to be mitigatable through higher SemCom deployment in a satellite network or by enabling semantic processing at the user side.

To address the limitations of existing wireless networks for demanding applications like brain-computer interfaces and intelligent transportation systems, we propose an advanced framework for joint resource allocation and task offloading across integrated terrestrial and non-terrestrial networks (TN-NTN). This framework utilizes multiple layers, including ground users, UAVs, HAPs, and satellites, to improve service quality and immersive experiences, particularly in scenarios like Metaverse applications. Ground users request resources, while UAVs and HAPs serve as resource providers, and satellites ensure reliable communication during emergencies. A double auction-based incentive scheme is employed in which operators control UAV and HAP resources to maximize utility, and users aim to minimize computation costs and protect data privacy. To handle the complexity of the operator-user interaction, which results in an NP-hard optimization problem, we applied a hierarchical multi-agent federated deep reinforcement learning (FeDRL) approach. Our simulation results demonstrate that the FeDRL algorithm significantly improves social welfare by 6.38%, 17.43%, and 28.73% over modified MADDPG, FRL, and DDPG algorithms, respectively.

Generalized Frequency Division Multiplexing (GFDM) is considered a strong candidate to replace Orthogonal Frequency Division Multiplexing (OFDM) in 5G MIMO networks because of its enhanced spectral utilization and design flexibility. Despite these advantages, GFDM faces the drawback of producing a relatively high Peak-to-Average Power Ratio (PAPR), which limits the efficiency of power amplifiers. To address this issue, the Partial Transmit Sequence (PTS) method is often employed for PAPR reduction. Nevertheless, the effectiveness of PTS is hindered by the intensive computational effort required for searching multiple phase factors. To overcome this challenge, we propose a method that integrates the Enhanced Squirrel Search Algorithm (ESSA) with an adaptive parameter control mechanism and a Grey Wolf Optimizer (GWO), enabling a dynamic balance between exploration and exploitation during phase factor selection. This improvement reduces the computational overhead, accelerates the convergence, and enhances the robustness of the phase sequence optimization. Simulation results show that the Hybrid PTS-ESSA-GWO-RPSM model achieves superior PAPR reduction compared to conventional ESSA-based approaches, while also providing better BER and SNR performance under varying channel conditions. The proposed method therefore offers an efficient trade-off between complexity and PAPR reduction, making it suitable for practical deployment in MIMO-GFDM-based 5G systems. The proposed scheme is evaluated against related methods by analyzing key performance indicators, including Complementary Cumulative Distribution Function (CCDF), Bit Error Rate (BER), Peak-to-Average Power Ratio (PAPR), and Signal-to-Noise Ratio (SNR).

To meet the demands of high-capacity and low-delay services, Fifth Generation (5G) Base Stations (BSs) are typically deployed in ultra-dense configurations, especially in urban areas. While this densification enhances coverage and service quality, it also leads to substantially increased energy consumption. However, the dense deployment pattern makes BS workloads more responsive to the spatiotemporal variations in user behavior, offering opportunities for energy-saving strategies that dynamically adjust BS operation states. In this context, we propose a Delay-aware and Energy-efficient Integrated Optimization System (DEIS) based on Deep Reinforcement Learning (DRL), which jointly optimizes energy consumption and network delay while maintaining user satisfaction. DEIS leverages a real-world dataset collected from operational 5G BSs provided by partner network operators, containing both BS deployment data and high-volume user request logs. Extensive simulations demonstrate that DEIS can achieve a 41% reduction in energy consumption while ensuring reliable delay performance.

Industrial Internet applications require networks to guarantee deterministic end-to-end latency and zero packet loss at both the data link and network layers. Traditional best-effort communication models in consumer networks are insufficient to meet these stringent demands. To meet these stringent demands, the IEEE 802.1 standards introduce Time-Sensitive Networking (TSN) at the data link layer, while the IETF proposes Deterministic Networking (DetNet) for the network layer. However, enabling seamless cross-domain communication between TSN and DetNet remains a significant challenge. This paper proposes a unified cross-domain network architecture and a time-slot alignment strategy that compensates for synchronization errors between the TSN and DetNet layers. We further develop a Joint Routing and Scheduling algorithm for Deterministic Cross-Domain Transmission (JRS-DCT), which simultaneously addresses routing and scheduling under cross-domain constraints. The algorithm leverages Cycle-Specified Queuing and Forwarding (CSQF) in DetNet and Cycle Queuing and Forwarding (CQF) in TSN to ensure bounded latency and deterministic transmission. Extensive simulations demonstrate that the proposed JRS-DCT algorithm significantly improves the scheduling success rate and effectively reduces network resource utilization compared to two baseline algorithms. These results validate the effectiveness and robustness of the proposed framework in supporting time-sensitive communication across heterogeneous network environments.

Large Language Models (LLMs) have enormous potential in healthcare, but we also need to be careful and implement strong safety measures. These advanced computer systems can understand complex medical questions written in everyday language and often provide accurate medical information. This article explores how the field has evolved, from the first language models to the LLMs we have now, specifically designed or adjusted for use in the medical world. We explore how LLMs can make important language-related tasks in healthcare more efficient and effective, such as identifying medical terms, understanding how different pieces of information are connected, drawing conclusions from written text, sorting documents, and answering medical queries. We also shed light on new and exciting ways LLMs can use more than just text, like combining written information with images or other types of data. We compare some of the best recent LLMs and discuss how they can be used in everyday medical situations. We also measure how well these models perform in the biomedical field, explaining what each measurement tells us and where it falls short. Last, by discussing the biggest hurdles LLMs face in healthcare—things like generating incorrect information (hallucinations), unfair biases, protecting patient privacy, making sure the models are dependable, and fitting these new tools into how doctors and nurses already work. We also provide a roadmap for future research. We aim to present a clear and fair picture of what LLMs can offer healthcare right now and what is necessary to use them safely and dependably. This includes explaining how researchers are working to make these models easier for doctors and patients to understand. We followed the PRISMA research method to guide our review of the field, from early language models to current LLMs adapted for clinical use.

Reconfigurable intelligent surface (RIS)-enabled integrated sensing and communication (ISAC) is emerging as a key 6G technology for improving spectral efficiency and enabling high-resolution sensing. However,sensing targets within the communication coverage may act as potential eavesdroppers and intercept confidential data. To address this challenge, this paper proposes a sensing-assisted secure beamforming framework to enhance physical-layer security (PLS). First, we design a closed-loop architecture that sequentially performs RIS cascaded CSI estimation, target direction-of-arrival (DoA) estimation, and a Cramér–Rao bound (CRB)-based sensing accuracy evaluation. We then introduce an angular-domain information leakage (ADIL) metric to characterize leakage within the target’s angular uncertainty region. Building on this metric, we formulate a weighted-sum utility to jointly optimize the communication rate and sensing CRB under an ADIL-suppression constraint. To solve the resulting non-convex problem, we develop a penalty dual decomposition (PDD)-augmented alternating optimization (AO) algorithm that iteratively updates the BS beamforming, RIS phase shifts, and sensing time allocation. Convergence and complexity analyses further demonstrate that PDD accelerates AO convergence and mitigates zig-zag updates caused by coupled variables. Simulation results verify that the proposed sensing-assisted secure beamforming scheme effectively suppresses ADIL at eavesdropper angles and enhances PLS. Moreover, the PDD-augmented AO achieves up to a 21.3% improvement in communication rate and a 5.2% reduction in CRB compared with conventional schemes.

With the increasing number of vehicles, the Internet of Vehicles (IoV) exhibits significant dense deployment characteristics, resulting in severe overlaps of communication areas among vehicles and complex interference. To address these issues, the resource allocation problem under complex interference in densely deployed IoV is investigated in this paper. Specifically, an interference hypergraph model is constructed to simultaneously analyze the complex interference relationships among multiple vehicles. Subsequently, the resource allocation problem is transformed into a vertex coloring problem on the hypergraph. Furthermore, a federated double dueling deep Q-Network algorithm is proposed to achieve conflict-free resource allocation while maximizing network throughput. Simulation experiments demonstrate that the proposed method achieves an average network throughput improvement of more than 17.93% compared to the baseline algorithms, showcasing superior network performance in densely deployed IoV scenarios.

Beamforming is a critical enabler of high-capacity, low-latency communication in 6G networks, leveraging massive Multiple-Input Multiple-Output (MIMO) technology to direct signals toward intended users while minimizing interference. Recent Deep Learning (DL) advancements have enhanced beamforming efficiency by enabling data-driven beam selection. However, adopting AI-based beamforming introduces security vulnerabilities, particularly against adversarial attacks that manipulate Channel State Information (CSI) to degrade communication performance. This study proposes a Large Language Model (LLM)-enhanced defense mechanism that detects and mitigates adversarial perturbations in CSI before the beamforming model processes them. The LLM functions as an intelligent anomaly detector, distinguishing between adversarially manipulated and legitimate inputs and refining CSI features to restore accurate beamforming decisions. Unlike adversarial training, this approach does not require retraining the beamforming model, offering a lightweight and scalable solution. Experimental results demonstrate that the LLM-based defense significantly reduces the impact of adversarial attacks, improving Mean Squared Error (MSE) and Achievable Rate metrics while maintaining a robustness ratio of 0.94. The proposed method enhances the security and reliability of AI-driven beamforming without modifying the underlying neural network, making it a practical and efficient defense for 6G communication systems.

We consider the problem of high-dimensional channel estimation in fast time-varying millimeter-wave MIMO systems with a hybrid architecture. By exploiting the low-rank and sparsity properties of the channel matrix, we propose a two-phase compressed sensing framework consisting of observation matrix completion and channel matrix sparse recovery, respectively. First, we formulate the observation matrix completion problem as a low-rank matrix completion (LRMC) problem and develop a robust rank-one matrix completion (R1MC) algorithm that enables the matrix and its rank to iteratively update. This approach achieves high-precision completion of the observation matrix and explicit rank estimation without prior knowledge. Second, we devise a rank-aware batch orthogonal matching pursuit (OMP) method for achieving low-latency sparse channel recovery. To handle abrupt rank changes caused by user mobility, we establish a discrete-time autoregressive (AR) model that leverages the temporal rank correlation between continuous-time instances to obtain a complete observation matrix capable of perceiving rank changes for more accurate channel estimates. Simulation results confirm the effectiveness of the proposed channel estimation frame and demonstrate that our algorithms achieve state-of-the-art performance in low-rank matrix recovery with theoretical guarantees.

As sixth-generation (6G) networks continue to evolve, the deployment of Machine Learning (ML) models in critical functions, such as millimeter-wave (mmWave) beam prediction, presents unique security challenges, particularly from adversarial attacks. Existing defenses against these attacks often have limitations, especially when gradient information is unavailable. This paper introduces the Lemur Optimizer for Adversarial Attacks (LO-Attack), a novel gradient-free approach inspired by swarm intelligence to enhance the robustness of 6G beam prediction models. By integrating LO-Attack into the adversarial training process, models become more resilient against both gradient-based and gradient-free attacks in dynamic, high-mobility environments. Experimental results from two 6G scenarios, encompassing indoor and outdoor settings, demonstrate that models trained with LOAttack achieve significant improvements in accuracy, resistance to adversarial perturbations, and computational efficiency. On average, adversarial training with LO-Attack enhances model robustness by 57.90%, outperforming traditional FGSM-based methods. These findings highlight LO-Attack's potential as an effective and scalable defense strategy for securing ML-driven applications in the complex 6G landscape.

In the dynamic vehicular metaverse, delivering a seamless user experience (UX) and effective human-machine interaction (HMI) is challenging due to vehicle mobility and varying resource needs. This paper introduces an adaptive resource allocation and twin migration framework using Multi-Agent Deep Reinforcement Learning (MADRL) for a multi-tier vehicular metaverse. The framework enables cooperative agents to dynamically allocate resources and migrate vehicle twins across vehicle, edge, and cloud layers, ensuring seamless UX and efficient HMI. The joint resource allocation and twin migration optimization problem is modeled as MDP and a hierarchical multi-agent deep deterministic policy gradient-with QMIX (MADDPG-Q) strategy is adopted to solve it, reducing latency and optimizing resource use. Moreover, the proposed framework is designed to be context-aware, adjusting HMI based on real-time conditions, and enhancing interaction quality. Simulation results show significant improvements in UX, latency reduction, and resource efficiency.

Unmanned aerial vehicles (UAVs) are proven useful for enabling wireless power transfer (WPT), resource offloading, and data collection from ground IoT devices in post-disaster scenarios where conventional communication infrastructure is compromised. As 6G networks emerge, offering ultra-reliable low-latency communication and enhanced energy efficiency, UAVs are poised to play a critical role in extending 6G features to challenging environments. The key challenges in this context include limited UAV flight duration, energy constraints, limited resources, and the reliability of data collection, all of which impact the effectiveness of UAV operations. Motivated by the need for efficient resource allocation and reliable data collection, we propose a solution using UAV swarms combined with reconfigurable intelligent surfaces (RIS) to optimize energy harvesting for IoT devices and enhance communication quality. We formulate the problem of resource optimization, UAVs-RIS trajectory planning, and RIS configuration as a mixed integer nonlinear programming optimization problem and solve it in a dynamic condition by transforming it into a Markov decision process and utilizing a deep reinforcement learning (DRL) approach based on proximal policy optimization (PPO) algorithm to solve it. Simulation results demonstrate that our framework outperforms traditional approaches, including the Actor-Critic (AC) algorithm and a greedy solution, achieving superior performance in energy harvesting efficiency, data collection, and communication reliability.

Millisecond-precision measurement of smart grid presents elevated demands for 6G sensing and communication capabilities. How to ensure timely, reliable, and green delivery of critical information remains a core challenge. In this paper, we address this issue by studying electric semantic short packet communication from a green integrated sensing and communication (ISAC) perspective. First, a novel information timeliness metric named peak age of incorrect semantics (PAoIS) is developed. It describes the entire lifetime of sensing, compression, encoding, transmission, and decoding. Then, a collaborative problem is formulated to jointly minimize PAoIS and ISAC energy consumption by optimizing sensing frequency and semantic compression ratio. An electric multimodal driven green collaborative optimization algorithm is proposed. It enables dynamical adjustment of sampling ratio of electric multimodal experience samples, enhancing optimization performance under sparse modes. Simulation results verify the effectiveness of the proposed algorithm.

The integration of terrestrial and non-terrestrial networks (TN-NTN) in 6 G is essential to support real-time applications like the Metaverse and intelligent edge services, which demand ultra-reliable low-latency communications (xURLLC). Managing these networks and maintaining robust security presents significant challenges due to their complexity and high-dimensional environments. Quantum communication, particularly quantum key distribution (QKD), offers a promising solution by providing unbreakable encryption and enhancing security across TN-NTN architectures. In this paper, we propose a novel deep reinforcement learning approach for QKD-enabled resource allocation in 6 G TN-NTN Metaverse service and transform the joint resource allocation and QKD deployment cost optimization problem into a stochastic game model to ensure secure and efficient resource distribution across TN-NTN environment. We introduce a novel hierarchical multi-agent proximal policy optimization (MAPPO) framework to address the formulated optimization problem. This framework enables dynamic and secure allocation of Metaverse resources and services from multiple providers to users while minimizing QKD deployment costs. Our simulations demonstrate that the proposed framework significantly enhances network performance, reduces key generation costs, and optimizes resource utilization and service quality.

We develop a new method for generating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution $\textup{P}_{Y \mid X}$. Existing methods, such as conformalized quantile regression and probabilistic conformal prediction, usually provide only a marginal coverage guarantee. In contrast, our approach extends these frameworks to achieve approximately conditional coverage, which is crucial for many practical applications. Our prediction sets adapt to the behavior of the predictive distribution, making them effective even under high heteroscedasticity. While exact conditional guarantees are infeasible without assumptions on the underlying data distribution, we derive non-asymptotic bounds that depend on the total variation distance of the conditional distribution and its estimate. Using extensive simulations, we show that our method consistently outperforms existing approaches in terms of conditional coverage, leading to more reliable statistical inference in a variety of applications.

We present a new method for generating confidence sets within the split conformal prediction framework. Our method performs a trainable transformation of any given conformity score to improve conditional coverage while ensuring exact marginal coverage. The transformation is based on an estimate of the conditional quantile of conformity scores. The resulting method is particularly beneficial for constructing adaptive confidence sets in multi-output problems where standard conformal quantile regression approaches have limited applicability. We develop a theoretical bound that captures the influence of the accuracy of the quantile estimate on the approximate conditional validity, unlike classical bounds for conformal prediction methods that only offer marginal coverage. We experimentally show that our method is highly adaptive to the local data structure and outperforms existing methods in terms of conditional coverage, improving the reliability of statistical inference in various applications.

The development of wireless systems based on metaverse to effectively enable novel applications (e.g., surveillance and remote healthcare) gained significant interest from the research community. A metaverse combines many technologies with a virtual representation of the real world system for enabling applications. Although a metaverse enables many applications, there are challenges associated with its deployment. Therefore, in this paper, we consider sensing in metaverse-empowered unmanned aerial vehicle (UAV) networks. A massive number of sensors are considered to effectively sense the physical world states and share them with the metaverse. A problem is formulated to minimize the cost of sensing in terms of energy and latency. Our problem is based on optimizing the resource allocation and association for sensing units. We present a solution based on double deep Q-network (DDQN) for joint association and resource allocation in sensing for metaverse over UAVs network because the defined problem is non-convex and combinatorial. In the end, we validate our idea with comprehensive simulation results.

Dangerous surroundings and difficult-to-reach landscapes introduce significant complications for adequate disaster management and recuperation. These problems can be solved by engaging unmanned aerial vehicles (UAVs) provided with embedded platforms and optical sensors. In this work, we focus on enabling onboard aerial image processing to ensure proper and real-time disaster detection. Such a setting usually causes challenges due to the limited hardware resources of UAVs. However, privacy, connectivity, and latency issues can be avoided. We suggest a UAV-assisted edge framework for disaster detection, leveraging our proposed model optimized for onboard real-time aerial image classification. The optimization of the model is achieved using post-training quantization techniques. To address the limited number of disaster cases in existing benchmark datasets and therefore ensure real-world adoption of our model, we construct a novel dataset, DisasterEye, featuring disaster scenes captured by UAVs and individuals on-site. Experimental results reveal the efficacy of our model, reaching high accuracy with lowered inference latency and memory use on both traditional machines and resource-limited devices. This shows that the scalability and adaptability of our method make it a powerful solution for real-time disaster management on resource-constrained UAV platforms.The code and DisasterEye dataset are available at: https://github.com/Branislava98/TensorRT.

In this paper, we examine the effectiveness of distributed null-steering-based physical layer security (PLS) in over-the-air computation (AirComp), considering the impact of imperfect channel estimation. AirComp has been extensively studied for its ability to efficiently aggregate data in Internet of Things (IoT) networks, but dealing with imperfect channel estimation is a major pragmatic challenge in these networks. This work focuses on how phase errors in imperfect channel estimation affect AirComp’s distributed null-steering-based PLS by evaluating their impact on the mean square error (MSE) performance. A closed-form expression is derived to quantify the degradation in PLS performance of AirComp systems caused by channel estimation errors. The analysis shows that imperfect channel state information (CSI) causes a non-negligible degradation in the MSE performance and also reduces the effectiveness of the PLS technique employed. The degradation of up to 10 times in the average MSE received at the desired location is observed at the loop SNR value of 5 dB. Furthermore, the average MSE gap between the legitimate receiver and the eavesdropper is reduced by more than 0.1 with imperfect channel estimation. The results provide insights into the potential degradation due to imperfect CSI, which helps to quantify the relationship between the CSI estimation error and its impact on the PLS performance of AirComp networks.

Fire poses significant ecological danger and can lead to human losses if not mitigated early. Fire detection systems on remote sensing devices have become critical to disaster prevention and management. In this paper, we propose a lightweight and effective fire detection model based on MobileViT-XS, compressed through the distillation of knowledge from a stronger teacher model. Our model combines the local feature extraction capabilities of convolutional networks with the global context capabilities of vision transforms. This allows our proposed model to achieve high detection accuracy and real-time inference, making it suitable for deployment on UAVs and other resource-constrained devices. Through rigorous experiments on the ADSF dataset, the proposed method achieves an accuracy, F1-score, and recall of 95.50%, along with a precision of 95.52%, thereby outperforming contemporary methods. Extensive evaluations on ADSF confirm that our method not only surpasses existing state-of-the-art techniques in accuracy but also provides significantly faster processing speeds.

Telesurgery is transforming healthcare by enabling surgeons to perform operations remotely through robotic systems connected to high-speed networks. The reliability and safety of these procedures depend on seamless communication, often secured using Proof-of-Stake (PoS) blockchain technology to ensure data integrity and validate transactions. However, malicious nodes within PoS blockchain networks pose significant risks by introducing delays, invalidating legitimate transactions, or colluding to compromise the system. This paper proposes a Deep Learning (DL) based framework to detect malicious nodes in PoS-based blockchain applications, ensuring secure and reliable operations. Using a dataset of node activity, DL models—LSTM, 1D-CNN, and FFNN—were trained with optimizers including Adam, Nadam, and RMSprop. Among these, the LSTM model with RMSprop achieved the highest detection accuracy of 87.37%. The framework enhances security by enabling real-time malicious node detection and communication monitoring, addressing key challenges in blockchain integrity and operational precision, ultimately ensuring the security and reliability of blockchain-integrated telesurgical systems.

As industrial robotic systems have become more integral to modern manufacturing, ensuring their cybersecurity and operational efficiency is a crucial part. This paper suggests a multi-layered framework aimed at improving the cybersecurity and operational efficiency of industrial robots. It consists of three layers: the Robot Data Acquisition Layer, which verifies and pre-processes sensor data for safe transmission; the Cybersecurity and Threat Detection Layer, which utilizes AI to classify data as malicious or non-malicious and identify specific types of attacks for targeted countermeasures; and the Data Translation and Execution Layer, which transforms safe data into actionable commands for smooth robot operation. Three models were tested for prediction tasks namely Long Short Term Memory (LSTM), Gated Recurrent Units (GRU) and 1-Dimensional Convolutional Neural Networks (1-D CNN). Using metrics like precision, recall, accuracy, and F1-score the proposed framework performance was measured with an overall accuracy of 97.87% from the experiments. This not only showed an excellent mitigation property against cyberattacks but also robustly improved the data integrity which therefore solved the security threats in industrial automation.

The primary obstacle in full-duplex (FD) communications lies in self-interference cancellation (SIC). Traditional FD systems often retain residual self-interference (SI) even after employing SIC techniques such as antenna isolation, spatial beam-forming, or the SI reconstruction and subtraction. In contrast, this work introduces an innovative FD transceiver design for SIC, grounded in the mathematical modeling of pseudo-Doppler (PD) shift systems. Our PD-aided transceiver design emerges as a highly effective SIC solution, capable of entirely eliminating SI from the center frequency of the transceiver’s desired signal. By employing a low-pass filter, the desired signal can be extracted without any residual SI contamination. Comparative analyses of SIC performance demonstrate that our transceiver design surpasses traditional FD systems, even when the latter achieve exceptionally high SI suppression levels.

Mobile Edge Computing (MEC) is crucial for enabling computation-intensive applications in Internet of Things (IoT) networks where low-latency and energy-efficient processing are essential. The integration of multiple Reconfigurable Intelligent Surfaces (RIS) enhances the communication efficiency between IoT devices and MEC servers by dynamically adjusting signal propagation. This paper addresses an energy minimization problem in a multi-RIS-assisted MEC system for IoT network, aiming to reduce the total energy consumption while meeting latency and resource constraints. Our proposed framework innovatively combines multi-RIS path selection with edge computing task offloading, and incorporates a dynamic RIS activation strategy for multi-user scenarios. To tackle this mixed-integer non-linear programming problem, we develop an effective decomposition algorithm based on Block Coordinate Descent (BCD). The problem is iteratively solved through three subproblems: RIS phase shift optimization, path selection and RIS activation, offloading ratio and power allocation. Simulation results show that our approach achieves up to 26% energy saving compared to the single RIS scheme, demonstrating the significant potential of multi-RIS integration in energy-efficient IoT-MEC systems.

Dynamic resource allocation is crucial for sustaining optimal network performance in Internet of Things (IoT) environments. The frequent arrival and departure of devices result in dynamic topology changes, which pose significant challenges to effective resource allocation. To address these limitations, this study introduces a method that leverages hypergraph modeling to explicitly characterize multi-node resource collision relationships and proposes a graph reinforcement learning with hypergraph convolutions for dynamic resource allocation. Experimental evaluations indicate that the proposed method outperforms compared approaches in channel allocation efficiency and resource utilization.

Generative AI (GAI) and Large Language Models (LLMs) have revolutionized natural language processing and content creation. However, their significant computational demands during inference often require cloud servers, which are currently the only viable option for handling complex multi-modal models like GPT-4. The inherent complexity of these models increases latency, posing challenges even within cloud environments. Furthermore, cloud reliance brings other challenges, including high bandwidth consumption to transfer diverse data types. Worse, in personalized GAI applications like virtual assistants, similar prompts frequently occur, causing redundant transmission and computation of replies, which further increases overhead. Accelerating the inference of multi-modal systems is, therefore, critical in artificial intelligence. In this paper, we aim to improve the inference efficiency through prompt caching; if a current prompt is semantically similar to a previous one, the system can reuse the earlier response without invoking the model again. We leverage collaborative edge computing to cache popular replies and store their request embeddings. New prompts are locally processed to extract embeddings, with their qualities determined by the resources available on edge servers. Our problem is formulated as an optimization to manage offloading decisions for GAI tasks, aiming to avoid cloud inferences and minimize latency while maximizing reply quality. Given its non-convex nature, we propose to solve it via Block Successive Upper Bound Minimization (BSUM). Reinforcement learning is employed to actively pre-cache prompts, tackling the complexity of unknown prompt popularity. Our approach demonstrates near-optimal performance, significantly outperforming cloud-only solutions.

Abstract With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

Federated learning (FL) gained importance in sensitive Internet of Things (IoT) environments by creating a privacy-preserving ecosystem where participants share machine-learning models instead of raw data. However, FL shifts data control away from the server, exposing it to non-independent and identically distributed (non-IID) problems caused by biased clients (IoT devices). This hinders the learning process by increasing execution time and cost. Current solutions alter the FL structure or compromise privacy by offloading clients’ raw data to an external server. To mitigate these limitations, this article proposes a solution to the non-IID problem by introducing an initialization phase, orchestrated by the server, that constructs high-quality initial models. These models can boost FL accuracy and convergence, regardless of whether IoT participants exhibit non-IID properties. Our proposed initialization scheme involves clients training over the same model sequentially, lessening the impact of aggregation, a primary cause of model degradation in federated approaches. Furthermore, a regulator algorithm deployed on the server maintains model integrity and mitigates catastrophic forgetting, enhanced by a client selection process that emphasizes the compatibility of IoT clients to cooperate effectively. Moreover, we devise an optimization scheme based on clustering and genetic algorithms to reduce the selection time while ensuring optimal performance in IoT networks. Experiments on MNIST, KDD, and CIFAR10 data sets show promising results in terms of initial model resiliency against catastrophic forgetting and non-IID settings. Additionally, our findings suggest that our approach can significantly enhance FL training in IoT applications by achieving 40% higher initialization accuracy and a 20% average improvement in end results compared to conventional methods, all while reducing computation time by 80% compared to similar approaches.

The widespread deployment of Internet of Things (IoT) devices has intensified the demand for scalable and secure trust management IoT systems. Existing GNN-based approaches often neglect real-time adaptability and contextual trust in dynamic, heterogeneous networks. This study introduces TrustAware-GNN, a trust-aware graph neural network framework designed to robustly evaluate device trustworthiness in IoT environments. The model integrates a multi-dimensional trust mechanism encompassing direct, indirect, temporal, and contextual trust, computed using localized device parameters: reliability, capability, security posture, reputation, and location awareness. Trust values modulate edge weights within the graph, enabling trust-adaptive message propagation. A trust-threshold-based edge formation mechanism filters unreliable links, while attention-based aggregation refines node embeddings. The model continuously adapts to behavioral shifts via time-decayed trust updates and contextual similarity matching. Simulation was conducted across IoT-23, EDGE-IIoTSET, AutoTrust, and ToN-IoT datasets. TrustAware-GNN achieved 94.83% accuracy on EDGE-IIoTSET and 93.25% on IoT-23, outperforming MGNN, STAR-GCN, and SEGC-PP in both accuracy and adaptability under dynamic trust scenarios.

In this paper, we introduce a novel framework designed specifically for federated reinforcement learning in IoT-driven networks, focusing on flow management in autonomous systems. Our framework optimizes flow table matching by monitoring IoT network traffic and ensuring efficient flow management across connected devices. By considering the specific flow requirements of IoT traffic, our framework enables an intelligent agent to make informed decisions regarding flow table entries, thereby improving the performance and management of autonomous systems. To gather essential data for decision-making, our framework utilizes an IoT-based SDN module that collects traffic statistics and relevant information from the network’s data plane. By leveraging SDN, our system enhances the learning and decision-making capabilities of IoT devices within autonomous systems. We introduce an optimization model called Software-Defined Network Assisted Federated Reinforcement Learning (SORE), based on the Markov decision process. SORE capitalizes on the advantages of SDN to boost the overall performance of IoT-based autonomous systems. By applying reinforcement learning techniques, our framework demonstrates significant improvements over existing models. Extensive simulations validate the effectiveness of our proposed system, showcasing superior performance and efficiency in IoT-enabled wireless networks within autonomous systems. The results underscore the potential of our approach in real-world IoT deployments.

In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation. Expanding client access and diversifying data enhance models by incorporating diverse perspectives, thereby improving adaptability. However, in dynamic and mobile environments, the availability of FL clients fluctuates as devices may become inaccessible, leading to inefficient client selection and reduced model performance. Current solutions often fail to adapt quickly to these changes, creating a gap in achieving real-time client availability and efficient data utilization. To address this, we propose a Deep Reinforcement Learning (DRL) On-Demand solution, deploying new clients using Docker Containers on-the-fly. Our On-Demand solution, employing DRL, targets client availability and selection while considering data shifts and container deployment complexities. It employs an autonomous end-to-end approach for handling model deployment and client selection. The DRL strategy leverages a Markov Decision Process (MDP) framework, with a Master Learner and a Joiner Learner to optimize decision-making. The designed cost functions account for the complexity of dynamic client deployment and selection, ensuring effective resource management and service reliability. Simulated tests show that our architecture can easily adapt to changes in the environment and respond to On-Demand requests while reducing the number of learning rounds used by 20-50 % compared with existing approaches. This highlights its ability to improve client availability, capability, accuracy, and learning efficiency, surpassing heuristic and traditional reinforcement learning methods.

In the rapidly evolving domain of the Internet of Vehicles (IoV), ensuring robust trust management, privacy, and security presents significant challenges. This article proposes a novel approach integrating generative AI (GAI) and federated learning (FL) to address these challenges. FL allows distributed learning across vehicles without the need to share data, enhancing privacy compared to centralized methods. Our approach enhances trust management by raising the level of accuracy in detecting anomalies and preserving data privacy. As a result, the effectiveness of the proposed approach in practical real-world urban settings is illustrated by comprehensive evaluations using the CityPulse dataset. The results show a 20% improvement in trust scores under normal conditions, a 92% anomaly detection accuracy, and acceptable latency despite the added security measures. Additionally, 3-D visualizations illustrate the system’s robustness and scalability. This solution aligns with the objectives of 6G wireless communications, laying the groundwork for future intelligent, ultrareliable, and secure vehicular networks. Future research will focus on expanding the application of GAI and FL for real-time decision-making in large-scale IoV networks and optimizing cryptographic protocols.

Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approach to enhancing data security in IoT through the integration of homomorphic encryption and blockchain technology. We conduct simulations using the Kaggle Smart Home Dataset to evaluate the effectiveness of our proposed methodology on smart home devices and wearable technology. Our approach not only secures data transmission but also guarantees data integrity and privacy through decentralized verification and secure aggregation techniques. Specifically, our evaluation demonstrates an encrypted data transmission rate exceeding 99.5%, a complete absence of unauthorized access instances in the simulated environment, and a verified data integrity rate of over 99.8%. Additionally, our method supports real-time processing and scalability, making it suitable for various IoT applications, including smart contract applications in IoT for privacy and security in supply chain transactions. The study highlights the robustness of combining homomorphic encryption and blockchain to protect sensitive data throughout its lifecycle.

This article aims at analyzing and comparing an adaptive algorithm-based method for improving the performance of Internet of Things (IoT) systems through simulation studies. Concentrating on active and complex scenarios, the study presents new proposals for secure and smart learning of routes, activity forecasting for nodes, link stability estimation, and flexible resource management. These methods are benchmarked against conventional algorithms to evaluate the effectiveness of the proposed solution based on routing efficiency, traffic prediction, link, resource consumption, network response time, and energy requirements. The findings are encouraging, the adaptive algorithms do improve dramatically on the standard ones making the system slower and consuming much less power. From the findings of the study it can be concluded that using adaptive algorithms in IoT can have a high impact in terms of improvement in efficiency as well as sustainability. We conclude this work by providing some directions for further research and development in the IoT field.

As the Internet of Vehicles (IoV) continues to evolve, the imperative for advanced algorithms capable of managing increased network demands, ensuring data security, and boosting overall system efficiency becomes crucial. This article introduces a novel suite of algorithms designed to enhance IoV system performance across multiple metrics. Our comprehensive simulations contrast the proposed system with three contemporary approaches the two-layer computing resource management (TCRM) model, the federated edge learning (FEL) approach, and the blockchain-based trust-value management (BTVM) approach. We demonstrate significant improvements: a latency reduction to as low as 90 ms, compared to 118 ms in TCRM, 125 ms in FEL, and 120 ms in BTVM; reliability in packet delivery with an enhancement from an initial 98% to 99.9%, compared to 98.5% in TCRM, 97.8% in FEL, and 99.5% in BTVM; resource utilization efficiency that surpasses baseline models by maintaining rates up to 85%, compared to their 60–65% in TCRM and FEL, and 75% in BTVM; and swift network response times peaking at just 50 ms, against 60 ms in TCRM, 65 ms in FEL, and 50 ms in BTVM. Additionally, our algorithms maintain robust data security levels, consistently achieving 100% effectiveness, compared to 99.2% in TCRM, 98.9% in FEL, and 99.5% in BTVM. These results underscore the proposed system’s potential to significantly outperform existing solutions, paving the way for more resilient and efficient IoV architectures. The integration of these algorithms into real-world IoV applications can substantially contribute to the advancement of intelligent transportation systems.

This study presents a federated learning (FL) framework tailored for uncrewed-aerial-vehicle (UAV)-enabled Internet of Things (IoT) networks, addressing challenges in efficiency, robustness, and scalability. The proposed system improves model learning with a 14.9 percentage point increase in accuracy (75.5%–90.4%) and a 69.2% reduction in loss over ten training epochs. It demonstrates resilience, limiting accuracy reduction to 7% under simulated attacks, and scalability with a linear increase in processing times as network size grows. High anomaly detection rates (92%) further enhance network security and reliability. These results validate the framework’s effectiveness in UAV networks and highlight its broader potential for IoT applications. Future work will explore further enhancements and diverse applications.

Recently, the research community has recently shown overwhelming interest in metaverse-enabled wireless devices, due to their compelling proactive learning and self-sustainability attributes. Proactive learning enables machine learning models to be trained before user requests, while self-sustainability allows a system to function with the least amount of assistance from network administrators/users. Because of these features, one can use metaverse to enable various applications (e.g., entertainment and collision avoidance) in intelligent transportation systems. However, the limitations of computing processing power (e.g., in autonomous cars) and communication resources make implementing metaverse-empowered vehicular networks challenging. Motivated by these facts, we present a new framework for cooperative sensing, communication, learning, and task offloading for vehicular networks enabled by the metaverse. Subsequently, we formulate a cost-function minimization problem that accounts for transmission energy and transmission latency. The cost is minimized by optimizing task offloading, wireless resource distribution, transmit power allocation, and sensing interval. We employ a decomposition-based strategy for simultaneous resource allocation, task offloading, sensing interval optimization, and transmit power allocation. Due to the combinatorial nature of the resource allocation and task offloading problems, matching-based solutions are used. For sensing interval optimization, convex optimization is used. On the other hand, due to the non-convex and continuous nature of the transmit power allocation problem, a proximal term is introduced into the objective function to approximate it as convex objective function, which is then solved using a convex optimizer. To gain further insights, the proposed scheme is supported by extensive numerical results.

Drones or UAVs (Unmanned Aerial Vehicles) are increasingly utilized with IoT (Internet of Things) to enhance security surveillance and environmental monitoring within smart cities. Although path planning algorithms, deployment strategies, and charging station optimization techniques have been explored to improve operational efficiency, the physical limitations of battery capacity remain a significant challenge. This study introduces the concept of a "resting drone," a specialized drone capable of attaching to structures or walls through structural modifications. By doing so, it can perform environmental monitoring and surveillance tasks without operating its motors, thus conserving energy. Furthermore, we conducted simulations based on real hardware data of resting drones to analyze their operations, including resting, movement, target area monitoring, and resource charging, within a smart city environment. A new movement algorithm was proposed to enhance the resource efficiency of the resting drones. The study compares the resource efficiency of resting drones with that of conventional drones. The results show that environmental monitoring coverage increases significantly, from 60% to 315%, depending on the movement algorithm used.

The advent of unmanned aerial vehicle (UAV) networks, renowned for their expansive coverage capabilities and heightened adaptability, presents a promising landscape for bolstering the efficacy of Internet of Things (IoT) data transmissions. Nevertheless, the integration of UAVs into IoT ecosystems introduces a spectrum of security challenges, notably data tampering, man-in-the-middle (MitM) attacks, and eavesdropping, which threaten the integrity and confidentiality of transmitted information. Since covert transmission has the characteristics of strong concealment and difficult detection, UAV networks based on information hiding become a new paradigm for solving these security problems. This article proposes an information hiding algorithm based on super-resolution enhancement and significant region detection (IH-SESD). This algorithm incorporates the super-resolution enhanced SRCNN and the U2Net salient region detection technologies. Comprehensive performance analysis and comparisons with existing methods demonstrate the superiority of the proposed IH-SESD algorithm.

To address the multi-UAV radar cooperative guided search task in the scenarios of large airspace with widespread distribution of cluster targets, an autonomous hierarchical decision-making framework based on multi-agent reinforcement learning is proposed to guide different airborne radar search processes in cooperative search tasks. Firstly, the top-level and bottom-level strategy modules are constructed based on the cooperative airspace set-covering model and search performance optimization models established in the above scenario respectively. Secondly, a cooperative search environment based on real-time beam scheduling is constructed where a complementary scheduling strategy for cooperative radar search beam positions is proposed to minimize cooperative search time and maximize global radar search performance. Finally, the lamaddpg-rgs algorithm is proposed to perform long-short term memory (LSTM) and feature extraction on the variable global observation state sequence, aiming to improve the algorithm training effect and convergence speed. Simulation results show that the trained hierarchical decision-making multi-agents can make precise autonomous decisions rapidly based on local observation states and current search task process. The optimization effect of radar cooperative search performance in above scenario based on proposed algorithm is superior to traditional algorithms.

Recently, fixed-wing unmanned aerial vehicles (UAVs) are able to extend the communications mission time and ease of deployment due to their powerful onboard capabilities and flexibility, and reflective intelligent surfaces (RISs) are capable of reflecting links to avoid obstacles and thus improve channel gain. Therefore, RIS-assisted fixed-wing UAVs are widely used in wireless communications. Nevertheless, fixed-wing UAVs have limited energy, so improving energy efficiency is critical. This article focuses on energy efficiency optimization problems under RIS-assisted fixed-wing UAV communications. In communications coverage systems, the flight trajectory of fixed-wing UAVs and service scheduling to ground nodes (GNs) significantly impact energy efficiency. Existing work often adopts circular trajectory and traditional deep reinforcement learning (DRL) algorithms for optimizing trajectory and service scheduling. However, circular trajectory can not adapted to the GN distribution well. In addition, the traditional DRL algorithm has two drawbacks: 1) the efficiency of exploring the empirical process is low and 2) the accuracy of handling the hybrid action space needs to be higher. Thus, we propose the midpoint iteration convex hull (MICH) algorithm based on the Graham scan to design trajectories that can be adapted to the distribution of the GNs. In addition, we propose the action screening virtual and real experience (AS-VRE) mechanism and the N-steps hybrid deep Q and policy network (NsHQPN) algorithm to address the low-exploration efficiency and the low-fetch accuracy in handling the hybrid action space. Experiments show that our proposed MICH algorithm, AS-VRE mechanism, and NsHQPN algorithm can effectively improve the system energy efficiency and outperform other baseline schemes.

Reconfigurable-intelligent-surface (RIS)-assisted autonomous aerial vehicles (AAVs) communications technology improves energy efficiency by reflecting signals. This article utilizes RIS and deep reinforcement learning (DRL) to optimize the scheduling of ground terminals (GTs), AAV trajectories, resource allocation, and time slot lengths to maximize system energy efficiency. Three flaws of the existing DRL algorithm are also addressed to seek higher energy efficiency further. First, DRL faces exploration challenges due to the complexity of the solution space, resulting in low rewards. We propose the ant colony DRL (ACDRL) algorithm, which optimizes the scheduling order of the GTs using the ant colony optimization (ACO) algorithm and feeds the results back to the DRL to optimize the subsequent decision making, thus reducing the exploration overhead. Second, to reduce the degree of local optimization when dealing with hybrid action space planning, we propose a hybrid discrete-continuous DRL (HDCDRL) algorithm to improve action accuracy. Finally, to better generalize the model to similar tasks, we propose the transfer-DRL (T-DRL) model to reduce the training time when the task changes. Experimental results show that our proposed solution outperforms the benchmark solution.

In the Internet of Things (IoT), blockchain-based cross-domain authentication schemes can effectively establish trust and share data across different administrative domains. However, current blockchain-based cross-domain authentication solutions often overlook dynamic node participation challenges, which is crucial for the flexible IoT environment. In this study, we propose a blockchain-based cross-domain authentication scheme that supports dynamic node participation, allowing administrative domainss to freely join or leave under legal conditions. First, we introduce an efficient blockchain-based cross-domain authentication framework. Second, we propose a blockchain consensus algorithm that supports dynamic node participation to serve the aforementioned framework. Specifically, inspired by the transmission control protocol (TCP) protocol’s piggybacking strategy, this algorithm integrates the process of node joining and leaving into the regular consensus flow to enhance efficiency. To further improve the algorithm’s efficiency, we designed compressed block and parallel chain structures to increase bandwidth utilization and throughput. Detailed correctness proofs demonstrate the algorithm’s security. Extensive experiments have been conducted to show that our scheme increases throughput by approximately 8x compared to existing approaches.

Efficient monitoring of carbon capture and storage (CCS) systems heavily relies on sensor data. However, sensors are susceptible to potential multiple faults, leading to performance degradation and posing a risk of catastrophic failures. Timely detection of sensor faults in CCS systems is crucial for safe and efficient carbon dioxide (CO2) pipeline operation. This paper addresses the challenge of diagnosing multiple sensor faults in CO2 pipelines by introducing a novel approach based on partial-distributed particle filter (PDPF). The novel distributed-filtering framework aims to reduce the computational complexity while identifying multiple faults in highly nonlinear systems. The proposed PDPF architecture comprises a collection of linear local filters and a nonlinear main filter. More specifically, the algorithm segregates nonlinear computations from local filters and assigns them to the main filter. The main filter handles the time updates involving all nonlinear computations associated with the nonlinear system, while the parallel linear local filters, each equipped with a distinct subset of sensor measurements, perform the measurement updates and combine their estimates via information fusion. As for fault detection and isolation, each local filter utilizes a novel kernel density estimation (KDE)-based approach that analyzes the consistency between model predictions and observed behavior, enabling the identification of sensor faults. Compared to existing methods, this approach reduces the computational requirements and is well-suited for highly nonlinear systems experiencing multiple sensor faults. Additionally, performance assessment via numerical simulations confirms its effectiveness and superiority in comparison to stateof-the-art alternative methods.

Edge computing is near end users and provides them with fast computing services. Compared to the cloud, edge provides services with low communication delays but can only service limited users due to constrained storage and computing resources. Thus, allocating more computing resources to some jobs will block the execution of others, and the edge sends them either to the cloud or back to the users. This article focuses on minimizing the average system time by exploring blocked job scheduling (BJS) and redundant computing resource allocation (RCRA) in the cloud–edge–user system. Since edge nodes often operate in highly unpredictable environments, we adopt the replication redundancy to use the resources to shorten the job execution time. First, we propose an approximate model to evaluate the average system time theoretically. Second, we analyze the optimal scheduling for the blocked jobs and the optimal resource allocation for the redundant computing resources. Finally, we propose an algorithm combining BJS and RCRA. Simulation results show that the proposed model approximates the cloud–edge–user system well, and the combined algorithm significantly outperforms the other algorithms under different service time distributions.

As a cutting-edge technology of low-altitude Artificial Intelligence of Things (AIoT), autonomous aerial vehicle object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multiband infrared small object detection approach for low-altitude intelligent surveillance services. First, we propose the multiband infrared image fusion algorithm based on cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based multiscale dense small object detection (MsDSOD) algorithm is proposed. The algorithm consists of the global-local object detection (G-LOD) network, the object dense area extraction (O-DAE) module, and the weighted boxes fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean average precision (mAP) of MsDSOD is improved by 0.80% and the average precision in small object detection$({\mathrm { AP}}_{s})$is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion.

The rapid expansion of Internet of Things (IoT) devices and applications necessitates the need for more efficient computational and data management strategies. The Fog-enabled UAV-as-a-Service (FU-Serve) platform addresses these demands by integrating fog computing to enhance the operational efficiency of UAVs in IoT environments. Despite its advantages, the FU-Serve platform faces significant challenges, including data transmission latency, resource allocation, and energy management, contributing to the underutilization of UAVs and fog nodes. To address these challenges, this paper introduces a consensus-driven approach, Con-Fog, that optimizes the selection of fog nodes for UAVs within the FU-Serve platform. Con-Fog evaluates potential fog nodes within the communication range by computing utility values based on geographical distance, link quality, available computational resources, and residual energy. UAVs rank these nodes according to their utility values and select the most suitable ones through a consensus-based approach, ensuring alignment with the operational demands of IoT devices. Additionally, we apply an optimal best-fit algorithm to refine fog node allocation, maximizing resource utilization while keeping it below each node’s capacity threshold (T%). Our simulation results show that Con-Fog significantly enhances key IoT performance metrics. Transmission time and the number of unassigned UAVs decrease by 10%-30% and 20%-40%, respectively, while residual energy increases by 30%-50% compared to existing systems. These improvements enhance the management of UAV and fog node resources, thereby advancing the effectiveness of IoT applications within the FU-Serve platform.

The Internet of Things (IoT) is a constantly expanding system connecting countless devices for seamless data collection and exchange. This has transformed decision-making with data-driven insights across different domains. However, challenges arise concerning security and computational limitations. To strengthen IoT against cyber threats and optimize resource usage, combining quantum computing (QC) with machine learning (ML) is a promising approach. ML enables computers to learn from data and detect patterns without explicit programming. By leveraging ML algorithms, vast datasets from IoT devices can be analyzed, identifying anomalies and forecasting potential security breaches. Yet, conventional ML algorithms may need help with the complexity and scale of IoT data. QC, based on quantum mechanics, offers unparalleled computational speed and scale. Quantum ML algorithms can quickly analyze IoT datasets, identifying patterns and potential threats. This study examines the ideas behind ML, QC, and their potential collaboration within IoT networks. The research focuses on the possibility of improving the security of IoT networks by integrating QC approaches with ML. It also addresses the challenges and limitations of integrating ML and QC in the context of IoT networks. These obstacles include hardware constraints, algorithm complexities, and the need for specialized knowledge.

Vision Large Language Models (VLMs) combine visual understanding with natural language processing, enabling tasks like image captioning, visual question answering, and video analysis. While VLMs show impressive capabilities across domains such as autonomous vehicles, smart surveillance, and healthcare, their deployment on resource-constrained edge devices remains challenging due to processing power, memory, and energy limitations. This survey explores recent advancements in optimizing VLMs for edge environments, focusing on model compression techniques, including pruning, quantization, knowledge distillation, and specialized hardware solutions that enhance efficiency. We provide a detailed discussion of efficient training and fine-tuning methods, edge deployment challenges, and privacy considerations. Additionally, we discuss the diverse applications of lightweight VLMs across healthcare, environmental monitoring, and autonomous systems, illustrating their growing impact. By highlighting key design strategies, current challenges, and offering recommendations for future directions, this survey aims to inspire further research into the practical deployment of VLMs, ultimately making advanced AI accessible in resource-limited settings.

Physical activity energy expenditure (PAEE) offers significant benefits for general healthcare monitoring and has the potential to promote healthy and active aging for elderly individuals. With recent advancements in quantum information and computation, quantum machine learning (QML) has emerged as a tool capable of improving upon the measurement of PAEE. In this paper, we propose a hybrid QML model to predict PAEE which consists of a classical long short-term memory (LSTM) model integrated with a variational quantum circuit (VQC). This model, which we refer to as the enhanced quantum long short-term memory linear (eQLSTML), was subsequently trained and tested using the publicly available GOTOV Human Physical Activity and Energy Expenditure Dataset for Older Individuals. In particular, we study the proposed eQLSTML model with different gate choices in the quantum circuit along with various embedding and layering techniques. Our results indicate our model to be superior in both performance comparisons and prediction when compared to traditional machine learning methods currently employed. Our findings indicate that combining QML approaches with wearable IoT healthcare devices provides a new avenue for personalized healthcare monitoring and an effective method for promoting healthy aging.

Federated learning (FL) is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban environments presents a significant challenge in selecting trustworthy clients for collaborative model training. The usage of traditional approaches, such as the random client selection technique, poses several threats to the system’s integrity due to the possibility of malicious client selection. Primarily, the existing literature focuses on assessing the trustworthiness of clients, neglecting the crucial aspect of trust in federated servers. To bridge this gap, in this work, we propose a novel framework that addresses the mutual trustworthiness in FL by considering the trust needs of both the client and the server. Our approach entails: 1) creating preference functions for servers and clients, allowing them to rank each other based on trust scores; 2) establishing a reputation-based recommendation system leveraging multiple clients to assess newly connected servers; 3) assigning credibility scores to recommending devices for better server trustworthiness measurement; 4) developing a trust assessment mechanism for smart devices using a statistical interquartile range (IQR) method; and 5) designing intelligent matching algorithms considering the preferences of both parties. Based on simulation and experimental results, our approach outperforms baseline methods by increasing trust levels, global model accuracy, and reducing nontrustworthy clients in the system.

Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently. Due to security concerns, many institutions avoid accessing state-of-the-art commercial LLM services, requiring the deployment and utilization of open-source LLMs in a local network setting. However, open-source LLMs usually have more limitations regarding their performance, such as their arithmetic calculation and reasoning capacities, and practical systems of applying LLMs to IoT have yet to be well-explored. Therefore, we propose an LLM-based Generative IoT (GIoT) system deployed in the local network setting in this study. To alleviate the limitations of LLMs and provide service with competitive performance, we apply prompt engineering methods to enhance the capacities of the open-source LLMs, design a Prompt Management Module and a Postprocessing Module to manage the tailored prompts for different tasks and process the results generated by the LLMs. To demonstrate the effectiveness of the proposed system, we discuss a challenging table question answering (Table-QA) task as a case study of the proposed system, as tabular data is usually more challenging than plaintext because of their complex structures, heterogeneous data types and sometimes huge sizes. We conduct comprehensive experiments on the two popular Table-QA data sets, and the results show that our proposal can achieve competitive performance compared with state-of-the-art LLMs, demonstrating that the proposed LLM-based GIoT system can provide competitive performance with tailored prompting methods and is easily extensible to new tasks without training.

The widespread adoption of computation- and communication-intensive applications, such as object detection, VR/AR, and telemedicine, has significantly alleviated transmission pressure on backbone networks and improved user experience. However, efficiently managing and computing these tasks on user sides remains a significant challenge, particularly under resource-constrained conditions. To address this problem, we propose a new service caching decision method based on deep dueling double Q-network (D3QN) by employing a learnable policy to handle the unknown task requests and determine the optimal caching strategies. Additionally, the limited storage capacity of edge servers (ES) is mitigated by forwarding the resource-intensive or infrequently requested tasks to the cloud data centers (CDC). The channel selection problem is modeled as a multiuser game and a distributed method is developed to achieve the Nash Equilibrium (NE). Simulation results demonstrate that the proposed method outperforms the existing benchmarks, showcasing its effectiveness in managing complex, dynamic environments.

In this article, we tackle the network delays in the Internet of Things (IoT) for an enhanced Quality of Service (QoS) through a stable and optimized federated fog computing infrastructure. Network delays contribute to a decline in QoS for IoT applications and may even disrupt time-critical functions. This article addresses the challenge of establishing fog federations, which are designed to enhance QoS. However, instabilities within these federations can lead to the withdrawal of providers, thereby diminishing federation profitability and expected QoS. Additionally, the techniques used to form federations could potentially pose data leakage risks to end-users whose data is involved in the process. In response, we propose a stable and comprehensive federated fog architecture that considers federated network profiling of the environment to enhance the QoS for IoT applications. This article introduces a decentralized evolutionary game theoretic algorithm built on the top of a genetic algorithm mechanism that addresses the fog federation formation issue. Furthermore, we present a decentralized federated learning algorithm that predicts the QoS between fog servers without the need to expose users’ location to external entities. Such a predictor module enhances the decision-making process when allocating resources during the federation formation phases without exposing the data privacy of the users/servers. Notably, our approach demonstrates superior stability and improved QoS when compared to other benchmark approaches.

The convergence of the Internet of Things (IoT) and Software-Defined Networking (SDN) has paved the way for a new technological paradigm in Healthcare Industry 5.0. This integration addresses the complexity, heterogeneity, and dynamic nature of smart IoT devices within healthcare systems. However, it also increases the risk of cyberattacks, particularly Distributed Denial of Service (DDoS) attacks, which pose significant threats to such critical infrastructure. While Deep Learning (DL)-based intrusion detection methods have demonstrated high accuracy in detecting these attacks, their opaque decision-making process often leads to their characterization as black-box models, limiting their practical use for security analysts. To overcome these challenges, this study proposes an explainable hybrid model for DDoS attack detection in SDN-IoT-based Healthcare Industry 5.0 environments. The model combines the strengths of Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies in network traffic. Implemented using an SDN controller, the model accurately classifies DDoS and IoT attacks while providing transparency through the SHapley Additive exPlanation (SHAP) method, which identifies the most influential features in the model’s decision-making process. Simulation results on the CICDDoS2019 and IoT Healthcare Security datasets demonstrate the model’s effectiveness, achieving detection accuracy of 99.59% and 98.12%, respectively. These findings confirm the robustness of the proposed hybrid model compared to state-of-the-art methods for detecting potential attacks in Healthcare Industry 5.0 systems.

Vehicular social networks (VSNs) play a crucial role in intelligent transportation systems, offering high-quality data management services that enhance various aspects of daily life. Due to their convenience, VSN systems, equipped with advanced data search and sharing capabilities, are increasingly integrated into modern vehicles. While earlier VSNs focused on securing data communication between users, the transmission of sensitive vehicle and traffic data, like road conditions and vehicle trajectories, has raised privacy concerns and the risk of data leakage, which could harm vehicle owners’ interests. Historically, these systems focused primarily on securing data communication between VSN users. However, the transmission of sensitive vehicle and traffic data, such as road conditions and vehicle trajectory information, has raised concerns about data privacy and the potential risks of data leakage, which could compromise the interests of vehicle owners. To address these challenges, we propose a novel group-grained data search and sharing scheme for VSN systems. Unlike traditional attribute-based encryption methods used in data management, our approach introduces a group-grained model that enables fine-grained control over search rights and data-sharing isolation, ensuring enhanced data privacy. Additionally, to reduce the computational burden on these Internet of Thing (IoT) devices, our scheme ensures constant-sized keyword index generation, data index generation, trapdoor creation, and decryption processes. We evaluate the efficiency of our construction and compare it with similar constructions. The results demonstrate that our construction is well suited for resource-constrained IoT devices in VSN systems.

Traditional traffic anomaly-based attack detection methods in Zero-trust Networks (ZTN) suffer from inherent security vulnerabilities, as they neglect considerations regarding their security defenses. Compromising the attack detection model itself can result in the breakdown of normal attack detection capabilities. Ensuring the security of the attack detection model during runtime presents a novel challenge. To address these shortcomings, we propose a novel attack detection model, termed Security within Security: Attack Detection Model with Defenses Against Attacks Capability for Zero-Trust Networks (SWS), aimed at enhancing the security of ZTN. SWS focuses on achieving attack detection in non-secure detection environments, to maintain its detection capability even when under attack. By employing a soft thresholding method, SWS adapts to the dynamic changes in network traffic, thus reducing the interference of attack signals. The incorporation of an attention mechanism enables SWS to concentrate on analyzing the most indicative traffic features of attack behavior. Additionally, we integrate Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (BiLSTM) to enhance the robustness of identifying complex network attack behaviors. The effectiveness of the SWS is validated through ablation studies, model comparisons, experiments conducted over different training epochs, and experiments conducted on various components of the dataset. Experimental results demonstrate that compared to existing attack detection models, SWS achieves improvements in detection accuracy and recall rate by 13.4% and 10.6%, respectively, while reducing the False Positive Rate (FPR) by 16.9%.

Autonomous aerial vehicles (AAVs) have found widespread utility in the field of multi-target tracking (MTT) due to their inherent advantages, such as ease of deployment, flexible maneuverability, and cooperative communication capabilities. AAVs can perform tasks ranging from regional surveillance to tracking and search-and-rescue operations in hazardous environments. Nonetheless, the issue of how to efficiently coordinate multiple AAVs to track diverse mobile targets remains a critical concern. This paper focuses on MTT with multi-AAV, aiming at optimizing system performance in scenarios where network availability is limited. In contrast to treating sensor perception as a monolithic process, we propose a scheme for cooperative sensing and data processing. This scheme is designed to reduce system response latency in environmental information sensing and multidimensional data processing for multiple AAVs. Furthermore, in contrast to assuming linear target movement and single-step target position prediction, we introduce a multi-agent deep reinforcement learning (MADRL) framework combined with multi-step prediction extended Kalman Filter (MP-EKF). This framework is tailored to enhance tracking precision, especially when targets’ trajectories are curved, which can reduce AAV flight displacement if the target’s position can be predicted multiple steps later. In addition, unlike using latency as a real-time application to measure the “freshness” of information, the Age of Information (AoI) is introduced for considering the waiting time of transmission and calculation between multiple AAVs. This assessment method is utilized to comprehensively evaluate and mitigate data latency within the MADRL algorithm. Finally, extensive simulation experiments demonstrate that the proposed scheme significantly outperforms both baseline methods and state-of-the-art approaches in terms of AoI, system latency, and energy consumption.

Existing deep learning-based circuit design methods mostly focused on the primary matching of the model itself or circuit data, lacking generalizability and ignoring deep representation of electromagnetic coupling effects in coupled circuits. Therefore, it exhibits difficulties to further improve the accuracy in circuit performance prediction and requires large training datasets. To address these challenges, this article proposes an intelligent circuit design methodology with graph transfer learning (CircuitGTL). Specifically, it achieves the weighted graph modeling of complex electromagnetic environment circuits, where nodes represent components, edges represent the electromagnetic coupling effect between components, and edge weights signify the differential strength of electromagnetic coupling. Hereby, a fused graph representation model, integrating graph isomorphic network and electromagnetic coupling effect-based graph attention network, is proposed to achieve deep representation learning of graphic circuit data. Then, a model- and data-driven graph transfer learning mechanism considering joint optimizing of circuit’s performance matrix and nonperformance indicators is proposed. This is to achieve lightweight cross-electromagnetic topologies parameters optimization. Taking Terahertz (THz) resonant filters as an example to verify the effectiveness of CircuitGTL, numerical results on the MITCircuitGNN experimental dataset show that: compared with state-of-the-art algorithm CircuitGNN, CircuitGTL achieves 9.2% improvement in cross-electromagnetic topologies performance prediction accuracy, 90.9% reduction in model convergence time and 33.6% reduction in the total coverage area of components with only 20% of data requirement; additionally, the design of CircuitGTL has lower-insertion loss, steeper skirts, and higher-passband intersection-over-union. These results provide valuable insights for lightweight, and high-precision design of coupled electromagnetic structures.

The integration of terrestrial and non-terrestrial networks (TN-NTN) with open radio access network (O-RAN) technology presents a significant advancement for facilitating scalable and immersive Metaverse services within 6G networks. Seamless virtual experiences necessitate highly reliable, low-latency communication, effective resource management, and adaptive decision-making to satisfy the varied and rigorous requirements of Metaverse applications, including gaming, healthcare, and autonomous systems. The inherent heterogeneity, dynamic nature, and substantial resource requirements of TN-NTN present significant challenges for effective resource allocation and optimizing quality of experience (QoE). Then, we formulate a multi-objective optimization problem for joint resource allocation and spectrum sharing in O-RAN-enabled TN-NTN Metaverse environments. This problem is inherently NP-hard due to the intricate coupling between continuous action spaces and discrete decision variables. Solving such a complex problem using traditional optimization approaches is complex. To overcome this, we transform the problem into a decentralized partially observable Markov decision process (Dec-POMDP) and address it using a hierarchical multi-agent deep reinforcement learning (MADRL) approach. This study presents a hierarchical multi-agent proximal policy optimization (MAPPO) framework, a new MADRL solution for dynamic resource allocation and spectrum sharing in O-RAN-enabled TN-NTN Metaverse environments. MAPPO facilitates collaborative learning among intelligent agents to optimize resource management strategies in a decentralized manner, considering essential metrics, including energy consumption, latency, and meta-distance. The proposed framework enhances resource utilization efficiency, minimizes latency, and improves the QoE for Metaverse users through the seamless allocation and management of resources. Comprehensive simulations show that MAPPO outperforms baseline methods, such as conventional reinforcement learning and centralized optimization approaches, achieving better energy efficiency, lower latency, and improved QoE. This demonstrates its effectiveness in adapting to dynamic 6G-enabled Metaverse requirements, enabling intelligent and scalable TN-NTN networks.

Satellite networks have long been regarded as a vital component of space communication systems, which provide integrated satellite-terrestrial broadband access in seamless coverage and cost-effective manner. The inter-satellite routing design for low earth orbit (LEO) satellite constellations is critical for achieving low-latency and high-reliability communication in the space communication systems. However, the inherent dynamic nature of LEO satellites, coupled with the variability in inter-satellite connectivity, imposes significant challenges for routing efficiency and network dependability. Existing routing schemes cannot handle such topological fluctuations due to their insensitivity to real-time network changes, thus suffering from performance degradations in highly dynamic space environments. This paper presents Iris, an intelligent reliable routing scheme for inter-satellite communication, aiming at increasing efficiency and reliability of the packet transmission process. Specifically, we propose a comprehensive deep reinforcement learning (DRL) framework that learns a policy to select routing paths automatically under the emerging software-defined satellite networking (SDSN) architecture. To strengthen fault-tolerance in fluctuating environments, we train an agent in an incremental manner by gradually increasing scenario complexity. Simulation results indicate that our solution significantly outperforms baselines and exhibits advances in adaptability and reliability, especially under dynamic environments with frequent topology changes.

In advanced vehicular networks, Roadside Unit (RSU)-based amplify-and-forward (AF) relay and passive Reconfigurable Intelligent Surface (RIS) are two potential helpers to enhance the vehicle-to-vehicle (V2V) communications when the direct link experiences poor quality. This paper presents a comprehensive comparison of the two enhancement modes from the outage performance perspective. In the presence of both direct link and enhanced link, the analytical expressions of the outage probability (OP) for the V2V communication under the two enhancement modes are derived respectively. Moreover, considering the co-channel interference caused by relay/RIS, the OP of the neighbouring vehicle-to-infrastructure (V2I) communication is also derived. Additional analysis compares the diversity order and the strength of interference created by the V2V communication under the two enhancement modes. Further discussions are presented on the effect of the channel estimation error and phase quantization error under the RIS mode. Finally, the pros and cons of the two enhancement modes are demonstrated by both the analytical and numerical results.

Underwater operations frequently encounter turbid environments, where light absorption and scattering by suspended particles degrade image quality by causing color distortion, uneven brightness, and blurred details. Clear imaging in such conditions is essential for enhancing the efficiency and effectiveness of underwater tasks, including exploration, marine ecological monitoring, and the preservation of underwater cultural heritage. However, existing underwater image enhancement methods struggle to perform well in turbid waters, especially in highly turbid conditions. In this study, we present an advanced method designed to significantly improve the clarity of images captured in turbid water. We begin by introducing an adaptive color correction algorithm that uses the dominant color channel’s pixel values to adjust and restore the colors of other channels, mitigating color distortion in turbid conditions. Subsequently, we apply adaptive threshold segmentation and turbidity assessment to automatically calibrate histogram equalization, which enhances local contrast and suppresses noise. Finally, we develop a dark channel prior based on turbidity background light estimation, which further improves color restoration and detail recovery. Our proposed method outperforms existing state-of-the-art techniques in color restoration, turbidity removal, and detail enhancement. Experimental results demonstrate that our approach effectively enhances imaging performance in turbid waters, thereby significantly improving the operational efficiency of various underwater applications.

Federated learning (FL) has gained attention for enabling efficient distributed learning while maintaining data privacy. However, the data privacy constraint reduces the transparency in the agents’ model update making the learning process vulnerable to Byzantine attacks. In this paper, a mathematical proof is provided to show that when the traditional model-combining scheme is used, the model will eventually diverge to non-useful solutions in the presence of Byzantine agents independently from their number or their contributions. A low complexity norm-control based aggregation approach is also proposed and shown to converge to the optimal and sub-optimal solutions in the absence or presence of Byzantine nodes, respectively. Monte-Carlo simulations are also conducted to verify and validate the mathematical derivations and the efficiency of the proposed approach in protecting the FL model.

Patent data generally includes information from different perspectives or different types, and its heterogeneous attributes can be greatly beneficial to data clustering analysis. However, the existing patent analysis method always focus on the patent text cues, and such a strategy merely depends on the feature information to capture the data characteristics, failing to multi-type informative patent representation. Therefore, in this paper, to model the underlying structure/relationships of patent data, we employ the knowledge graph to depict the heterogeneous attributes of patent, and propose a novel Knowledge Graph-based Patent Clustering (KGPC) method, where the relationship reconstruction in knowledge graph as well as clustering-oriented representation refinement for patent clustering are jointly considered. With this model, there are three components, i.e., entity representation refinement, relationship reconstruction and self-supervised entity clustering. Given a patent knowledge graph as input, the entity representation refinement can be mutually boosted by the relationship reconstruction and self-supervised clustering objective, thereby leading to a balanced clustering-oriented output. Extensive experiments on several real-world patent knowledge graph datasets validate the effectiveness of KGPC while compared with the state-of-the-art.

Mobile edge computing (MEC), along with device-to-device (D2D) assisted MEC (D-MEC), are promising technologies that could improve the quality-of-experience for mobile devices (MDs) by offloading their tasks to edge servers or nearby idle MDs. There is a popular trend to develop distributed task offloading algorithms using multi-agent reinforcement learning (MARL), whose adoption of central critics during training makes the offloading still size-sensitive. Therefore, this paper proposes a Size-Insensitive Task Offloading (SITOff) algorithm for D-MEC based on fully-distributed offloading without maintaining any central venue. Specifically, taking advantage of the inherent graph-like structure of D-MEC, SITOff adopts graphs to represent MDs’ states and relationships and form each MD's local knowledge about D-MEC through graph computation. Furthermore, considering the limitation of local knowledge in performing whole performance-oriented offloading, each MD utilizes D2D-transmitting to exchange knowledge with its neighbors and form a comprehensive knowledge about D-MEC to enhance the coordination of distributed offloading. Additionally, regarding the different impacts of neighbors’ knowledge, each MD leverages attention mechanisms to selectively learn its neighbors’ knowledge during knowledge-exchange. Extensive experimental results show the superiority of SITOff over state-of-the-art MARL-based offloading algorithms in D-MEC with various MDs, and the easy collaboration of SITOff with curriculum-learning for large-scale D-MEC offloading.

Mobile crowdsensing (MCS) has emerged as a prominent trend across various domains. However, ensuring the quality of the sensing data submitted by mobile users (MUs) remains a complex and challenging problem. To address this challenge, an advanced method is needed to detect low-quality sensing data and identify malicious MUs that may disrupt the normal operations of an MCS system. Therefore, this article proposes a prediction- and reputation-based truth discovery (PRBTD) framework, which can separate low-quality data from high-quality data in sensing tasks. First, we apply a correlation-focused spatio-temporal Transformer network that learns from the historical sensing data and predicts the ground truth of the data submitted by MUs. However, due to the noise in historical data for training and the bursty values within sensing data, the prediction results can be inaccurate. To address this issue, we use the implications among the sensing data, which are learned from the prediction results but are stable and less affected by inaccurate predictions, to evaluate the quality of the data. Finally, we design a reputation-based truth discovery (TD) module for identifying low-quality data with their implications. Given the sensing data submitted by MUs, PRBTD can eliminate the data with heavy noise and identify malicious MUs with high accuracy. Extensive experimental results demonstrate that the PRBTD method outperforms existing methods in terms of identification accuracy and data quality enhancement.

The rapid advancement of the UAV swarm network has made its widespread application across a multitude of domains. However, the inherently dynamic nature of the network often gives rise to intermittent connectivity issues, leading to a significant reduction in the data transmission capacity. To address this challenge, this study explores the integration of Information-centric Network (ICN) with the delay-tolerant network (DTN). This design aims to enhance message delivery rates by caching content data packets in UAV nodes. Building upon this architecture, we study the congestion control and load balancing problem. We design an on-demand collaborative communication routing algorithm. In our design, we first propose a routing decision model that incorporates multiple routing metrics to capture the dynamic evolution patterns of network nodes, effectively controlling local congestion issues. Subsequently, we employ Lyapunov optimization techniques to achieve a network load balancing. By integrating the Lyapunov drift function, we ensure the stability of a feasible solution space within the model. Additionally, considering the high communication overhead caused by the sparse communication characteristics of DTN, we deploy a Multi-Agent Incentivized Communication (MAIC) algorithm to optimize routing scheduling strategies. Within the MAIC framework, each agent develops unique models for its teammates to generate customized information and minimize network information redundancy. Simulation results demonstrate that this algorithm effectively ensures a congestion control and a load balancing within the UAV swarm network while maintaining communication overhead in routing computations at a minimal level.

Machine learning models can support decision-making in mobile terminals (MTs) deployments, but their training generally requires massive datasets and abundant computation resources. This is challenging in practice due to the resource constraints of many MTs. To address this issue, data-parallel distributed learning can be conducted by offloading computation tasks from MTs to the edge-layer nodes. To facilitate the establishment of trust, one can leverage trust management, say to use trust values derived from local model quality and evaluations by other nodes as access criteria. Nonetheless, security and performance considerations remain unsolved. In this paper, we propose a blockchain-assisted dynamic trust management scheme for distributed learning, which comprises nodes attributes registration, trust calculation, information saving, and block writing. The proof of stake (PoS) consensus mechanism is leveraged to enable efficient consensus among the nodes using trust values as stakes. The incentive mechanism and corresponding dynamic optimization are then proposed to further improve system performance and security. The reinforcement-learning approach is leveraged to provide the optimal strategy for nodes’ local iterations and selection. Simulations and security analysis demonstrate that our proposed scheme can achieve an optimal trade-off between efficiency and quality of distributed learning while maintaining system security.

The rise of artificial intelligence of things (AIoT) has enabled AI-powered services within wireless networks, relying on well-trained machine learning (ML) models. Distributed learning, such as federated learning (FL), allows smart devices (SDs) to collaborate on model training without sharing raw data, but privacy protection is still necessary to prevent potential information leakage from evolving attacks. Additionally, training efficiency is hampered by limited resources and selfishness of SDs. This paper considers a layered distributed learning scenario using a double-layer auction approach, where model users act as buyers, SDs act as data owners contributing their datasets, and edge layer nodes (ELNs) serve as model trainers providing computing resources. The differential privacy (DP) mechanism is utilized to add Gaussian noise to the trained models by the ELNs. Then, we formulate a joint optimization problem to optimize task assignment, data owners' sensing durations, and model trainers' local iterations and privacy budgets, aiming to maximize the utility of all participants while ensuring cost-effective and privacy-preserving distributed learning. We decompose the formulated problem into four sub-problems and design a layered algorithm to solve them and derive collaboration strategies. Simulation results validate the algorithm's performance and demonstrate the advantages of our proposed approach compared to benchmark schemes.

The spotlight on autonomous aerial vehicles (AAVs) is to enhance wireless communications while ignoring the potential risk of AAVs acting as adversaries. Due to their mobility and flexibility, AAV eavesdroppers pose an immeasurable threat to legitimate wireless transmissions. However, the existing fixed jamming scheme without cooperation cannot counter the flexible and dynamic AAV eavesdropping. In this article, a cooperative intelligent jamming scheme is proposed, authorizing ground jammers (GJs) to interfere with AAV eavesdroppers, generating specific jamming shields between AAV eavesdroppers and legitimate users. Toward this end, we formulate a secrecy capacity maximization problem and model the problem as a decentralized partially observable Markov decision process (Dec-POMDP). To address the challenge of the huge state space and action space with network dynamics, we leverage a deep reinforcement learning (DRL) algorithm with a dueling network and double-Q learning (i.e., dueling double deep Q-network) to train policy networks. Then, we propose a multi-agent mixing network framework (QMIX)-based collaborative jamming algorithm to enable GJs to independently make decisions without sharing local information. Additionally, we perform extensive simulations to validate the superiority of our proposed scheme and present useful insights into practical implementation by elucidating the relationship between the deployment settings of GJs and the instantaneous secrecy capacity.

Computing offloading optimization for energy saving is becoming increasingly important in low-Earth orbit (LEO) satellite-terrestrial integrated networks (STINs) since battery techniques have not kept up with the demand of ground terminal devices. In this paper, we design a delay-based deep reinforcement learning (DRL) framework specifically for computation offloading decisions, which can effectively reduce the energy consumption. Additionally, we develop a multi-level feedback queue for computing allocation (RAMLFQ), which can effectively enhance the CPU’s efficiency in task scheduling. We initially formulate the computation offloading problem with the system delay as Delay Markov Decision Processes (DMDPs), and then transform them into the equivalent standard Markov Decision Processes (MDPs). To solve the optimization problem effectively, we employ a double deep Q-network (DDQN) method, enhancing it with an augmented state space to better handle the unique challenges posed by system delays. Simulation results demonstrate that the proposed learning-based computing offloading algorithm achieves high levels of performance efficiency and attains a lower total cost compared to other existing offloading methods.

This paper presents a new framework that integrates Federated Learning (FL) with advanced privacy-preserving mechanisms to enhance the security of millimeter-wave (mmWave) beam prediction systems in 6G networks. By decentralizing model training, the framework safeguards sensitive user information while maintaining high model accuracy, effectively addressing privacy concerns inherent in centralized Machine learning (ML) methods. Adaptive noise augmentation and differential privacy principles are incorporated to mitigate vulnerabilities in FL systems, providing a robust defense against adversarial threats such as the Fast Gradient Sign Method (FGSM). Extensive experiments across diverse scenarios, including adversarial attacks, outdoor environments, and indoor settings, demonstrate a significant 17.45% average improvement in defense effectiveness, underscoring the framework’s ability to ensure data integrity, privacy, and performance reliability in dynamic 6G environments. By seamlessly integrating privacy protection with resilience against adversarial attacks, the proposed solution offers a comprehensive and scalable approach to secure mmWave communication systems. This work establishes a critical foundation for advancing secure 6G networks and sets a benchmark for future research in decentralized, privacy-aware machine learning systems.

Recently, unmanned aerial vehicles (UAVs) have demonstrated exemplary performance in various scenarios, such as search and rescue, smart city services, and disaster response applications. UAVs can facilitate wireless power transfer (WPT), resource offloading, and data collection from ground IoT devices. However, employing UAVs for such applications poses several challenges, including limited flight duration, constrained energy resources, and the age of information of the data collected. To address these challenges, we employ a UAV swarm to maximize energy harvesting (EH) and data rates for IoT devices by optimizing UAV paths and integrating reconfigurable intelligent surfaces (RIS) technology. We tackle critical constraints, including UAV energy consumption, flight duration, and data collection deadlines, by formulating an optimization problem to find optimal UAV paths and RIS phase shifts. Given the complexity of the problem, its combinatorial nature, and the challenges of obtaining an optimal solution through conventional optimization methods, we decompose the problem into two sub-problems, employing deep reinforcement learning (DRL) to optimize EH and particle swarm optimization (PSO) to optimize RIS phase shifts. Our extensive simulations show that the proposed solution outperforms competitive algorithms, including Brute-Force-PSO, AC-PSO, and PPO-PSO algorithms, providing a robust solution for modern IoT applications.

In recent years, there has been a rising trend towards emerging applications (e.g., brain-computer interaction and haptics-based autonomous cars) with diverse requirements. To effectively enable these applications via autonomous operation and intelligent analytics, one can use a metaverseFor more details on how a metaverse can enable emerging applications and architecture, please refer to khan2024ametaverse. In a metaverse, we have two spaces: (a) a meta space based on a virtual model that performs analysis and resource management and (b) a physical space comprised of real world entities. A metaverse effectively enables emerging applications by performing three main tasks: (a) distributed learning of metaverse models; (b) instantly serving the end-users; and (c) sensing of the physical environment and sharing it with the meta space for synchronized operation. To perform these tasks, efficient wireless resource management is needed. Therefore, a novel resource scheduling framework for the wireless metaverse to enable various applications is proposed. Our aim is to minimize the cost of learning and sensing in metaverse. Subsequently, we formulate a problem. Meanwhile, the reliability as well as latency constraints of the service-requesting devices/users will be fulfilled. We assign multiple resource blocks to learning and sensing devices/units, whereas we use a concept of puncturing for service-requesting devices/users upon arrival. We use a scheme that is based on block successive upper-bound minimization and convex optimization for solving our formulated problem. At the end, we use empirical cumulative distribution function vs. cost and cost vs. metaverse entities for numerical evaluations.

AI has numerous applications in virtual reality (VR), particularly in analyzing the progress of its implementation, identifying existing limitations, and determining future development paths. This study finds that AI’s main contributions to VR are in four key areas: creating intelligent virtual characters, advancing education and training, providing medical assistance, and generating dynamic scenes. This interdisciplinary convergence also brings significant opportunities, particularly in sectors such as education, healthcare, gaming, and corporate training. Additionally, the study discusses technical challenges related to computational costs, real-time feedback, user privacy, and algorithmic ethics. Critical challenges persist in data processing bottlenecks, privacy protection issues, and user adaptation. While AI enhances VR’s intelligence and interactivity, breakthroughs are still needed in cross-modal integration, privacy, security, and user experience. Future advancements in deep learning and reinforcement learning may unlock unlimited potential for AI-driven VR in personalized adaptation and immersive interaction. Therefore, this study provides a comprehensive analysis of AI–VR integration, providing valuable insights for academic research and technological development.

Hierarchical federated learning (HFL) improves the scalability and efficiency of traditional federated learning (FL) by incorporating a hierarchical topology into the FL framework. In a typical HFL system, clients are divided into multiple tiers, and the training process involves both local and global model aggregation. However, existing HFL approaches have several significant drawbacks. Firstly, the root parameter server (PS) is vulnerable to single-point failure and also acts as a bottleneck for global aggregation. Additionally, frequent global aggregation over the wide area network (WAN) incurs substantial communication costs, which negatively affect training efficiency. In this paper, we propose a novel HFL algorithm called CPFedAvg to address the aforementioned challenges. CPFedAvg introduces a root-free hierarchical topology, where the top tier consists of multiple PSes, effectively resolving the issues associated with the root PS. Additionally, we substitute the expensive global aggregation with parameter mixing operations between the PSes in the top tier. We analyze the convergence rate of CPFedAvg under non-convex loss. Based on this analysis, we formulate a convex optimization problem to optimize the frequency of executing local aggregations between consecutive parameter mixing operations. To simulate real-world communication networks, we develop FedNetSimulator to simulate a diverse range of FL communication processes. Finally, we conduct extensive experiments using real datasets (i.e., CIFAR-10 and CIFAR-100). The experimental results demonstrate that CPFedAvg can improve model accuracy by up to 18% and the speedup can be as high as 6 compared with the state-of-the-art baselines.

Electrocardiogram(ECG) signal analysis is crucial in healthcare service computing. Ensuring accurate assessment of ECG signal quality is vital to prevent wastage of transmission bandwidth and ineffective analysis caused by noise. This enables the efficient utilization of service resources. However, existing ECG signal quality assessment(SQA) methods primarily focus on single-modal learning, overlooking the interrelation of ECG in a multimodal feature space and failing to effectively exploit available information for pattern mining. In this paper, we model the SQA for ECG as an anomaly detection problem and propose a multimodal unsupervised SQA method. It jointly explores the boundaries between high-quality ECG and noise in both the time and frequency domains by introducing time-frequency pseudo anomalies. Specifically, we first simulate real ECG noise from the time-domain using a combination of a series of noises and convert it to the frequency-domain to form time-frequency pseudo-anomalies. Next, we map the time-frequency pseudo anomalies onto hyperspheres and jointly refine the hyperspheres learned only from high-quality ECG samples in both feature spaces. Finally, the noise score is defined as the distance from the joint time-frequency features to the center of the hypersphere. Multiple experiments on various real-world ECG datasets validate the superior performance of our proposed method.

LLM serving is shifting from cloud to edge due to privacy concerns over user interaction data. However, mobile devices struggle with very limited computing power and memory, requiring collaboration among multiple devices to run LLM apps. The mainstream solution, pipeline parallelism, is inefficient for such cases because mobile devices typically run only one inference task at a time. This article argues that tensor parallelism, despite its high communication cost, can better fit such scenarios. We introduce TPI-LLM, a compute and memory-efficient tensor parallel inference system designed to run 70B-scale LLMs on low-resource mobile devices. It keeps sensitive raw data local on users’ devices and employs a sliding window memory scheduler to dynamically manage layer weights. It overlaps disk I/O with computation and communication, enabling efficient operation of large models on memory-limited devices. Extensive experiments show that TPI-LLM reduces token latency by 80%–90% compared to Transformers, Accelerate, and Galaxy. It also cuts the peak memory footprint by 90%, requiring just 3.1 GiB of memory for 70B-scale models.

Artificial Intelligence of Things (AIoT) utilizes a combination of computing, storage, and networking resources to provide highly reliable and low-latency information services to the industrial production processes. However, with the increasing integration of numerous smart terminals into real-time sensing, autonomous decision-making, and precision manufacturing execution systems, the current task scheduling pattern appears to be insufficient to meet the latency requirements of computationally intensive tasks. To address the above challenge, this paper presents a collaborative edge-terminal task offloading scheme. First, the Task Backlog and Multi-slot Scheduling (TBMS) problem is converted from a long-term offloading problem to a single timeslot scheduling problem by Lyapunov optimization. Then, to simplify the problem, the single timeslot problem is decomposed into three subproblems: the local resource allocation problem, the server resource allocation problem, and the indicator weight selection problem. The two resource allocation problems are proved to be convex, which have been solved by using the Bisection method and the Karush-Kuhn-Tucker (KKT) method, respectively. For the indicator weight selection problem, we proposed the enhanced jumping spider optimization algorithm that integrates the elite opposition-based learning strategy. Extensive experiments show that the proposed algorithm can alleviate the computing pressure of the terminal device. Compared with the traditional methods, the offload system cost is effectively reduced by at least 58.8% and the average execution success rate is increased by at least 6%.

To mitigate the rising concern of privacy leakage, the federated recommender (FR) paradigm emerges as a potential solution, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private federated recommender (DPFR) further enhances the FR by injecting differentially private (DP) noises into clients' data. Yet, current DPFRs, suffering from noise distortion, cannot achieve the desired satisfactory accuracy. Various efforts have been dedicated to improving DPFRs by adaptively allocating the privacy budget over the learning process. However, due to the intricate relation between privacy budget allocation and model accuracy, existing attempts are still far from maximizing the DPFR accuracy. To address this challenge, we develop a BGTplanner (Budget Planner) to strategically allocate the privacy budget for each round of the DPFR training, improving overall training performance. Specifically, we leverage the Gaussian process regression and historical information to predict the change in the recommendation accuracy with a certain allocated privacy budget. Additionally, Contextual Multi-Armed Bandit (CMAB) is harnessed to make privacy budget allocation decisions by reconciling the current improvement and long-term privacy constraints. Our extensive experimental results on real datasets demonstrate that theBGTplannerachieves an average improvement of 6.76% in training performance compared to the state-of-the-art baselines.

Medical healthcare centers are envisioned as a promising paradigm to handle vast data for various disease diagnoses using artificial intelligence. Traditional Machine Learning algorithms have been used for years, putting the sensitivity of patients' medical data privacy at risk. Collaborative data training, where multiple hospitals (nodes) train and share encrypted federated models, solves the issue of data leakage and unites resources of small and large hospitals from distant areas. This study introduces an innovative framework that leverages blockchain-based Federated Learning to identify 15 distinct lung diseases, ensuring the preservation of privacy and security. The proposed model has been trained on the NIH Chest Ray dataset (112 120 X-Ray images), tested, and evaluated, achieving test accuracy of 92.86%, a latency of 43.518625 ms, and a throughput of 10034017 bytes/s. Furthermore, we expose our framework blockchain to stringent empirical tests against leading cyber threats to evaluate its robustness. With resilience metrics consistently nearing 87% against three evaluated cyberattacks, the proposed framework demonstrates significant robustness and potential for healthcare applications. To the best of our knowledge, this is the first paper on the practical implementation of blockchain-empowered FL with such data and several diseases, including multiple disease coexistence detection.

In this paper, we employ an unmanned aerial vehicle (UAV) to ensurethe freshness of sensing data, as measured by the age of information (AoI), in Internet of Things (IoT) networks. Specifically, the UAV switches between flying and hovering modes to collect data from widely distributed IoT devices. UAV trajectory planning, which determines the times and moments of data collection, is vital for optimizing the system AoI. Considering the limited UAV onboard energy and mission duration, AoI-optimal trajectory planning is formulated as a mixed-integer nonlinear programming (MINLP) problem. We first decompose the MINLP problem into two subproblems: a UAV time scheduling subproblem and a UAV path planning subproblem. Then, we propose a learning-based iterative (LBI) algorithm that consists of two modules: a successive convex approximation (SCA)-based module for solving the time scheduling subproblem, and a hierarchical asynchronous advantage actor-critic (A3C) module for addressing the path planning subproblem. The numerical results verify that the proposed LBI algorithm outperforms typical baselines in terms of the AoI performance.

To overcome the challenges of complex time-varying satellite channels and severe inter-user interference in non-orthogonal multiple access (NOMA), rational power allocation and accurate multi-user joint detection methods are essential. In this paper, a sparrow search algorithm-based resource allocation and deep learning-based joint detection scheme (SSA-DeepJD) in the satellite-terrestrial NOMA system is proposed. First, the NOMA-orthogonal frequency division multiplexing (OFDM) system model is constructed. Next, a convolutional neural network-based image super-resolution recovery network is proposed for offline training and online channel estimation, which incorporates densely connected convolutional layers and residual learning to model for handling complex non-linear channel fitting. Then, a multi-user signal detection based on an iterative deep neural network is proposed, which is iteratively retrained to improve the detection accuracy. Finally, due to the significant impact of the power allocation on the system error performance, the optimal power allocation is found within the power allocation factor threshold based on SSA. Simulation results show that the proposed SSA-DeepJD algorithm is well-suited for multi-user superposed NOMA systems and complex non-linear channel environments. Compared to the baseline algorithms, the SSA-DeepJD algorithm degrades the Bit Error Rate (BER) by 21.5 dB and 11.9 dB in the 2-user and 3-user NOMA systems, respectively.

In this paper, the covert performance of the non-orthogonal multiple access (NOMA) system with random transmit power under hardware impairments over the Nakagami fading channel is investigated, where the stronger user serves as a covert user (Bob) and receives the secure message from the transmitter (Alice) under the cover of the weaker user (Carl). The transmission is monitored by a warden (Willie). To improve the covert performance, we randomize the transmit power of Alice, which is subject to a uniform distribution. Based on this, considering the randomness of covert user’s position, we derive the closed-form expressions of the average detection error probability (ADEP) respectively for the warden with instantaneous and statistical channel information. Then, the optimal detection threshold based on one-dimensional (1D) search method is presented for minimizing the ADEP. To simplify the optimal threshold, the suboptimal threshold with low complexity is also derived. Besides, the outage probability (OP) of the system is deduced for performance analysis and optimization. Under the constraints of covertness and reliability, the power allocation (PA) coefficient is optimized to minimize the OP of the covert user, and the 1D search scheme is proposed to obtain the optimal solution. Also, a suboptimal scheme is presented to reduce the complexity of the optimal scheme. Numerical results are presented to validate our theoretical analysis, and show that randomizing the transmit power can effectively improve the covert performance. Moreover, the system with two optimized thresholds can obtain lower ADEP than that with a fixed threshold, and the system with two optimized PA coefficients has a lower OP than that with a fixed PA.

In this paper, we introduce third-party intelligent reflecting surfaces (TIRSs) into the physical layer security aware wireless communication system, where a central legitimate transmitter is designed to transmit secret signals to a group of legitimate receivers in the presence of the threat from an active eavesdropper (EV). Due to the channel reshaping ability of TIRSs, they are able to not only help legitimate pairs (LPs) enhance the secure transmission rate but also assist EV in improving the eavesdropping performance. Furthermore, with the potential selfishness, TIRSs may dynamically choose to ally with LPs or EV in exchange for potential benefits (e.g., payoffs). This leads to complex dynamic ally-adversary relationships among LPs, EV, and TIRSs under unpredictable wireless channel conditions. To address this issue, we formulate a repeated coalition formation game (RCFG) with dynamic decision-making to model the long-term strategic interactions among LPs, EV, and TIRSs. In particular, we theoretically analyze the existence of Nash equilibrium in the formulated RCFG, and then propose a switch operations-based coalition selection along with a deep reinforcement learning (DRL)-based approach for obtaining such an equilibrium. Simulations examine the feasibility of the proposed approach and show its superiority over counterparts.

This paper addresses long-term energy efficiency in a wireless power transfer-enabled mobile-edge computing (MEC) system, facing challenges from time-varying channels and stochastic task arrivals. We formulate the problem to optimize offloading, power transfer duration, and energy consumption while ensuring queue stability. We propose a novel Lyapunov-guided deep reinforcement learning (LyCNN-DRL) algorithm to efficiently solve the long-term mixed integer non-linear programming problem without prior knowledge of future conditions. The approach decomposes the problem into resource allocation and binary offloading components, using a convolutional neural network for near-optimal offloading decisions and the Lagrange dual function for optimal resource allocation. Extensive simulations show that LyCNN-DRL outperforms benchmark algorithms in energy efficiency and latency, achieving over 97% of the optimal utility while reducing execution latency to approximately 50 milliseconds in ten-WD networks. Additionally, we derive the trade-off between energy efficiency and queue length as [O(1/V),O(V)], where V is the Lyapunov control parameter.

Reciprocal recommender systems (RRS) have been widely used in online platforms such as online dating and recruitment. They can simultaneously fulfill the needs of both parties involved in the recommendation process. Due to the inherent nature of the task, interaction data is relatively sparse compared to other recommendation tasks. Existing works mainly address this issue through content-based recommendation methods. However, these methods often implicitly model textual information from a unified perspective, making it challenging to capture the distinct intentions held by each party, which further leads to limited performance and the lack of interpretability. In this paper, we propose a Knowledge-Aware Explainable Reciprocal Recommender System (KAERR), which models metapaths between two parties independently, considering their respective perspectives and requirements. Various metapaths are fused using an attention-based mechanism, where the attention weights unveil dual-perspective preferences and provide recommendation explanations for both parties. Extensive experiments on two real-world datasets from diverse scenarios demonstrate that the proposed model outperforms state-of-the-art baselines, while also delivering compelling reasons for recommendations to both parties.

This paper examines the security challenges while implementing Machine Learning (ML) algorithms to Sixth Generation (6G) networks, specifically in Millimeter-Wave (mmWave) beam prediction. While ML has significant benefits, the potential vulnerabilities of Artificial Intelligence (AI) models to adversarial attacks remain a concern. To mitigate these risks, a new Integrated Transformer and Feedforward Model (ITFM) has been introduced, which accomplishes real-time beamforming vector prediction and adversarial defense. This approach enhances the reliability and security of 6G applications. The proposed model demonstrated a 37.86% average increase in defense effectiveness against adversarial threats in indoor and outdoor scenarios.

The reciprocity of channel state information (CSI) observed by two devices communicating wirelessly has been leveraged to develop security solutions for resource-limited IoT devices. Despite considerable research in this area, much of the attention has been on theoretical and simulated analyses. However, the practical implementation of these security solutions faces significant challenges, primarily due to limited hardware capabilities and varying channel conditions. To bridge this research gap, we revisit the assumption of channel reciprocity from an experimental perspective. Our experimental investigations uncover a notable decline in channel reciprocity for low-cost devices due to the variability in channel conditions and the asynchronous nature of CSI measurements. Through our experiments, we highlight key practical factors contributing to the diminished channel reciprocity and demonstrate that Pearson’s correlation and time-lagged cross-correlation can effectively measure and assess the channel reciprocity property between two communicating devices. Building upon the experiments’ findings, we then introduce a technique that exploits the reciprocity of collected CSI data, as well as its temporal variations and time shifts to enhance device authentication resiliency through an effective detection of replay attacks.

The industrial Internet of Things (IIoT) and network slicing (NS) paradigms are key enablers of the industrial revolution in current and future mobile networks. However, peer-to-peer (P2P) resource blocks (RBs) exchange to match supply and demand in sliced IIoT networks requires proper incentivization and renegotiations between the service providers (SPs). This paper models the business strategic interactions between seller and buyer SPs as a coalitional game in which sellers form coalitions to set RB prices and buyers join coalitions to determine their best-response RB demand. The aim is to maximize the profit of the seller coalition and minimize the expenses of the buyer coalition while jointly contributing to maximize system RB utilization. Due to the uncertainty of network traffic, we propose a coalitional game-guided multiagent reinforcement learning approach that takes the output of the coalitional game as the starting Nash equilibrium (NE) and computes the optimal price and demand strategies of the coalitions regardless of network condition changes. Simulation results and analysis prove the efficacy of the proposed approach in terms of optimizing seller and buyer coalition payoffs, as well as maximizing the overall RB utilization.

Federated learning provides clients with a means of collaboratively training a global model without sharing their local data, managed by a central server. However, this server cannot always be trusted, as it may act dishonestly and compromise the privacy of clients’ local data. Consequently, mechanisms for privacy preservation and aggregation verification become crucial components of a secure federated learning system. In addition, support for weighted aggregation is also essential to address the challenges posed by non-IID training data. In this article, we present the Privacy-Preserving and Verifiable Weighted Aggregation (PVWA) scheme. Our approach introduces a new privacy-preserving solution by leveraging masking and homomorphic encryption techniques to protect local and global models, respectively. The masking protocol facilitates secure weighted aggregation, whereas a verification mechanism based upon homomorphic hashing and bilinear aggregated signatures ensures the correctness of aggregated results. Experimental evaluations of the performance, compared against alternative methods on two datasets, demonstrate its effectiveness and efficiency.

In recent years, with the development of wireless communication networks, federated learning (FL) has been widely deployed in distributed scenarios as a privacy-preserving machine learning paradigm. Due to its inherent features, FL shows vulnerability to backdoor attacks. In a backdoor attack, an adversary manipulates the global model’s output by compromising the model of one or multiple participants. Existing backdoor attacks are constrained to outputting a single specified target label during the inference phase, limiting the adversary’s flexibility to alter the model’s output when different target labels are required. In this paper, we study the multi-target attack scenario within the federated learning context, where the adversary aims to manipulate the global model to output various specified labels by inserting different types of triggers. To effectively insert multiple backdoors simultaneously without reducing the attack’s effectiveness, we propose MulDoor, a novel multi-target backdoor attack scheme. MulDoor incorporates the concept of supervised contrastive learning to learn the discrepancies among different types of triggers and mitigate interference between them. The experimental results demonstrate that MulDoor achieves better attack effectiveness compared to existing backdoor attacks in a multi-target backdoor attack setting.

Federated learning, while advancing data privacy, faces risks of sensitive information leakage through parameter updates, making it susceptible to inference and data reconstruction attacks. Fraudulent behaviors by central servers or clients can undermine the integrity of model training, thereby reducing accuracy and affecting decision-making quality. This paper introduces a bidirectional, privacy-preserving verifiable federated learning framework(BPVFL) built on homomorphic encryption and a novel three-party zero-knowledge protocol. This framework guarantees the integrity of server aggregation and the credibility of the information uploaded by clients. Experimental results demonstrate that BPVFL effectively protects client privacy, prevents fraud by servers and certain clients, and efficiently handles numerous client disconnections with minimal overhead.

With the rapid expansion of mobile internet usage, the prevalence of the Android operating system on smartphones is steadily growing. However, improper utilization of the Intent mechanism within Android applications can result in security vulnerabilities. Presently, the majority of Android security testing methods, which rely heavily on fuzzing, are predominantly focused on UI interactions, lacking sufficient testing capabilities for Intents. The motivation of this paper is to find a more effective testing method to improve the security detection capabilities of Intents. This paper introduces an Intent fuzzing method based on genetic mutation principles. Initially, we establish an Intent seed library using a text classification model, followed by employing Jaccard distance and minimum edit distance to refine high-quality seeds. Subsequently, we augment the seeds through extensive mutation using genetic algorithms, generating numerous test cases that exhibit structural similarity but contain varied content. During testing, we compare the state before and after Intent testing using image similarity to detect anomalies. Experimental results demonstrate that this method effectively enhances test coverage and identifies potential issues in edge cases. This approach offers an efficient means of conducting Intent security testing and enhances Android app robustness and security.

The integration of Mobile Edge Computing (MEC) with aerial platforms introduces novel potential for the Metaverse world by providing low-latency and highly reliable computing and communication services at the network edge. Nevertheless, this integration presents critical challenges, such as low Quality of Experience (QoE) due to the dynamic nature of aerial platforms, high resource demands, and the requirements for real-time data processing in the Metaverse environment. To address these challenges, we propose a Combinatorial Client-Master Multiagent Deep Reinforcement Learning (CCM-MADRL) based joint resource allocation and QoE maximization framework to enable intelligent real-time decision-making in aerial MEC enabled Metaverse services. We form a collaborative ecosystem where agents are designed to represent both Metaverse service providers and aerial platforms to promote fairness and efficiency in resource allocation, as well as optimize service delivery. By incorporating CCM, our approach considers diverse metrics, such as latency, reliability, meta-distance, and energy efficiency, to ensure a holistic optimization of Metaverse services. The MADRL approach enables adaptive decision-making, allowing the system to respond to the dynamic and unpredictable nature of Metaverse applications. Results from simulations that mimic realistic Metaverse scenarios demonstrate the effectiveness of the proposed CCM-MADRL framework in terms of improved service performance, reduced latency, cost, and virtual meta-distance, maximized average QoE utility of Metaverse users, and enhanced resource utilization compared to baseline algorithms.

The power of machine learning makes it available for medical image classification. However, machine learning requires large medical datasets to improve accuracy, which will involve patients’ private information and lead to their privacy leakage. Federated learning is a trending technique to both protect patients’ privacy and improve the accuracy of medical image classification. Unfortunately, current research shows that federated learning faces the risk of privacy leakage. In this paper, we propose a privacy-preserving scheme FHDM. Specifically, we construct a local hybrid differential privacy algorithm (LHDP) against patients’ privacy leakage. This hybrid algorithm utilizes both Gaussian and Laplace differential privacy without enlarging the privacy budget. We prove that the algorithm applies to the model parameter. Moreover, we design loss optimization and global optimization strategies on the algorithm to achieve higher accuracy in medical image classification. Finally, we validate FHDM in terms of privacy-preserving and model accuracy on real datasets. Experiments show that FHDM effectively protects privacy and improves the average accuracy by 10.0% compared to adopting the LDP-based scheme with the same medical image dataset and privacy budget.

As autonomous vehicles, smart infrastructure, and connected devices become integral components of our daily lives, the need to protect communication and establish trust among entities is fundamental. This problem necessitates the development of robust authentication mechanisms tailored for autonomous systems. In this paper, we address the critical challenge of designing authentication methods to verify the legitimacy of messages and participants in autonomous systems. The proposed methods aim to validate the origin of messages and the identity of participants, ensuring that only authorized entities interact with the system. Achieving this requires cryptographic techniques, digital signatures, and secure key management. To that end, we developed an algorithmic framework that includes message digest generation, digital signature creation, and recipient-side verification. Additionally, participant authentication and message integrity checks are incorporated to fortify the authentication process. The algorithm leverages public key cryptography to verify digital signatures and ensure the message's integrity. Second, we develop a simulation by harnessing Federated Learning (FL) which provides a dynamic and self-improving authentication mechanism that aligns with the high-reliability demands of modern autonomous applications on MATLAB. We elaborate on how addressing this problem is essential to bolster the security of autonomous systems, safeguard against cyber threats, and instill trust in the reliability and authenticity of communication within these systems. The proposed framework has been tested and validated on MATLAB.

The rapid evolution of 6G networks has brought novel security and privacy challenges in managing user data, particularly with the extensive use of millimeter wave communications (mmWave) technology. Ensuring robust security and privacy preservation in this dynamic network environment is paramount. This paper explores a multifaceted approach to address these challenges, focusing on Federated Learning (FL) for mmWave beam prediction and utilizing outlier detection in the aggregation process to mitigate adversarial attacks like the Fast Gradient Sign Method (FGSM). The potential of FL to enhance beam prediction accuracy while safeguarding user data privacy is investigated by training models on edge devices and aggregating model updates rather than raw data. Furthermore, integrating outlier detection in the aggregation process identifies and filters malicious model up-dates that may compromise the FL system's integrity. Simulations assessing the robustness of the proposed system against various threats reveal substantial enhancement in data security, with an average 15.30% increase in defense effectiveness.

Recently, Unmanned Aerial Vehicles (UAVs) have attracted the attention of researchers in academia and industry for providing wireless services to ground users in diverse scenarios like festivals, large sporting events, natural and man-made disasters due to their advantages in terms of versatility and maneuverability. However, the limited resources of UAV s (e.g., energy budget and different service requirements) can pose challenges for adopting UAV s for such applications. Our system model considers a UAV swarm that navigates an area, providing wireless communication to ground users with RIS support to improve the coverage of the UAV s. In this work, we introduce an optimization model with the aim of maximizing the throughput and UAVs coverage through optimal path planning of UAVs and multi-RIS phase configurations. The formulated optimization is challenging to solve using standard linear programming techniques, limiting its applicability in real-time decision-making. Therefore, we introduce a two-step solution using deep reinforcement learning and particle swarm optimization. We conduct extensive simulations and compare our approach to two competitive solutions presented in the recent literature. Our simulation results demonstrate that our adopted approach is 20 % better than the brute-force approach and 30% better than the baseline solution in terms of QoS.

Over the past decade, the Unmanned Aerial Vehicle (UAV) swarm has emerged as a disruptive force reshaping our lives and work. Benefiting from its fast and flexible deployment capabilities, UAV swarms have been widely applied to emergency communications. In the event of damaged ground communication base stations, UAV swarms can quickly reconstruct an emer-gency communication network. However, considering the limited coverage power of a single UAV node, it underscores the need for effective coordination among swarm units as well as diligent planning of a coverage trajectory. In this paper, we propose a cooperative intelligence-based UAV swarm approach for establishing emergency communications. We model a multi-UAV base station-assisted emergency communication scenario as a team Markov game model. To achieve cooperative collaboration among multiple UAVs, we propose a Q-function mixing network based coverage trajectory planning algorithm. Our experimental results demonstrate the superior convergence speed and throughput of the proposed algorithm.

Metaverse has gained rapid interest from the research community due to its various promising features. These features include proactive learning and self-sustainability. These features enable wireless systems with analysis prior to deployment, efficient resource management with strict latency, and operation with the least possible intervention from the operators. Therefore, deploying the metaverse for wireless systems will offer many benefits. However, metaverse signaling itself requires novel design. Therefore, in this work, a new framework using network virtualization for the metaverse is proposed. Such virtualization will provide more flexibility in terms of management by using the shared physical network resources to support different services. The proposed framework takes into account three key stakeholders, namely, the network operators, a metaverse operator, and the end-users. Resources will be purchased by a metaverse operator from network operators and allocated to users in the proposed framework. To carry out this interaction, a cost function is defined that considers both communication resources and computing resources. By optimizing communication resource management, computing resource management, association, communication resources cost, and computing resource management costs, the cost function is minimized. To achieve this, we propose a strategy based on matching theory and convex optimization. In order to demonstrate the effectiveness of the proposed scheme, numerical results are provided in the final section.

Due to the emergency of multi-view graph data, considerable attention is focused on the multi-view graph clustering. Although great efforts have been made in developing the multi-view graph clustering methods, most of them implicitly follow the homophily assumption, where the connected nodes with edges tend to be in the same category. As a matter of fact, such an ideal assumption is hard to be satisfied in the real-world graph data, and there are some heterogeneous edges connecting dissimilar nodes in graph. How to well consider the homophily and refine the noisy/heterogeneous edges in multi-view graph clustering still remains an under-explored challenge. Therefore, in this paper, we propose a Homophily-enhanced Adaptive Graph Refinement for Multi-view Graph Clustering (HomoMGC) method, where an adaptive graph refinement strategy is seamlessly designed. Specifically, a feature-oriented graph is constructed based on the shared feature, and an integrated graph is computed by averagely fusing all the input adjacent graphs. Then, the feature-oriented graph and integrated graph are stacked into a graph tensor with a low-rank tensor constraint, where a refined affinity probability matrix can be adaptively recovered from the integrated graph by considering multiple graph information as well as the semantics features. Extensive experiments on several benchmark datasets demonstrate the superiority of HomoMGC compared with the state-of-the-art graph clustering methods. For the code reproducibility, the source code of HomoMGC is public available at https://github.com/ManshengChen/Code-for-HomoMGc-master.

In the evolving landscape of sequential recommendation systems, the application of Large Language Models (LLMs) is increasingly prominent. However, current attempts typically utilize general-purpose LLMs, which present a mismatch in capability and a large semantic gap relative to the specialized needs of recommendation tasks. To tackle these issues, we introduce RecCoder, an innovative model that reformulates sequential recommendation as a code completion task. This approach leverages the superior reasoning capability of code LLMs as a backbone, aligning well with the requirements of recommendation systems. To bridge the semantic gap, RecCoder creates extra tokens for each item and employs item content to initialize token embeddings. Furthermore, we have developed a suite of Semantic Adaptation Fine-tuning tasks, tailored to enhance the model's acquisition of both content and collaborative semantic information, thus aligning the model's intrinsic capabilities with the unique demands of recommendation tasks. Through extensive testing on three public datasets, RecCoder has shown remarkable improvements over existing models in terms of recommendation accuracy and efficiency. This success highlights the substantial yet previously underexplored potential of code LLMs in improving recommendation accuracy and efficiency, suggesting a promising new direction for future research in this area. The implementation code is accessible at https://github.com/AllminerLab/Code-for-RecCoder-master.

In next basket recommendation, baskets are usually formed through a large number of user interactions with items in the early stage. In general, the existing methods for next basket recommendation primarily focus on historical purchase behavior of users, assuming that user purchase interests are static, and overlook the dynamic and diverse changes in user purchase interests. In order to fully capture dynamic user interests and provide users with more diverse recommendations, we propose our method, Dynamic Heterogeneous Graph Prompt (DHGP), for next basket recommendation. By constructing a dynamic heterogeneous graph, we can adequately consider the influence of various interactive behaviors on the user's baskets at different times. Furthermore, we introduce a periodic dynamic heterogeneous prompt strategy to capture the interest directions between baskets from different users and provide users with more diverse interest directions. Extensive experimental validation on six real world datasets demonstrates that our method shows strong applicability across datasets under various conditions and outperforms several state-of-the-art recommendation methods. To the best of our knowledge, DHGP is the first next basket recommendation method that effectively combines dynamic and heterogeneous information. The implementation code is accessible at https://github.com/AllminerLab.

With the explosive growth of information, recommendation systems have emerged to alleviate the problem of information overload. In order to improve the performance of recommendation systems, many existing methods introduce Large Language Models to extract textual information from description text. However, Large Language Models are trained on large-scale generic textual data and may face a semantic gap for downstream recommendation tasks. To address the above issues, we propose Contrastive Learning for Adapting Language Model to Sequential Recommendation (CLA-Rec). In CLA-Rec, we first extract text embeddings from description text using Large Language Models and align the text embeddings learned by Large Language Models with the collaborative information through contrastive learning to obtain high-quality item representations. Through semantic alignment, we bridge the semantic gap between Large Language Models and the recommendation task. To map textual information and collaborative information into user representations, we utilize a Transformer model to learn user representations and capture user preferences by combining the semantically aligned item representations. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches on multiple evaluation metrics, illustrating the effectiveness of the CLA-Rec model in adapting Large Language Models to recommendation tasks.

Federated graph learning (FGL) has risen as a promising paradigm for collaboratively training graph neural networks while safeguarding data privacy. Nevertheless, the distributed nature of FGL also renders it susceptible to backdoor attacks. Although backdoor attacks are recognized as a significant threat to both centralized graph learning and federated learning (FL), the study of such attacks in FGL remains very limited. Current research on FGL backdoor attacks often merely adapts centralized graph backdoor attacks or FL backdoor attacks designed for image classification tasks to the FGL context, leaving key issues such as the effectiveness of triggers and the stealthiness of malicious models largely unexplored. To bridge this research gap, in this paper, we propose a novel backdoor attack, named FGLBA, targeting the FGL paradigm. Specifically, we design an input-aware trigger generator that generates a customized trigger for each target node based on its feature vector and neighborhood information, making that poisoned nodes injected with triggers are more likely misclassified into the category specified by the attacker. Additionally, we develop a stealthy federated backdoor training strategy that leverages collaborative optimization among multiple malicious clients to circumvent existing server-side defenses. The trigger generator and malicious clients' local models are iteratively optimized through a bilevel optimization framework, enabling the malicious models to achieve optimal attack performance under the optimal trigger generator. Extensive experiments on 4 real-world datasets demonstrate the effectiveness and superiority of our attack, outperforming all baseline attacks and successfully bypass 6 state-of-the-art and classical FL backdoor defenses.

Next-basket recommendation (NBR) infers a set of items that a user will interact with in the next basket. Existing methods often struggle with the data sparsity problem, particularly when the number of baskets is significantly large due to diverse user behaviors. Cross-domain recommendation (CDR) can effectively alleviate this problem in NBR by transferring knowledge across different domains. Nevertheless, these methods often rely on the similarities of overlapping users, which leads to the negative transfer problem and ignores the overlapping items that are general in real-world scenarios like chain stores. In this paper, we provide a clear symbolic definition of cross-store recommendation (CSR) and distinguish it from CDR. We also propose a novel CSNBR model for cross-store next-basket recommendation task. To fully model the transferable collaborative information between two stores, we learn the embeddings of users, baskets, and items by two intra-store bipartite graphs, and use an inter-store unified bipartite graph to transfer the previously learned knowledge. Furthermore, to alleviate the negative transfer problem, we propose to reconstruct the inter-store unified bipartite graph by utilizing user embeddings obtained from the transfer layer and the disentanglement layer. We also employ two sequence encoders to model the historical sequential information at basket-level and item-level. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of the CSNBR model.

Federated learning (FL) is an emerging machine learning paradigm for preserving data privacy. However, diverse client hardware often has varying computation resources. Such system heterogeneity limits the participation of resource-constrained clients in FL, and hence degrades the global model accuracy. To enable heterogeneous clients to participate in and contribute to FL training, previous works tackle this problem by assigning customized sub-models to individual clients with model pruning, distillation, or low-rank based techniques. Unfortunately, the global model trained by these methods still encounters performance degradation due to heterogeneous sub-model aggregation. Besides, most methods are heuristic-based and lack convergence analysis. In this work, we propose the FedLMT framework to bridge the performance gap, by assigning clients with a homogeneous pre-factorized low-rank model to substantially reduce resource consumption without conducting heterogeneous aggregation. We theoretically prove that the convergence of the low-rank model can guarantee the convergence of the original full model. To further meet clients’ personalized resource needs, we extend FedLMT to pFedLMT, by separating model parameters into common and custom ones. Finally, extensive experiments are conducted to verify our theoretical analysis and show that FedLMT and pFedLMT outperform other baselines with much less communication and computation costs.

With the rapid development of Internet of Vehi-cles (IoV) technology, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication have become an important part of intelligent transportation systems (ITS). In V2V commu-nication, clustering is essential to optimize network efficiency and reliability. Conventional V2V clustering prioritizes the physical distance between vehicles, neglecting key attributes information of the vehicles (i.e., computational capacity, energy consumption) and behavior like relative motion states between vehicles. Thus, this paper proposes a clustering algorithm based on vehicle attributes and behavioral characteristics (C-VABC) that utilizes the Fuzzy C-Means (FCM) algorithm. Once clusters are established, intelligent data routing decisions between clusters is facilitated by a Deep Q-Network (DQN) based algorithm. This algorithm selects neighboring vehicular Cluster Heads with similar mobility states and higher computational power as the next hop vehicles for packet forwarding. The experimental results demonstrate that the proposed clustering algorithm possesses strong adaptability and reliability, while enhancing the communication quality and efficiency in vehicle networking situations. This provides invaluable support for intelligent transportation systems, as well as intelligent and autonomous driving systems.

With the development of intelligent edge computing (IEC) in industrial IoT (IIoT), there is a growing number of service providers trying to leverage computing resources in the cloud and at the edge to meet the users‘ demand for low latency and high reliability in diversified applications. This evolving landscape necessitates innovative approaches to manage and process the vast amounts of data generated by IIoT devices. Among these approaches, distributed learning frameworks, such as federated learning (FL), have emerged as popular solutions. However, compared to computing, communication remains the primary bottleneck that constrains the speed of federated model training. Most of the previous solutions have focused on reducing communication overhead. Differently, we propose a transmission-centric approach by designing an efficient communication archi-tecture for FL with cloud-edge collaboration, specifically aimed at enhancing communication capabilities through multi-path transmission. We deploy this FL system in a real environment and conduct extensive testing. The results demonstrate that the new approach can significantly reduce communication time in FL setting, thereby enhancing model aggregation efficiency and shortening the overall training duration. Compared to conventional single-path transmission, the proposed solution improves training efficiency by up to 26.4%.

In the present era, the increasing number of network devices and ubiquitous computing leads to the flow of enormous amounts of data traffic, including sensitive and confidential information, on the internet and carrying out commercial and banking transactions online. This gives cybercriminals an opportunity to launch an attack like phishing to steal confidential information from the user and gain unauthorized access. This can be mitigated by the help of an intelligent machine learning-based phishing detection system, which can detect potential phishing attacks and take appropriate action. In this paper, we have addressed this major cyber issue and proposed a machine learning-based phishing detection scheme (in short, EM-PAD), which is trained on a benchmark dataset and evaluated on standard metrics: F1-score and Accuracy. The proposed model is compared with different existing schemes based on Accuracy, indicating that it has outperformed them with remarkable results.

Solar panels, the primary components of solar photovoltaic systems, play a pivotal role in converting sunlight into electricity. However, the efficiency and performance of solar panels can be significantly influenced by environmental factors, notably the accumulation of dust and debris on their surfaces. This paper focuses on the investigation of deep learning image classification techniques to detect dust periodically, utilizing solar panel images collected by drones or robots. This approach aims to reduce the impact of dust on solar panels and help identify effective cleaning methods for each case. This work proposes the development of a deep learning binary image classifier model specifically designed to differentiate between “dusty” and “clean” solar panels. The proposed system is based on pre-trained deep learning models fine-tuned for dusty solar panel detection. The results demonstrate that fine-tuning the weights of the pre-trained model enhances performance, with the EfficientNetB7 model yielding the best outcome.

In this paper, we present a peer-to-peer (P2P) energy trading system designed for equitable energy exchanges within a microgrid. Our objective is to enhance the fairness of energy distribution among all microgrid members. To achieve this, we introduce a novel objective function that enables us to simultaneously maximize overall welfare and minimize disparities among microgrid participants, thus promoting equitable transactions. The core of our system is an agent proficiently trained via deep reinforcement learning, ensuring efficient and equitable energy distribution across the network. We simulated the energy consumption of 8 houses, and their energy production to assess the feasibility and efficiency of the proposed system. The utilization of the proposed approach has yielded promising results. The intelligent agent was able to execute transactions and improve the fairness of energy trading within the microgrid. The overall average welfare of the microgrid over a day increased by 99.8%, while the disparities among the members were reduced.

Recent advancements in renewable energy technologies, along with the energy exchange capabilities of Electric Vehicles (EVs), present new opportunities for enhancing renewable energy management and its integration into traditional power systems. Despite these advancements, challenges such as the pricing of charging and discharging clean energy, and the distribution of available energy supplies persist in the realm of energy trading. Our research addresses these issues by leveraging Vehicle-to-Everything (V2X) technologies, which enable EVs to distribute energy to a wide range of consumers. We introduce a dual-level optimization approach that synchronizes financial incentives with the variable electricity prices at EV charging in the parking. This approach is supported by a state-of-the-art reinforcement learning model that integrates primal-dual optimization with upper-confidence bound techniques. Our model is specifically designed to optimize both power management and the effective use of incentives. The overarching goal of this strategy is to augment the adaptability of Vehicle-to-Grid (V2G) and Vehicle-to-Vehicle systems to encourage user participation in energy exchange processes, thereby promoting a more efficient and integrated renewable energy ecosystem.

Intrusion Detection Systems (IDSs) have played a significant role in detecting and preventing cyber-attacks within traditional computing systems. It is not surprising that the same technology is being applied to secure Internet of Things (IoT) networks from cyber threats. The limited computational resources available on IoT devices make it challenging to deploy conventional computing-based IDSs. The IDSs designed for IoT environments must also demonstrate high classification performance, utilize low-complexity models, and be of a small size. Despite significant progress in IoT-based intrusion detection, developing models that both achieve high classification performance and maintain reduced complexity remains challenging. In this study, we propose a hybrid CNN architecture composed of a lightweight CNN and bidirectional LSTM (BiLSTM) to enhance the performance of IDS on the UNSW-NB15 dataset. The proposed model is specifically designed to run onboard resource-constrained IoT devices and meet their computation capability requirements. Despite the complexity of designing a model that fits the requirements of IoT devices and achieves higher accuracy, our proposed model outperforms the existing research efforts in the literature by achieving an accuracy of 97.28% for binary classification and 96.91% for multiclassification.

Critical infrastructure refers to the key systems, assets, and facilities, whether they are physical or virtual, that are necessary for the overall functioning of a country. As our reliance on technology and networked systems continues to expand, so does the significance of taking precautions to protect critical infrastructure from being compromised by cyberattacks. We need some security schemes to secure the communication happening in the critical infrastructure devices. Therefore, in this paper, we focus on the design of an authentication and key establishment scheme, which is used in the critical infrastructure to secure its data transmissions. A secure authentication and key management mechanism for fog computing-based IoT-driven critical infrastructures (in short, AKM-FCCI) is proposed in the paper. We then provide the network model and threat model of the proposed AKM-FCCI to explain its deployment and organization of devices and servers. The threat model further explains the various threats of this communication environment. In addition, the security analysis of AKM-FCCI is presented to demonstrate that it is secure against the many different kinds of attacks that could be launched against it. The comparisons demonstrate that the performance of AKM-FCCI is superior to that of the other currently used schemes. In addition to that, it offers a high level of security and utility. Therefore, the proposed AKM-FCCI is appropriate for use in protecting critical infrastructure equipment against a wide variety of threats.

Physical layer security is of paramount importance in vehicular cyber-physical systems, as it safeguards not only the privacy of sensitive data exchanged between vehicles and infrastructure but also ensures the integrity and reliability of the entire transportation network. Key generation plays a crucial role in establishing secure communication channels and facilitating the creation of unique cryptographic keys used for encryption, decryption, and authentication purposes. The secret key generation involves deriving secret bits by harnessing the inherent randomness present within the communication channels. The difficulty lies in precisely evaluating the randomness of the channel to achieve unanimous agreement on secure key generation within an unpredictable environment. In this line, we propose a combinatorial approach involving A2C and LSTM to decrease the key disagreement rate. A2C employs policy and value-based strategies to choose quantization levels predicated on the randomness of wireless channels and LSTM includes the partially observable radio channels and improves the environment. Based on our performance evaluation, the proposed A2C-LSTM method substantially accelerates the convergence rate by $\mathbf{5 0 - 6 0 \%}$ and reduces the Key Disagreement Rate (KDR) by $40 \%$.

With the changing lifestyle of people, they are getting more prone to various diseases, some of which might prove to be dangerous and can cause serious health problems. The chances of getting brain tumors are increasing, with new alarming cases being found every day, and the detection and classification for further surgery are very crucial and should be done without due. Therefore, we propose here a Deep Learning (DL) CNN-based brain tumor classification framework that helps in analyzing the image and classifying it in its respective type. In addition, we integrate the blockchain technology to securely store the data, make it easily accessible at the time of need, and prevent it from malware practices. We have used the IPFS protocol in order to make our framework cost-efficient. The results of various DL models are compared and a CNN-based framework is proposed. The CNN-based model demonstrated an exceptional performance by achieving an accuracy of 97.91%.

The energy market has witnessed the drastic penetration of on-road electric vehicles (EVs), which serve as a robust and energy-efficient solution for mitigating the high cost, high energy consumption, and environmental pollution challenges of fossil-fuel vehicles. However, the huge energy demand of EVs needs to be coordinated for charging based on the available charging infrastructure, considering the critical factors of fluctuating prices and distance corresponding to the charging station (CS) and energy demand of the EVs. Therefore, we propose an Artificial Intelligence (AI)-based EV charging allocation scheme by performing the CS selection considering the EVs state of charge (SoC). We have applied the neural network model to predict the EV SoC to check their battery level for scheduling purpose. Further, we have formulated various CS selection scenarios based on the price and distance by assigning priority to the scenarios to enable efficient EV scheduling. Finally, the proposed scheme is evaluated considering various performance metrics such as loss curve, MAE curve, and SoC prediction plot in which adam optimizer yields the best result for the neural network model.

Physical layer security issues have attracted significant attention in wireless networks to protect information leakage from illegitimate eavesdroppers. In this paper, we focus on an intelligent reflecting surface (IRS)-assisted secure non-orthogonal multiple access (NOMA) uplink system. Multiple users intend to transmit sensitive data to an access point (AP) considering the existence of a nearby eavesdropper (Eve). The IRS can be used to enhance the NOMA users’ sum rates while concurrently weakening the Eve’s channel condition, suppressing information leakage to the Eve without resorting to a cooperative jammer or the power injection of artificial noise in the system. The users’ scheduling, the IRS’s passive beamforming, and the AP’s receive beamforming are jointly optimized to maximize the secure rate of the IRS-assisted NOMA system. We develop a hierarchical deep reinforcement learning (DRL) framework to iteratively search for an optimal solution considering the combinatorial nature of the NOMA users’ scheduling and the high-dimensional beamforming design. Firstly, we search for the NOMA users’ scheduling strategy by using the PPO-based DRL algorithm. Given the NOMA scheduling strategy, we then optimize the active and passive beamforming strategies by the alternating optimization (AO) algorithm. The inner optimization helps evaluate the quality of the scheduling strategy and thus guides the outer-PPO algorithm to update a better scheduling strategy. Simulation results demonstrate the superiority of the proposed scheme over existing benchmarks, resulting in significant gains in secure rate.

Autonomous driving technology achieves self-driving capability through extensive training on user driving data. As data regulations tighten, Federated Learning(FL) helps self-driving in the Internet of Vehicles (IoV) comply and evolve without restrictions. However, this method faces severe structural challenges and susceptibility to parameter pollution attacks. This paper presents TreeChainFL, a Byzantine-Fault-Tolerant FL system that incorporates a blockchain-based decentralized trust mechanism and knowledge distillation(KD) for IoV. This decentralized architecture employs Byzantine fault tolerance to curb the growing threat from malicious nodes and uses KD with meta-learning to fend off poisoning attacks. Our experiments show that TreeChainFL outperforms recent advancements, effectively neutralizing over 50% Byzantine node attacks and ensuring robust structural and computational fault tolerance.

The Internet of Vehicles (10V) brings significant economic benefits to countries. However, large-scale smart vehicle production planning remains challenging in the 10V. Currently, heuristic algorithms and solvers commonly used for these problems often lack scalability and fall into local optima. Moreover, security concerns about wireless data transfer arising from multi-factory manufacturing processes are garnering attention. To address these issues, this paper introduces an algorithm, TRL, which is a Transformer-based Reinforcement Learning for vehicle production planning problems. Furthermore, we propose a Transformer-based Federated Reinforcement Learning algorithm, named TFRL, tailored for large-scale manufacturing and secure wireless communication. Experimental results showcase the high performance and security of TFRL. It schedules 1000 orders in about 14 seconds and avoids exchanging plaintext during the interaction. Compared to Non-dominated Sorting Genetic Algorithm II(NSGA-II), the TFRL enhances computational speed by 95.12% and reduces constraint violation scores by 93.18%.

Federated learning (FL) has emerged in sparse low earth orbit (LEO) satellite constellations as a promising architecture for on-board machine learning (ML) model training, aimed at preserving Internet of Things (IoT) data privacy in specialized and sophisticated tasks. However, the user in FL who spends the longest time in a FL round significantly hinders efficiency. Furthermore, intermittent satellite connectivity, rapidly changing network topologies of sparse LEO satellite constellations and a dearth of information including computation capabilities and positions of satellites greatly obstacle the efficient implementation of FL. To address this challenge, we propose a deep reinforcement learning (DRL)-based server selection scheme for FL in sparse LEO satellite constellations. The optimization problem to minimize the overall FL latency is formulated. A Markov decision process (MDP) is subsequently established and the corresponding double Q-learning agent is trained to make sequential FL server selection decisions to figure it out. Simulation results demonstrate that the proposed scheme reduces latency compared to other server selection schemes.

Recently, metaverse-empowered wireless systems have gained significant interest in the research community because of the appealing features of self-sustainability and proactive learning. Self-sustainability allows a system to run with the least amount of assistance from network administrators, whereas proactive learning allows for the development of machine learning models prior to user requests. As a result, the idea of the metaverse in vehicular networks can be used to enable a variety of applications (e.g., infotainment and collision avoidance) for a massive number of autonomous vehicles. However, metaverse-empowered vehicular networks are challenging to implement due to computing (i.e., at autonomous cars and network edges) and communication resource constraints. We present a novel framework for joint sensing, communication, and task offloading for vehicular networks empowered by the metaverse in order to address these issues. We formulate a problem to minimize a cost function that takes into account transmission latency, sensing, and transmission energy. Sensing interval, resource distribution, and task offloading are all optimized for minimizing the cost. We use convex optimization for the sensing problem while a decomposition-relaxation-based approach is used for joint resource allocation and task offloading. Finally, numerical results are provided to support the proposed scheme.

The metaverse, the next digital frontier, demands high-performance models and quick personalization due to the dynamic nature of user tasks despite limited data availability. The frequent user customization is resource-intensive and data-heavy. Meta-learning, especially federated meta-learning (FML) known for its adaptive capabilities, is crucial for addressing the dynamics in metaverse, characterized by user heterogeneity, diverse data structures, and varied tasks. However, the diversity of tasks can compromise global training outcomes due to statistical heterogeneity. Given this, an urgent need arises for smart coalition formation that accounts for these disparities. This paper proposes a game-theoretic framework for managing FML in metaverse services, with meta-learners as workers. A blockchain-based cooperative coalition formation game is introduced, grounded on a reputation metric, the similarity of users, and their incentives. The reputation metric is derived based on our novel reputation system, which takes into account users' historical contributions and potential contributions to current tasks, by exploiting the correlations between past and new tasks. Meanwhile, the incentive mechanism is formulated as an optimization to minimize users energy cost and boost the users contribution for higher federated meta-learning efficacy. Simulations show the framework's resilience against misbehavior and its superiority over other schemes, improving service utility and worker profitability in metaverse meta-learning.

The increasing number of IoT devices in the network brings new challenges to the network carrying capacity of intelligent edge computing, and the complicated network services make the demand for network resources in industrial production scenarios or ordinary network users often exceed the carrying capacity of the edge computing network. To alleviate this problem, this paper proposes an intelligent edge computing architecture that introduces network service identification, extracts and analyses the data characteristics of network traffic, and designs appropriate algorithms to classify network traffic into six different service types. This enables real-time and computing-requiring tasks to be prioritised in the network. Using two machine learning algorithms, KNN and MLP, a model validation is carried out on the constructed dataset, and the results show the effectiveness of the method, with the correct rate of data validation reaching 85%, which is more than 5% higher than the correct rate of direct classification of the specified applications, and the accuracy can be as high as 97% in certain scenarios.

As the Metaverse develops, it is becoming more crucial to prioritize the safety of users, especially regarding the potential risks, such as users experiencing dizziness or making incorrect movements that may lead to falls. With more virtual environments becoming increasingly available and immersive, detecting and preventing falls within the Metaverse is required. Given the constrained resources of wearable sensors, precise fall prediction models are critical to efficiently analyzing data gathered by these devices. Traditional fall detection systems require centralizing data collection, which raises privacy concerns over the collected data. Resource-aware Split Federated Learning (RSFL) enables collaboration among multiple devices within the Metaverse to train a fall detection model, all while preserving individual data privacy. The approach also leverages parallelism in Federated Learning (FL) and Split Learning (SL) by decomposing training tasks between clients and servers. Moreover, we devise an efficient client selection mechanism to ensure timely training and model convergence performance. We implemented our architecture and assessed its performance using a sensory dataset. The evaluation results with the baseline demonstrate our architecture's superiority in terms of convergence time. Our approach mitigates data heterogeneity and privacy concerns, creating secure and efficient fall detection systems for the Metaverse.

The dramatic recent increase of the smart Internet of Everything (IoE) in Industry 4.0 has significantly\nincreased energy consumption, carbon emissions, and global warming. IoE applications in Industry\n4.0 face many challenges, including energy efficiency, heterogeneity, security, interoperability, and\ncentralization. Therefore, Industry 4.0 in Beyond the Sixth-Generation (6G) networks demands moving\nto sustainable, green IoE and identifying efficient and emerging technologies to overcome sustainability\nchallenges. Many advanced technologies and strategies efficiently solve issues by enhancing\nconnectivity, interoperability, security, decentralization, and reliability. Greening IoE is a promising\napproach that focuses on improving energy efficiency, providing a high Quality of Service (QoS), and\nreducing carbon emissions to enhance the quality of life at a low cost. This survey provides a comprehensive\noverview of how advanced technologies can contribute to green IoE in the 6G network of\nIndustry 4.0 applications. This survey provides a comprehensive overview of advanced technologies,\nincluding Blockchain, Digital Twins (DTs), Unmanned Aerial Vehicles (UAVs, a.k.a. drones), and\nMachine Learning (ML), to improve connectivity, QoS, and energy efficiency for green IoE in 6G\nnetworks. We evaluate the capability of each technology in greening IoE in Industry 4.0 applications\nand analyze the challenges and opportunities to make IoE greener using the discussed technologies.

Deep Neural Networks (DNNs) have become the key technique to revolutionize the healthcare sector. However, conducting online remote inference is often impractical due to privacy constraints and latency requirements. To enable local computation, researchers have attempted network pruning with minimal accuracy loss or DNN distribution without affecting the performance. Yet, distributed inference can be inefficient due to the energy overhead and fluctuation of communication channels between participants. On the other hand, given that realistic healthcare systems use pre-trained models, local pruning and retraining relying only on the available scarce data is not possible. Even pre-pruned DNNs are limited in their ability to customize to the local load of data and device dynamics. The online pruning of DNN inferences without retraining is viable; however, it was not considered in the literature as most well-known techniques do not perform well without adjustment. In this paper, we propose a novel pruning strategy using Explainable AI (XAI) to enhance the performance of pruned DNNs without retraining, a necessity due to the scarcity and bias of local healthcare data. We combine distribution and pruning techniques to perform online distributed inference assisted by dynamic pruning when needed for highest accuracy. We use Non-Linear Integer Programming (NLP) to formulate our approach as a trade-off between resources and accuracy, and Reinforcement Learning (RL) to relax the problem and adapt to dynamic requirements. Our pruning criterion shows high performance compared to other reference techniques and ability to assist distribution by reducing resource usage while keeping high accuracy.

With the widespread use of distributed machine learning (DML), many IT companies have established networks dedicated to DML. Different communication architectures of DML have different traffic patterns and different requirements on network performance, which is closely related to network topology . However, traditional network topologies usually pursue general goals and are agnostic to the special communication pattern of the applications. The mismatch between network topology and the applications will directly affect the training performance. Although some studies have analyzed the effect of topology on training performance, the topologies and communication architectures involved are not comprehensive, and it is still not known which topology is appropriate for which communication architecture. This survey investigates typical topologies and analyzes whether they meet the requirements of three commonly used communication architectures (i.e., Parameter Server (PS), Tree and Ring architectures) of DML. Specifically, the topology requirements of each communication architecture and two common topology requirements (i.e., high scalability and fault tolerance) for DML are studied firstly. Next, whether these topologies meet the topology requirements is analyzed. Then, this paper discusses potential technologies and approaches to construct the appropriate scheme for each topology requirement, and then presents DMLNet , a novel network topology that suits the three communication architectures. Finally, several potential directions for future research are outlined.

Internet of Things (IoT) applications are switching from general to precise in different industries, e.g., healthcare, automation, military, maritime, smart cities, transportation, logistics, and many more. In the healthcare domain, these applications had demonstrated an incredible improvement in patient assessment, monitoring, and prescription, etc., with ease of access through the Internet. Despite its benefits, this technology also offers several security challenges for the research community and healthcare stakeholders, because of its wireless communication and open-area deployment. To explore, patient wearable devices and other networking entities follows unstructured communication format to share their accumulated data in the network, which makes them susceptible to manifold security threats. Considering the significance of these applications, data acquisition, processing, storage, and assessment on client and remote sides need a high standard of secure communication infrastructure. Therefore, security of these applications is one of the major obstacles that prevent their widespread use in different healthcare domains. To discuss different security constraints, in this paper, we present a comprehensive survey of the theoretical literature from 2015-to-2023 to highlight the unresolved security problems of this emerging technology. Based on the evaluated literature pros and cons, we determine the security requirements and challenges of Healthcare-IoT (HC-IoT) applications. Following this, we demonstrate future research directions that could be useful for the researchers and industry stakeholders working in this domain. To demonstrate the uniqueness of this work and claim its contribution, we compare our work section-wise with previously published papers to answer the question of reviewers, editors, students, and readers, why this review article is required in the presence of already published review articles.

The integration of Predictive Maintenance (PdM) with the Industrial Internet of Things (IIoT) represents a pivotal shift in equipment management, particularly with the incorporation of Deep Learning (DL) for processing time series data from IIoT devices. This combination offers a sophisticated approach to predictive analysis, harnessing DL’s prowess in analyzing complex patterns in large datasets. However, it also presents notable challenges, including significant security risks associated with centralized organizations and the immense volume of time series data generated by IIoT. To address these issues, our study introduces an innovative decentralized framework thoughtfully segmented into device and edge levels. This framework leverages the strengths of blockchain technology and the Interplanetary File System (IPFS). IPFS effectively manages the large-scale storage needs of time series data for DL applications in a decentralized manner, while blockchain provides a robust foundation for ensuring data security and maintaining consistent transactions. Furthermore, we conducted thorough performance analyses, examining aspects such as accuracy, execution time, and computational cost, which validated the efficacy of our approach. Security considerations were also rigorously evaluated, focusing on potential attacker scenarios, the strengths of a decentralized architecture, and the immutable nature of smart contracts. The results highlight our framework’s exceptional ability to ensure the highest level of security in DL, maintain data integrity, and preserve model accuracy. In conclusion, the seamless integration of DL, PdM, blockchain, and IPFS in our framework marks a significant advancement in contemporary industrial maintenance strategies. It successfully bridges the gap between advanced security needs and the handling of vast quantities of data, positioning our approach at the forefront of modern industrial maintenance solutions.

This article outlines the architecture of autonomous driving and related complementary frameworks from the perspective of human comfort. The technical elements for measuring autonomous vehicle (AV) user comfort and psychoanalysis are listed here. At the same time, this article introduces the technology related to the structure of automatic driving and the reaction time of automatic driving. We also discuss the technical details related to the automatic driving comfort system, the response time of the AV driver, the comfort level of the AV, motion sickness, and related optimization technologies. The function of the sensor is affected by various factors. Since the sensor of automatic driving mainly senses the environment around a vehicle, including “the weather” which introduces the challenges and limitations of second-hand sensors in AVs under different weather conditions. The comfort and safety of autonomous driving are also factors that affect the development of autonomous driving technologies. This article further analyzes the impact of autonomous driving on the user’s physical and psychological states and how the comfort factors of AVs affect the automotive market. Also, part of our focus is on the benefits and shortcomings of autonomous driving. The goal is to present an exhaustive overview of the most relevant technical matters to help researchers and application developers comprehend the different comfort factors and systems of autonomous driving. Finally, we provide detailed automated driving comfort use cases to illustrate the comfort-related issues of autonomous driving. Then, we provide implications and insights for the future of autonomous driving.

In the era of data-driven healthcare, the amalgamation of blockchain and Federated Learning (FL) introduces a paradigm shift towards secure, collaborative, and patient-centric data-sharing. This paper pioneers the exploration of the conceptual framework and technical synergy of FL and blockchain for decentralized data-sharing, aiming to strike a balance between data utility and privacy. FL, a decentralized machine learning paradigm, enables collaborative AI model training across multiple healthcare institutions without sharing raw patient data. Combined with blockchain, a transparent and immutable ledger, it establishes an ecosystem fostering trust, security, and data integrity. The paper elucidates the technical foundations of FL and blockchain, unravelling their roles in reshaping healthcare data-sharing. The paper vividly illustrates the potential impact of this fusion on patient care. The proposed approach preserves patient privacy while granting healthcare providers and researchers access to diversified datasets, ultimately leading to more accurate models and improved diagnoses. The findings underscore the potential acceleration of medical research, improved treatment outcomes, and patient empowerment through data ownership. The synergy of FL and blockchain envisions a healthcare ecosystem that prioritizes individual privacy and propels advancements in medical science.

The metaverse, envisioned as the next digital frontier for avatar-based virtual interaction, involves high-performance models. In this dynamic environment, users’ tasks frequently shift, requiring fast model personalization despite limited data. This evolution consumes extensive resources and requires vast data volumes. To address this, meta-learning emerges as an invaluable tool for metaverse users, with federated meta-learning (FML), offering even more tailored solutions owing to its adaptive capabilities. However, the metaverse is characterized by users heterogeneity with diverse data structures, varied tasks, and uneven sample sizes, potentially undermining global training outcomes due to statistical difference. Given this, an urgent need arises for smart coalition formation that accounts for these disparities. This paper introduces a dual game-theoretic framework for metaverse services involving meta-learners as workers to manage FML. A blockchain-based cooperative coalition formation game is crafted, grounded on a reputation metric, user similarity, and incentives. We also introduce a novel reputation system based on users’ historical contributions and potential contributions to present tasks, leveraging correlations between past and new tasks. Finally, a Stackelberg game-based incentive mechanism is presented to attract reliable workers to participate in meta-learning, minimizing users’ energy costs, increasing payoffs, boosting FML efficacy, and improving metaverse utility. Results show that our dual game framework outperforms best-effort, random, and non-uniform clustering schemes -improving training performance by up to 10%, cutting completion times by as much as 30%, enhancing metaverse utility by more than 25%, and offering up to 5% boost in training efficiency over non-blockchain systems, effectively countering misbehaving users.

Payment channel networks (PCNs) have emerged as a leading solution to address the scalability problem of blockchain-based cryptocurrencies. Within a PCN, two parties can efficiently conduct path-based off-chain transactions using the funds held in payment channels. Intermediaries involved in transaction routing charge a fee for their services. However, since transaction routing leads to highly dynamic and irreversible changes in channel balances, the success of transaction routing is difficult to guarantee, especially in the presence of concurrent transactions. Existing solutions for PCN routing either overlook the routing of concurrent transactions or impose high fees. In this article, we propose a novel routing protocol Deter-Pay for concurrent PCNs, which improves the success rate of the transaction routing and minimizes transaction fees. Our protocol adopts the idea of the deterministic transaction request declaration and reserved balance feedback. By reserving a sufficient balance during the path probing process, we mitigate the risk of transaction failure caused by fluctuations in channel balances during transaction routing. Additionally, our protocol employs the cheapest path first strategy to select paths, thereby minimizing transaction fees. Extensive simulations show that Deter-Pay can significantly increase the successful transaction volume and reduce the transaction fee compared to the comparison algorithms.

With the development of low-cost computer chips and wireless networks, any object or thing can become a component of the Internet of Things (IoT). As a result, these disparate things will gain digital intelligence, enabling them to connect with real-time data and be used for a wide range of valuable and practical IoT applications. However, due to the broadcast nature of wireless communication and the presence of eavesdroppers, the IoT systems pose serious threats to privacy and message integrity, particularly with mobile sensors. Recently, physical-layer security (PLS) has been proposed as a cost-effective solution that mitigates the impact of growing security threats. In this article, we propose a PLS-based hybrid nonorthogonal multiple access (NOMA)/power division multiplexing (PDM) IoT system that provides a high probability of secured transmission, a low computational complexity, and a low-power consumption. 3-D stochastic geometry tools have been used to introduce and evaluate our proposed scheme in a variety of practical scenarios where users are randomly located in a 3-D space. Furthermore, we derive the new scheme’s key agreement ratio (KAR) expression, the probability expressions of using NOMA and PDM modes. Moreover, we derive the associated secrecy outage probability (SOP), and the average ergodic capacity (AEC) expressions for the proposed scheme as well as for the pure NOMA and pure PDM schemes. In addition, we introduce and investigate the security power efficiency (SPE) metric, that serves as a new key agreement optimization metric. Numerical results are performed to validate the obtained analytical expressions and to assess the proposed scheme performances in terms of KAR, SOP, and AEC, when compared to pure NOMA and pure PDM schemes.

With the explosive growth of blockchain platforms and applications, security threats of blockchain also occur frequently. As a decentralized application deployed on the blockchain, smart contracts help the blockchain realize safe and efficient information storage, asset management, and value transfer. Therefore, smart contracts play a vital role in the security of the blockchain. In recent years, security threats against smart contracts have increased, not only causing huge economic losses but also impacting the credit system of the blockchain. Therefore, many researchers have carried out corresponding research on the security threats of smart contracts. Common threat detection methods include formal verification, symbolic execution, fuzzing, etc. Most of these methods are only for known threats, while there is not much work on detecting unknown threats. In order to better deal with unknown threats, we present a review of the typical smart contract security events in recent years, analyze the security threats from contract coding, Ethereum virtual machine, and blockchain characteristics. Further, we compare and summarize the latest unknown threat detection methods. Then, to address the problem that very few unknown threat samples are available, a detection method based on a few-shot learning is proposed.

Due to the outbreak of the new crown epidemic, more companies prefer to use telecommuting for work, which also provides more attack surfaces for APT attacks. After initially gaining access to the intranet, attackers will use server message block (SMB), RDP, and other remote sharing or connection protocols to move horizontally to achieve the purpose of privilege escalation. In this work, we design a multidimensional detection framework to detect lateral movement behavior based on the SMB protocol in the intranet environment. This framework combines active trapping and passive scanning, and uses neural networks to determine the attack samples used by the adversary when moving laterally. We test the effectiveness of the active trapping technology in a simulation environment, and verify through real malware samples that the accuracy of neural network detection can reach about 90%. The experimental results show that our work can effectively detect the lateral movement behavior using the SMB protocol in the intranet environment.

Phishing is a common cybercrime event with great harm. Various phishing attacks have occurred repeatedly and have caused huge economic losses. With the booming development of blockchain and cryptocurrency, the huge amount of money in the field and the immature ecosystem have induced phishing attacks to flood the field in large quantities. Unfortunately, phishing has become the main means of attack in the field, posing a huge security threat to users’ digital assets. The existing methods for detecting phishing websites rely on the quality of uniform resource locator (URL) feature extraction, and the extraction angle is becoming increasingly rigid. Therefore, this article proposes a phishing URL detection model that utilizes feature extension. This method uses the TextRank algorithm to generate a feature extension library and embeds the extracted features into the URL to be detected. After the URL is vectorized, it is input into the two-layer classification network proposed in this article to classify the website. This classifier consists of an upstream task Bert layer and a downstream task convolutional neural network layer. It is possible to simultaneously learn the comprehensive representation information and local feature information of URLs, effectively avoiding overfitting problems and improving the ability to identify phishing websites. Comparative experiments are conducted using a data set of real phishing websites. The experimental results show that this model has higher accuracy and stability compared to other phishing website detection models.

Continuously preventing weak password attacks is one of the most important initiatives to secure IoT and smart contract platforms. Despite their significance as crucial components of passwords, special character segments have been overlooked. This study systematically studies the basic characteristics and semantic patterns of special character segments. We assess the efficacy of special character segment characteristics in cracking trials through assimilation into the latest Probabilistic Context-Free Grammar (PCFGv4) method for password cracking by updating the pre-terminal structure or performing special character segment transformation. Experimental findings demonstrate that a mere 6% transformation rate improves the cracking rate by 3.72% under the optimal assimilation combination. Our investigation reveals that the current password creation policies of mainstream IoT platforms and smart contract wallets overestimate the strength of passwords with special characters. To enhance their passwords, users can employ low-frequency special character semantic strings. For IoT platforms or smart contract wallets, the use of blacklist constructed from special character segment characteristics can effectively mitigate the risk of overestimating the strength of passwords with special characters.

Unmanned aerial vehicles (UAVs) show significant potential in enhancing communication services within the mobile edge computing (MEC) system by taking their advantages on the flexible mobility and reliable line-of-sight links. However, in the scenarios with multiple UAV-MECs (UMs) operating concurrently, potential conflicts in their trajectories need to be mitigated. Thus, the 3-D trajectory needs to be properly designed in a highly reliable manner. Besides, such an infrastructure-free communication paradigm also exposes a potential risk of misuse by malicious parties, which allows them to eavesdrop on private communications, posing a threat to the security and privacy. Therefore, we consider a multi-UAV-assisted MEC communication system, where a UAV maliciously eavesdrops on the data transmission from the user devices (UDs) while a jammer is deployed on the ground to interfere with the eavesdropping channel. In specific, our objective is to minimize the energy consumption and latency while incorporating fairness metrics by optimizing the 3-D trajectories of UMs, transmission power of UDs, and the offloading strategies under the constraints of ensuring communication security and load fairness. Given the complexity of this mixed-integer nonconvex programming problem, we decompose the formulated problem into three subproblems. Specifically, at each time slot, we optimize the transmit power and offloading strategies using theoretical derivation and mathematical analysis, respectively. Additionally, a multiagent deep deterministic policy gradient (MADDPG) algorithm is employed to optimize the trajectories of UMs. Simulation results demonstrate that our proposed joint optimization algorithm successfully minimizes the system energy consumption and delay as compared to benchmarking schemes.

The emergence of the Internet of Things (IoT) has revolutionized people’s daily lives, providing superior quality services in cognitive cities, healthcare, and smart buildings. However, smart buildings use heterogeneous networks. The massive number of interconnected IoT devices increases the possibility of IoT attacks, emphasizing the necessity of secure and privacy-preserving solutions. Federated learning (FL) has recently emerged as a promising machine learning (ML) paradigm for IoT networks to address these concerns. In FL, multiple devices collaborate to learn a global model without sharing their raw data. However, FL still faces privacy and security concerns due to the transmission of sensitive data (i.e., model parameters) over insecure communication channels. These concerns can be addressed using fully homomorphic encryption (FHE), a powerful cryptographic technique that enables computations on encrypted data without requiring them to be decrypted first. In this study, we propose a secure FL approach in IoT-enabled smart cities that combines FHE and FL to provide secure data and maintain privacy in distributed environments. We present four different FL-based FHE approaches in which data are encrypted and transmitted over a secure medium. The proposed approaches achieved high accuracy, recall, precision, and F-scores, in addition to providing strong privacy and security safeguards. Furthermore, the proposed approaches effectively reduced communication overhead and latency compared to the baseline approach. These approaches yielded improvements ranging from 80.15% to 89.98% in minimizing communication overhead. Additionally, one of the approaches achieved a remarkable latency reduction of 70.38%. The implementation of these security models is nontrivial, and the code is publicly available athttps://github.com/Artifitialleap-MBZUAI/Secure-Federated-Learning-with-Fully-Homomorphic-Encryption-for-IoT-Communications.

Due to their potential to accomplish complicated missions more effectively, UAV swarms have attracted a lot of attention lately. UAV swarm offers enhanced intelligence, improved coordination, increased flexibility, survivability, and reconfigurability. It is a multi-disciplinary system that necessitates a tight integration of several sub-systems, including optimal trajectory planning, localization, task coordination, etc. This review covers the important aspects of UAV swarms including swarm formation control, communication, swarm path planning, autonomy, coordination, and security. It additionally explores recent technical advancements in UAV swarm algorithms that have made the development of complex UAV swarm systems possible. This paper also provides insight into ethical aspects and the use cases of UAV swarms in various military, civilian, and entertainment applications. This paper is concluded by highlighting the potential future directions and challenges of UAV swarm technology and the need for more research and development to exploit their potential fully. Overall, this paper presents a comprehensive review of UAV swarm technology, addressing its potential for revolutionizing many fields and supporting advancements in the future.

With the fast development of Internet of Things (IoT), anomaly detection has recently become a common concern for IoT smart applications. The circuit detection mode of all services to detect application anomaly is widely adopted. However, regularly collecting key performance indicators (KPIs) of all services under different clouds is a time-consuming task. Moreover, too many alerts in a short period of time will affect the processing speed of engineers. Recently, some researches of inferring the key service on the call paths can solve the above problems. However, it is still a challenge to locate dynamic and scattered key services accurately. In this article, we propose a service inference-based anomaly detection approach (SIADA), which integrates three sources of logs: 1) call; 2) business; and 3) metric. SIADA leverages the deep graph representation with context-aware multigraph fusion based on a recurrent encoder. This should infer key services and adopt variational autoencoder (VAE) with the flow model to detect multivariate time-series anomalies for key services. We have conducted extensive experiments on the public data set MicroSS. SIADA achieves the best average accuracy of 92% in service inference and the best average F1-score of 0.98 in anomaly detection, which has improved by 12.17% and 6.42% compared with the best SOTA baseline, respectively. Moreover, the total detection time, network transmission, and average alert number are reduced by 42.12%, 81.87%, and 83.5%, respectively.

In contrast to methods relying on a centralized training, emerging Internet of Things (IoT) applications can employ federated learning (FL) to train a variety of models for performance improvement and improved privacy preservation. FL calls for the distributed training of local models at end-devices, which uses a lot of processing power (i.e., CPU cycles/sec). Most end-devices have computing power limitations, such as IoT temperature sensors. One solution for this problem is split FL. However, split FL has its problems, including a single point of failure, issues with fairness, and a poor convergence rate. We provide a novel framework, called hierarchical split FL (HSFL), to overcome these issues. On grouping, our HSFL framework is built. Partial models are constructed within each group at the devices, with the remaining work done at the edge servers. Each group then performs local aggregation at the edge following the computation of local models. End devices are given access to such an edge aggregated model so they can update their models. For each group, a unique edge aggregated HSFL model is produced by this procedure after a set number of rounds. Shared among edge servers, these edge aggregated HSFL models are then aggregated to produce a global model. Additionally, we propose an optimization problem that takes into account the relative local accuracy (RLA) of devices, transmission latency, transmission energy, and edge servers’ compute latency in order to reduce the cost of HSFL. The formulated problem is a mixed-integer nonlinear programming (MINLP) problem and cannot be solved easily. To tackle this challenge, we perform decomposition of the formulated problem to yield subproblems. These subproblems are edge computing resource allocation problem and joint RLA minimization, wireless resource allocation, task offloading, and transmit power allocation subproblem. Due to the convex nature of edge computing, resource allocation is done so utilizing a convex optimizer, as opposed to a block successive upper-bound minimization (BSUM)-based approach for joint RLA minimization, resource allocation, job offloading, and transmit power allocation. Finally, we present the performance evaluation findings for the proposed HSFL scheme.

In the production environment of the Internet of Things (IoT), sensors of various qualities generate a large amount of multivariate time series (MTS) data. The long-term prediction of time series data generated by various IoT devices provides longer foresight and helps execute necessary resource scheduling or fault alarms in advance, thus improving the efficiency of system operation and ensuring system security. In recent years, deep learning models like Transformers have achieved advanced performance in multivariate long-term time series forecasting (MLTSF) tasks. However, many previous research attempts either overlooked the interseries dependencies or ignored the need to model the strict temporal order of MTS data. In this article, we introduce GTformer, a graph-based temporal-order-aware transformer model. We propose an adaptive graph learning method specifically designed for MTS data to capture both uni-directional and bi-directional relations. In addition, we generate positional encoding in a sequential way to emphasize the strict temporal order of time series. By adopting these two components, our model can have a better understanding of the interseries and intraseries dependencies of MTS data. We conducted extensive experiments on eight real-world data sets, and the results show that our model achieves better predictions compared with state-of-the-art methods.

The increasing demand for intelligent management in modern power systems has emphasized the importance of smart grids, which facilitate real-time analysis and management through data aggregation. Fog computing provides efficient data processing and low-latency transmission for data aggregation. However, fog-assisted smart grids still face significant challenges, including privacy leakage, calculation limitations, and system stability issues. In response to these obstacles, we propose a robust and privacy-enhanced multidimensional data aggregation (RPMDA) scheme. Specifically, the Chinese Remainder Theorem is used to improve the efficiency of processing multidimensional data, combined with an innovative double-masking method to cope with secure data aggregation. For the purpose of reliable authentication, a conditional anonymous certificateless signature algorithm is designed in RPMDA, where the pseudonym generation mechanism ensures the conditional anonymity of smart meters. Besides, our scheme incorporates robustness, ensuring that the aggregated results remain unaffected even if smart meters malfunction. Compared to the existing solutions, RPMDA shows superior performance while meeting security requirements.

Federated learning (FL) emerges as an attractive collaborative machine learning framework that enables training of models across decentralized devices by merely exposing model parameters. However, malicious attackers can still hijack communicated parameters to expose clients’ raw samples resulting in privacy leakage. To defend against such attacks, differentially private FL (DPFL) is devised, which incurs negligible computation overhead in protecting privacy by adding noises. Nevertheless, the low model utility and communication efficiency makes DPFL hard to be deployed in the real environment. To overcome these deficiencies, we propose a novel DPFL algorithm called FedDP-SA (namely, federated learning with differential privacy by splitting Local data sets and averaging parameters). Specifically, FedDP-SA splits a local data set into multiple subsets for parameter updating. Then, parameters averaged over all subsets plus differential privacy (DP) noises are returned to the parameter server. FedDP-SA offers dual benefits: 1) enhancing model accuracy by efficiently lowering sensitivity, thereby reducing noise to ensure DP and 2) improving communication efficiency by communicating model parameters with a lower frequency. These advantages are validated through sensitivity analysis and convergence rate analysis. Finally, we conduct comprehensive experiments to verify the performance of FedDP-SA compared with other state-of-the-art baseline algorithms.

Due to advantages in security and privacy, blockchain is considered a key enabling technology to support 6G communications. Practical Byzantine fault tolerance (PBFT) and RAFT are seen as the most applicable consensus mechanisms (CMs) in blockchain-enabled wireless networks. However, previous studies on PBFT and RAFT rarely consider the channel performance of the physical layer, such as path loss and channel fading, resulting in research results that are far from real networks. Additionally, 6G communications will widely deploy high-frequency signals, such as terahertz (THz) and millimeter wave (mmWave), while performances of PBFT and RAFT are still unknown when these signals are transmitted in wireless PBFT or RAFT networks. Therefore, it is urgent to study the performance of nonideal wireless PBFT and RAFT networks with THz and mmWave signals, to better make PBFT and RAFT play a role in the 6G era. In this article, we study and compare the performance of THz and mmWave signals in nonideal wireless PBFT and RAFT networks, considering rayleigh fading (RF) and close-in free space (FS) reference distance path loss. Performance is evaluated by five metrics: 1) consensus success rate; 2) latency; 3) throughput; 4) reliability gain; and 5) energy consumption. Meanwhile, we find and derive that there is a maximum distance between two nodes that can make CMs inevitably successful, and it is named the active distance of CMs. The results show that the two consensus networks have a lower consensus success rate, higher delay, lower throughput, and lower energy consumption in mmWave than THz. Compared with the wireless RAFT consensus, wireless PBFT consensus has a lower consensus success rate, higher delay, lower throughput, and higher energy consumption. The research results provide important references for the future transmission of THz and mmWave signals in PBFT and RAFT networks.

The rapid increase in the adoption of Electric Vehicles (EVs) and the installation of Charging Stations (CSs) are key components for bidirectional energy transfer between EVs and CSs. However, the traditional techniques of energy trading have issues of trust, scalability, traceability, provenance, and authenticity among energy prosumers. To address these challenges, particularly information imbalances between energy buyers and sellers, we propose TokenGreen, a novel framework that leverages blockchain and Non Fungible Tokens (NFTs) to enable participants to have ownership of energy assets through investments in distributed energy generation, distribution, and clean energy infrastructure, leading to trust and transparency management among the participants. The proposed framework uses Ethereum Virtual Machine (EVM), ERC-721 NFT, Inter Planatery File System (IPFS), and Solidity smart contracts to develop an NFT based energy marketplace. Various smart contracts, contract events, functions, algorithms, have been designed and integrated into the energy marketplace to facilitate the minting, creation, purchase, and resale of NFT tokens, including energy trading. To assess the performance of the proposal, experiments are performed using tools such as Geth, Hyperledger Caliper, and the Ethereum SDK. The obtained results indicate that the average maximum latency for CreateToken reached 12.39s, while BuyToken and ResellToken reached 11.02s. Additionally, the average minimum latency for CreateToken, BuyToken, and ResellToken reached 10.46s, 10.03s, and 9.14s, respectively. On average, memory consumption ranged from 640 to 775 MB, while CPU usage averaged between 30% and 55% for each function. The performance analysis indicate CreateToken has low throughput, while BuyToken shows higher, and ResellToken exhibits the highest throughput due to fewer write operations. TokenGreen demonstrates superior performance compared to the existing state-of-the-art, considering the mentioned parameters.

Historical patients’ medical data has an important impact on the healthcare industry for providing the best care to patients through intelligent health diagnosis and prediction of diseases. The existing intelligent health diagnosis systems collect data from medical institutions or laboratories and then use machine learning algorithms to predict diseases. But, in most cases, the medical institutions have incomplete medical data of the patients since a patient may consult different specialists (from various hospitals) during the treatment process. To overcome this problem, we build a smart and secure federated learning framework for intelligent health diagnosis with a blockchain-based incentive mechanism and nonfungible tokens (NFTs)-based marketplace. We make use of NFTs to develop clear demarkations on the ownership and accessibility of the data of patients. We create an NFT marketplace that manages access to the historical medical data of patients. A comprehensive incentive mechanism based on several factors, including the quality and relevance of the data, the frequency, regularity of data uploading, etc., is incorporated to encourage and penalize the patients based on their contributions to the global model. We used the Polyak-averaging technique for aggregating local models to form a global model. The extensive analysis shows that the proposed model achieves comparable performance with the centralized machine learning models while affording better security and access to better data. The results also show the efficacy of the proposed blockchain-based incentive mechanism.

Mobile edge computing (MEC) emerges as a promising paradigm that aims to extend the computing capabilities of mobile devices. This enhancement is achieved by offloading heavy computational tasks to nearby edge servers, where relevant services, such as programs, databases, and libraries, are stored to support the execution of these tasks. Given the restricted storage capacity and computing resources of edge servers, it is crucial to carefully consider which tasks to offload and which services to cache. However, the highly dynamic system and closely coupled decisions between offloading and caching pose considerable challenges to developing an effective strategy. In this article, we investigate both computation offloading and service caching in an end-edge–cloud collaborative system under time-varying wireless channels and task requests. We formulate an optimization problem aimed at minimizing the system cost, including time and energy consumption, and model it further as a Markov decision process (MDP). To address this, we propose a deep reinforcement learning (DRL)-based approach with dynamic action masking to concurrently optimize offloading and caching decisions. This approach renormalizes the valid action probability distribution at every step, enabling the agent to explore within the space of valid action combinations and thereby learn the optimal policy. Extensive experimental results demonstrate that the proposed approach greatly reduces the system cost compared to other representative benchmark schemes.

Satellite communication networks with the characteristics of wide coverage, high deployment flexibility, and seamless communication services can provide communication services to users who don’t communicate with ground networks but directly communicate with satellites. In response to the increasing demand for user services, this paper proposes a collaborative computing offloading scheme for satellite edge computing networks with a four-layer architecture. By utilizing collaborative computing between ground users and three layers of satellites (low-orbit satellites, edge, and cloud data centers), the service quality for ground users is improved. Considering the mobility of vehicles and satellite nodes, the frequent changes in link states further complicate the design and implementation of such systems, leading to increased latency and energy consumption. This paper proposes to optimize the computation offloading decision while satisfying the constraint of satellite computing capabilities, aiming to improve the success rate of tasks and minimize the overall cost of the system. However, with the increase in the number of ground users and satellites, the formulated problem becomes a mixed-integer nonlinear programming (MINLP) problem, which is difficult to solve with general optimization algorithms. To address this issue, this paper proposes a dynamic distributed learning offloading (DDLDO) algorithm based on distributed deep learning. The algorithm utilizes multiple parallel deep neural networks (DNN) to dynamically learn computation offloading strategies. Simulation results demonstrate that the algorithm outperforms other benchmark algorithms in terms of latency, energy consumption, and successful execution efficiency.

With the development of the Internet of Vehicles (IoV), multiaccess edge computing (MEC) technology places computing resources closer to users at edge nodes, enabling faster, more reliable, and secure computing services. In the MEC-enabled IoV networks, task offloading scheduling, as an effective method to alleviate the computational burden on vehicles, is gaining increasing attention. However, with the intelligent and networked development of vehicles, the complex data dependency between in-vehicle tasks brings challenges to offloading scheduling. In contrast to many existing methods that solely address individual tasks, there is a growing need to tackle interrelated tasks within the IoV framework. This includes tasks like processing vehicle sensor data, gathering and analyzing road condition information, facilitating collaborative decision making among vehicles, and optimizing traffic signal systems. Our objective is to address the broader challenge of offloading dependent tasks, as this closely aligns with real-world scenes and requirements. In this article, we propose a priority-based task scheduling algorithm (PBTSA) to minimize processing delay when the tasks are interdependent. PBTSA proposes a method that can better measure the data transmission and calculation delay of the IoV networks. We first model dependent tasks as a directed acyclic graph (DAG) and then use the reverse breadth-first search (RBFS) algorithm to generate the priority of each subtask, and finally according to the priority with low complexity to offload subtasks greedily to minimize task processing delay. We compare the PBTSA with the other two existing algorithms through simulations. The results show that the PBTSA can effectively reduce the task processing delay and can reach close to 10%.

As the demand for global Internet connectivity continues to grow, the satellite-terrestrial integrated networks (STINs) have become more and more crucial for expanding the service coverage and enhancing the network performance. However, the task offloading problem in STINs faces many significant challenges, such as high processing latency and energy consumption. The current intelligent offloading strategies often rely on the real-time interactions with the environments which not only consume valuable satellite resources but also cause irreversible damage to the satellite equipment due to some operational errors. To address these issues, in this article, we propose an offline deep reinforcement learning (offline DRL) approach to learn and optimize the task offloading decisions by leveraging the stored historical decision data and employing the soft actor-critic (SAC) algorithm specifically. Experimental results show that the proposed strategy outperforms most of the existing methods in terms of latency and energy consumption and effectively reduces the direct interactions with STINs.

Internet of Vehicles (IoV) is an emerging paradigm with significant potential to improve traffic efficiency and driving safety. Here, we focus on the design of a novel visible light communication (VLC)-assisted scheme to enable driving safety-related Internet of Vehicles (IoV) services that require ultrareliable and low-latency communications (URLLC). Specifically, the Vehicle-to-Vehicle (V2V) communication mode is adopted to satisfy the ultralow latency requirement of URLLC in roadside infrastructure-less IoV systems. In the outdoor V2V- VLC scenarios, the quality of the received optical signal is degraded by path loss, atmospheric turbulence and additive noise. In addition, the short-packet feature of URLLC introduces inevitable data decoding errors and imperfect channel state information (CSI). With this background, we aim to investigate the reliability performance of URLLC in outdoor V2V- VLC systems, which is described by the average packet loss probability under given user-plane transmission latency. First, we consider the ideal case of a perfect CSI at the receiver, and derive an analytical expression of average packet loss probability. Further, a closed-form approximation is provided to simplify the numerical calculation. Next, we extend the theoretical analysis to a practical V2V- VLC system with imperfect CSI at the receiver. Through numerical results, we validate the accuracy of our designed theoretical framework and propose ideas to enable driving safety-related IoV services in outdoor V2V- VLC systems.

As the number of devices increases dramatically in the Internet of Things (IoT), features of dense deployment of massive devices generate mutual interference in communication overlapping areas, which will impose an imperative challenge on spectrum resource allocation. To handle this challenge, this article proposes a mutual interference-aware throughput enhancement scheme. For the mutual interference among multiple IoT devices, this scheme first builds an interference hypergraph model to quantify the impact of the mutual interference for each device. According to the main goal of the spectrum resource allocation, this article formulates a graph reinforcement learning (GRL) framework, whose action space is multidimensional discrete, and the reward function is designed to enhance the throughput and mitigate the impact of interference. Then, a graph convolutional network-double dueling deep Q-network-based spectrum resource allocation algorithm is developed upon the proposed GRL framework to extract the mutual interference information from the hypergraph model, and then achieve a dynamic resource allocation for massive IoT. Simulation results prove that the proposed GRL algorithm effectively improves the network throughput compared to the comparison algorithms.

In unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) networks, the communication ranges between densely deployed IoT devices overlap, resulting in wireless resource conflicts between them. Hence, achieving conflict-free resource allocation is a challenging issue that must be urgently addressed for UAV-enabled IoT networks. To tackle this issue, a hypergraph is used to quantify conflicts, and a federated reinforcement learning (RL)-based resource allocation framework is proposed. Specifically, a conflict graph model is developed for UAV-enabled IoT networks with dense deployments. The model is then converted into a conflict hypergraph model using hypergraph and faction theory. Consequently, the conflict avoidance problem of resource allocation can be reformulated as a hypergraph node coloring problem. The problem is formulated as a Markov decision process, which is solved using a deep RL-based approach. Additionally, to distribute the computational workload across the network and alleviate the burden on the central server, we propose the FedAvg dueling double deep Q-network (FedAvg-D3QN). The proposed FedAvg-D3QN is verified through simulation to have advantages in resource reuse rate and throughput compared to baseline approaches.

In the distributed Cognitive Industrial Internet of Things (CIIoT), since industrial devices may self-organize to determine their connection and dispersion and the network may be distributed with no infrastructure, network management with self-organizing characteristics is still an unsolved and a difficult problem. To overcome this challenge, this paper proposes a hierarchical network management strategy for distributed CIIoT with imperfect channel state information (CSI) considering services with different transmission delay requirements. First, we build a two-layer architecture that supports collaborative spectrum sensing and multi-node spectrum sharing in a distributed CIIoT. Then, to improve the network management efficiency and reduce the number of backbone nodes (BNs) while achieving spectrum sharing for massively distributed nodes, we establish a minimum backbone node set mathematical model and design the simplest backbone network optimization algorithm (SBNOA). For the problem of spectrum sharing with imperfect CSI while considering the delay-sensitive industrial services, we establish a time delay model that incorporates delay-sensitive and delay-tolerant services. Based on the model, we establish a robust optimization model with multiple conditional aim-listed probability constraints that consider imperfect CSI and transform the stochastic optimization problem into a convex optimization by using the quadratic transformation method and Gaussian Q function to solve it. Simulation results show that the proposed algorithm has good performance in a distributed CIIoT.

Load-balancing and path selection optimization are efficient solutions to network optimization problems. Since the emergence of software-defined network (SDN), more research in these directions appears because SDN provides a better environment for these topics. However, some current load-balancing optimization methods take a conventional approach. Moreover, we only find a few works attempting to combine load-balancing and path selection optimization to improve the network performance. To address these problems, in this article, we propose an integrated optimization approach (ILBPS) which combines adaptive load-balancing and heuristic path selection. ILBPS first uses singular spectrum analysis (SSA) to extract information from network traffic data and divides the data into payload data and noise data. Then, we use a deep learning model (GCBAC model) to predict the payload data and the convolutional neural network (CNN) model to predict the noise data. After combining the predicted results and calculating the weight of the links, we design a heuristic path selection approach based on the artificial bee colony (ABC) algorithm to compute the optimal path. We refer to this method as ABC-SP. Finally, these optimal paths are sent to the data plane. To test the effectiveness of our approach, we construct an experimental environment based on the GÉANT network and SDN. We compare our approach with the existing approaches. Experimental results show that the proposed ILBPS outperforms the existing approaches in terms of throughput, jitter, and load-balancing factor.

Artificial intelligence enhanced Internet of unmanned aerial vehicles (UAVs) is a promising network to achieve the complicated vehicular tasks and construct intelligent communication networks. One of the critical tasks is to guarantee a secure network access while achieving trade-off between accuracy and latency through lightweight deployment on resource-limited and hardware-constrained UAVs. To address this issue, a novel noise-tolerant radio frequency fingerprinting (NT-RFF) based on tiny machine learning (TinyML) scheme is proposed, which amalgamates contrastive learning and data augmentation, aiming to improve the generalization ability of unauthorized device identification (UDI). Particularly, we first exploit the augmentation technique to enhance the legitimate training datasets under the circumstance of varying signal-to-noise ratios, facilitating an enhanced and diversified datasets. Second, a synthesis of contrastive learning and supervised learning is employed to attain comprehensive global learning. We design a new contrastive loss criteria to capture relevant information from the samples collected over the air. Besides, we design a categorical cross-entropy loss criteria by which supervisory information can be leveraged from associated labels. Finally, quantification is utilized to enhance model efficiency and achieve an optimal balance between accuracy and latency within computing and energy resource-limited UAVs. Experimental results demonstrate that the proposed tiny NT-RFF which only contains about 25-30% quantitative parameters can maintain excellent performance and improve the UDI accuracy greatly compared with the traditional machine learning-based RFF schemes. Moreover, the remarkable results showcase that our proposed framework attains a substantial increase in identification accuracy compared to the DACL-RFF and DASL-RFF methods, exhibiting improvements of 14.16% and 5.17%, respectively.

Future mobile networks should provide on-demand services for various industries and applications with the stringent guarantees of Quality of Experience (QoE), which highly challenge the flexibility of network management. However, the diverse requirements of QoE and the management of heterogeneous networks create significant pressure toward communication service providers (CSPs). In the sixth-generation mobile networks, the CSPs should guarantee resilient performance for the communication service consumers with less human involvement. In this work, we turn to Intent-driven network and on-demand slice management, and to decrease the complexity and cost in full life cycle slice management, we first present an intent-driven closedloop (CL) control and management framework that automates the deployment of network slices and manages resources intelligently based on the extended CL architecture. And then, we explore and exploit the deep reinforcement learning algorithm to address the problem of resource allocation, which is formulated as a Markov decision process. Finally, we demonstrate the feasibility of the proposed framework by deploying the open radio access network (RAN) infrastructure in the OpenAirInterface platform and realizing the CL control and management with a near real-time RAN intelligent controller. The emulation results demonstrate the effectiveness of slicing performance, measured in terms of delay and rate.

To enable resource-constrained wireless devices (WDs) to process the computation-intensive and latency-sensitive computation tasks, the wireless powered mobile edge computing (WP-MEC) network has been proposed as a promising approach. Incorporating mobile cloud computing (CC) in the WP-MEC network, we investigate the wireless powered mobile edge-CC (WP-MECC) network, where the WDs first harvest energy from a hybrid access point (HAP), and then consume the harvested energy to compute the tasks locally, offload them to the HAP for computation, or offload them to the cloud server (CS) via the relaying of the HAP. To pursue fairness among the WDs, we minimize the maximum task completion time (TCT) of WDs by jointly optimizing the time resources, computing mode selection, and computation resources. We prove the minimization problem is NP-hard. To tackle the problem, we decompose it into the subproblem and top problem, and propose an alternate optimization-based resource allocation and the computing mode selection (ARACM) algorithm with low computational complexity, which achieves a comparable performance with the exhaustive search method in terms of the minimal maximum TCT of WDs. Moreover, we propose a deep reinforcement learning (DRL)-based resource allocation and the computing mode selection (DRACM) algorithm with less execution latency than the ARACM algorithm. Numerical results show that the two proposed algorithms achieve satisfactory performance in terms of the minimal maximum TCT of WDs and execution latency.

Although Deep Neural Networks (DNN) have become the backbone technology of several Internet of Things (IoT) applications, their execution in resource-constrained devices remains challenging. To cater for these challenges, collaborative deep inference conducted by IoT devices was introduced. However, the prevalence of DNN computation suffers from severe privacy problems, e.g. data-reverse and model leakage. Particularly, malicious participants can accurately recover the received data to access sensitive information. Furthermore, the system is composed of heterogeneous data-sources represented by different DNN models that wish to execute classifications without exposing their data and models. Though, relaying the trained models to a centralized unit managing the collaboration leads to major risks because some features can be revealed through these models, in addition to dependency and scalability problems. In this paper, we present an approach that targets the privacy of collaborative inference via controlling the amount of data assigned to different participants, to prevent them from reversing attempts. Moreover, each independent data-source requesting inference will be responsible to manage the distribution of its DNN locally. In this context, different sources are required to compete over the pervasive resources while cooperating to maintain privacy welfare. We formulate this methodology, as an integer programming problem, where we establish a trade-off between the latency of co-inference and the privacy required by heterogeneous entities. A distributed solution scheme is also developed based on the Lagrangian dual problem. Next, to relax the optimization, we shape our approach as a cooperative and competitive Multi-Agent Reinforcement Learning (MARL) that supports heterogeneous/independent agents. Our comprehensive simulations demonstrated that our method yields results on par with those of a single RL agent in terms of action performance, while maintaining the privacy of individual agents’ information. Additionally, it surpasses the Independent Q-Learning (IQL) approach, where agents operate autonomously, in safeguarding inference privacy.

Spatial crowdsourcing (SC) has become a fundamental and emerging technology in Metaverse, facilitating the creation of immersive experiences through location-based services. In these systems, a central SC server leverages SC workers who physically travel to task locations to gather spatiotemporal environment data. However, conventional SC systems face two significant challenges: (1) the SC server, functioning as a centralized authority, can sometimes be unreliable, either due to intentional or unintentional misconduct, (2) to ensure efficient task assignment and validation, the location privacy of tasks and workers is openly accessible. In this study, we formally define location privacy preserved proof generation and verification problem (LP-PGVP) within an SC task matching scenario, with the aim to the above two challenges. Our proposed solution is a blockchain-based SC system (BlockSC), which provides a decentralized platform for task requesters and workers in the Metaverse context through calling smart contracts. We also introduce a ciphertext-based task matching scheme where task location access is granted only to eligible workers executing a task, benefiting from the design of geographic coordinate transformation and bilinear mapping methodology. To further demonstrate the task matching scheme’s operation and impact, we present an easy-to-understand case study. Our evaluation findings confirm that the proposed system effectively maintains location privacy for both SC workers and task requesters, without a considerable sacrifice in task matching efficiency.

This paper proposes an active aerial reconfigurable intelligent surface (ARIS) assisted secure communication framework by integrating sensing and positioning against a mobile eavesdropper. In the proposed scheme, the base station (BS) beamforms the private information to the legitimate user and jams the eavesdropper with artificial noise (AN), while reconfiguring the phases and amplitudes of the passive signal by the active ARIS for promoting secure communications. To acquire the channel state information of the time-vary wiretap channel, the BS tracks the position of the eavesdropper by exploiting the reflected AN. Based on the tracked position of the eavesdropper in the previous time slot, we propose a secure communication scheme that aims to maximize the secrecy rate in the current time slot. This scheme is assisted by the ARIS through jointly optimizing the passive beamforming of the privacy information and AN, the reflection matrix of the ARIS, and the position of the ARIS. In the case of this non-convex quandary with highly coupled variables, we opt to disassemble it into three constituent subproblems and design an alternating optimization framework, where the optimal power beamforming at the BS is derived using a successive convex approximation method and semi-positive definite relaxation technique, the reconfigurable coefficient of the ARIS is optimized using the majorization-minimization algorithm, and the optimal position of the ARIS using the three-dimensional network is obtained by the deep deterministic policy gradient algorithm. Simulation results demonstrate the superior performance of the proposed scheme in the context of the secrecy rate when compared with benchmark schemes. By adopting the active beamforming and positioning technique, the secrecy rate can be increased by 38.3% and 10.8%, respectively.

In this paper, the energy consumption (EC) optimization of an aerial high altitude platform station (HAPS) aided mobile edge computing (MEC) network with non-orthogonal multiple access (NOMA) in the presence of imperfect successive interference cancellation is studied. Specifically, joint design schemes of the resource allocation (RA) and the two-dimensional (2D) horizontal position are proposed to minimize the sum EC subject to the different constraint conditions. In particular, we jointly optimize the receive beamforming (BF), the power allocation (PA), HAPS position, the local computation resource, the computation task offload coefficient, and the computation resource allocated for each user via the block coordinate descent method. Namely, given the other optimization parameters, we first optimize a 2D position of HAPS. Then, given the 2D position, by introducing the auxiliary variables, a joint design of BF, PA, offload coefficient and computation resource is solved by an efficient iteration algorithm based on the successive convex approximation method. Additionally, a suboptimal joint design scheme is also developed to lower the complexity. Simulation results show that the proposed two design schemes of the joint RA and position are effective in reducing the EC, and they have a lower EC when compared to benchmark schemes.

Blockchain data mining has the potential to reveal the operational status and behavioral patterns of anonymous participants in blockchain systems, thus providing valuable insights into system operation and participant behavior. However, traditional blockchain analysis methods suffer from the problems of being unable to handle the data due to its large volume and complex structure. With powerful computing and analysis capabilities, graph learning can solve the current problems through handling each node's features and linkage relationships separately and exploring the implicit properties of data from a graph perspective. This paper systematically reviews the blockchain data mining tasks based on graph learning approaches. First, we investigate the blockchain data acquisition method, integrate the currently available data analysis tools, and divide the sampling method into rule-based and cluster-based techniques. Second, we classify the graph construction into transaction-based blockchain and account-based methods, and comprehensively analyze the existing blockchain feature extraction methods. Third, we compare the existing graph learning algorithms on blockchain and classify them into traditional machine learning-based, graph representation-based, and graph deep learning-based methods. Finally, we propose future research directions and open issues which are promising to address.

Wireless powered mobile edge computing (WP-MEC), which combines mobile edge computing (MEC) and wireless power transfer (WPT), is a promising paradigm for coping with the computing power and energy constraints of wireless devices. However, how to realize the online optimal offloading decision and resource allocation in the WP-MEC system is very challenging. This paper studies the system computation completion time (SCCT) minimization problems for WP-MEC networks using non-orthogonal multiple access (NOMA) communication under binary and partial offloading modes. Due to the complexity of the optimization problems and the time-varying nature of the channel state information, we decouple the original problems into a top-problem of optimizing WPT duration and a sub-problem of optimizing resource allocation, and then propose a convolutional deep reinforcement learning online (CDRO) algorithm. For the top-problem, a deep reinforcement learning framework is used to obtain the near-optimal WPT duration, and an incremental exploration policy is designed to balance the exploration accuracy and exploration range to improve the convergence performance of the CDRO algorithm. For the sub-problems, we propose their corresponding low-complexity algorithms based on in-depth analysis and derivation of the optimal offloading decision’s properties. Finally, numerical results show that the proposed CDRO algorithm achieves near-optimal SCCT with low computational complexity, enabling online decision-making in time-varying channel environments.

Artificial intelligence (AI)-based cybersecurity services offer significant promise in many scenarios, including malware detection, content supervision, and so on. Meanwhile, many commercial and government applications have raised the need for intellectual property protection of using deep neural network (DNN). Existing studies (e.g., watermarking techniques) on intellectual property protection only aim at inserting secret information into DNNs, allowing producers to detect whether the given DNN infringes on their own copyrights. However, since the availability protection of learning models is rarely considered, the piracy model can still work with high accuracy. In this paper, a novel model locking (M-LOCK) scheme for the DNN is proposed to enhance its availability protection, where the DNN produces poor accuracy if a specific token is absent, while it maps only the tokenized inputs into correct predictions. The proposed scheme performs the verification process during the DNN inference operation, actively protecting models' intellectual property copyright at each query. Specifically, to train the token-sensitive decision-making boundaries of DNNs, a data poisoning-based model manipulation (DPMM) method is also proposed, which minimizes the correlation between the dummy outputs and correct predictions. Extensive experiments demonstrate the proposed scheme could achieve high reliability and effectiveness across various benchmark datasets as well as typical model protection methods.

In vehicular networks, some edge servers may not function properly due to the time-varying load condition and the uneven computing resource distribution, resulting in a low quality of caching services. To overcome this challenge, we develop a Vehicular dew computing (VDC) architecture for the first time by combining dew computing with vehicular networks, which can achieve wireless communication between vehicles in a resource-constrained environment. Consequently, it is crucial to develop an adaptive caching scheme that empowers vehicles to form efficient cooperation in VDC. In this paper, we propose an intelligent caching scheme based on VDC architecture, which includes two parts. First, to meet the dynamic nature of VDC, a spatiotemporal vehicle clustering algorithm is proposed to establish adaptive cooperation to assist content caching for vehicles. Second, the multi-armed bandit algorithm is employed to select suitable content for caching in vehicles based on real-time file popularity, and a model is established to dynamically update each vehicle’s request preferences. Extensive experiments are conducted to demonstrate that the proposed scheme has excellent performance in terms of cluster head stability and cache hit rate.

Industrial Control System (ICS) depends on the underlying Programmable Logical Controllers (PLCs) to run. As such, the security of the internal control logic of the PLCs is the top concern of ICS. Reversing analysis and forensic work against PLC require extracting control logic from the control application running inside PLC, which is still an unresolved problem. To address the challenge, we propose a PLC decompile framework named CLEVER, which can analyze the control application and extract the control logic. First, we propose a simulation execution based code extraction method, which is utilized to filter the control logic related data. Then, to normalize the control application decompile process, an intermediate representation (IR) is designed, which can simplify the analysis process and enhance the extensibility of CLEVER. Finally, a heuristic data flow analysis algorithm is proposed to find variable dependency, and a sequential parsing method is utilized to reconstruct the source code from the control application. To evaluate our work, real world PLC hardware and programming software are used for the experiment. We use 22 real-world, 58 hand-written, and 150 auto-generated control applications to demonstrate the usability, correctness, and operational efficiency of CLEVER.

With the increasing communications between the In-Vehicle Networks (IVNs) and external networks, security has become a stringent problem. In addition, the controller area network bus in IVN lacks security mechanisms by design, which is vulnerable to various attacks. Thus, it is important to detect IVN anomalies for complete vehicular security. However, current studies are constrained by either requiring labeled data or failing to accurately detect message-level anomalies without labeled data. In addition, the concept drift of existing methods has become a challenge over time. To address these problems, this paper proposes an IVN anomaly detection method based on Self-supervised Learning (IVNSL), which is capable of detecting message-level anomalies without labels. The essential idea of IVNSL is to make the message prediction model learn the distribution of normal messages in sequences using message sequences with noise. Furthermore, to accurately detect anomalies, a Message Prediction Model based on Hierarchical transformers (MPMHit) is proposed, which captures the spatial features of the message and the dependencies between messages. Meanwhile, to solve the concept drift over time, this paper proposes an online update mechanism for MPMHit based on vehicle-cloud collaboration. We conduct an extensive experimental evaluation on the car hacking dataset, resulting to an F1-score average and average false positive rates of IVNSL being 2.282% higher and 1.595% lower than the best baseline method. The average detection speed of each message is as fast as 0.1075 ms.

Mobile edge computing (MEC) offers promising solutions for various delay-sensitive vehicular applications by providing high-speed computing services for a large number of user vehicles simultaneously. In this paper, we investigate non-orthogonal multiple access (NOMA) assisted secure offloading for vehicular edge computing (VEC) networks in the presence of multiple malicious eavesdropper vehicles. To secure the wireless offloading from the user vehicles to the MEC server at the base station, the physical layer security (PLS) technology is leveraged, where a group of jammer vehicles is scheduled to form a NOMA cluster with each user vehicle for providing jamming signals to the eavesdropper vehicles while not interfering with the legitimate offloading of the user vehicle. We formulate a joint optimization of the transmit power, the computation resource allocation and the selection of jammer vehicles in each NOMA cluster, with the objective of minimizing the system energy consumption while subjecting to the computation delay constraint. Due to the dynamic characteristics of the wireless fading channel and the high mobility of the vehicles, the joint optimization is formulated as a Markov decision process (MDP). Therefore, we propose an asynchronous advantage actor-critic (A3C) learning algorithm-based energy-efficiency secure offloading (EESO) scheme to solve the MDP problem. Simulation results demonstrate that the agent adopting the A3C-based EESO scheme can rapidly adapt to the highly dynamic VEC networks and improve the system energy efficiency on the premise of ensuring offloading information security and low computation delay.

The development of the Internet of Vehicles (IoVs) has attracted much attention due to the increasing number of connected cars. IoV refers to the interconnection of vehicles with other devices through the internet to enable information sharing and interaction. The advent of 5G mobile communication technologies has provided high-speed, low-latency, and high-reliability communication services, which have gone a long way in solving the communication problems associated with IoV. Additionally, the Multi-Access Edge Computing (MEC) technology has placed computing resources on edge nodes closer to the users, thus enabling faster, more reliable, and more secure computing services to meet the vehicles’ computing resource requirements. However, task offloading and resource allocation issues of 5G-connected vehicles enabled by Mobile edge computing remain a significant challenge when it comes to computing tasks and data related to IoVs. Our study proposes a Lyapunov Based Profit Maximum (LBPM) task offloading algorithm, which utilizes the Lyapunov optimization theory to maximize the time-averaged profit as the optimization objective. The algorithm uses the drift plus penalty optimization framework to establish the Lyapunov function and transforms the optimization goal into making a reasonable offloading decision at each time slot to optimize the upper bound of the function. We also compare the LBPM algorithm with existing algorithms for simulation experiments and performance analysis. The experimental results indicate that the LBPM algorithm increases the time-averaged profit by over 15%.

In this paper, we propose a platoon access authentication system for initial access process in autonomous vehicle platoons (AVPs) in which post-quantum encryption and signal processing techniques are employed to protect against both active and passive cyber-physical attacks. To avoid passive quantum cyber attacks, a quasi-cyclic moderate-density parity-check code is used to encode and decode AVP messages. Moreover, an independent component analysis-based signal separation technique is employed to eliminate the effect of high-power active cyber attacks on AVP messages. To measure the reliability of the system, we derive an analytical expression for the system failure probability, taking into account the influence of both the cyber and physical planes. The simulations show that the proposed system is effective against attacks and can help reduce system failures caused by intentional and unintentional adverse cyber-physical effects. The proposed system offers a potential solution to the challenge of protecting initial access while maintaining ultra-reliable low-latency communications between AVPs and the infrastructure.

With the maturity of 5G and Intelligent Transportation Systems (ITS) technologies and the prospect of Beyond 5G (B5G) and 6G technologies, the limited lifetime and computing of mobile devices pose significant challenges to Quality of Service (QoS). In addition, the problem of inefficient use of computing, storage, communication, and other resources still exists in communication systems. In response to the above issues, Multi-tier Computing Networks (MTCNs) migrate computationally intensive tasks to the cloud, fog, or edge with sufficient resources, thereby realizing energy-efficient collaborative computing and multi-dimensional resource sharing. However, in the MTCN environment with complex heterogeneity, and high-intensity dynamics, how to provide sustainable solutions for resource scheduling strategies is a meaningful issue. Inspired by Virtual Network Embedding (VNE) to decouple physical network configuration, we propose a multi-target-aware dynamic resource scheduling algorithm for MTCN to improve resource flexibility, which is the first attempt in this direction. Specifically, we consider differentiated QoS requirements like computing, storage, bandwidth, delay, etc., and establish multi-target-aware embedded constraints. Additionally, we present a Deep Reinforcement Learning (DRL)-based scheduling network that can interact scientifically and efficiently with the MTCN environment. It extracts environmental information as state input to better focus on dynamic characteristics as well as calculates candidate nodes and links using a three-layer network architecture and related constraints. Furthermore, the learning process is optimized through the combination of the reward mechanism and the gradient descent mechanism. Finally, comparison experiments on three widely used evaluation indicators (long-term average revenue, long-term average revenue-cost ratio, and VNR acceptance rate) verify that the proposed algorithm has made an average improvement of$19.042\%$,$2.563\%$, and$3.932\%$respectively compared with all baselines.

How to localize the rumor source is a common interest of all sectors of the society. Many researchers have tried to use deep-learning-based graph models to detect rumor sources, but they have neglected how to train their deep-learning-based graph models in thenoisysocial network environmentefficiently. Especially for deep learning models, the performance relies on the data scale. However, even though its known that a substantial amount of rumor data distributed across multiple edge servers (e.g., cross-platform), due to conflicting business interests, its challenging to coordinate all parties to train a model driven by many samples while avoiding moving data. Federated learning, is an effective technique to bridge this gap. Therefore, this paper proposes aDistributedRumorSourceDetection viaBoostedFederatedLearning (DRSDBFL). Specifically, this paper proposes an effective rumor source detection method based on a deep-learning-based graph model with a denoising module. To the best of our knowledge, we are the first to attempt to the use of a denoising module to reduce the noisy effects of social networks. Then, we propose a novel boosted federated learning mechanism through boosting the high-quality edge worker to improve the training efficiency. Finally, the effectiveness of the proposed method is verified by extensive experiments.

With the increasing proliferation of Internet-of-Things (IoT) devices, there is a growing trend towards distributing the power of deep learning (DL) among edge devices rather than centralizing it at the cloud. To deploy deep and complex models at edge devices with limited resources, model partitioning of deep neural network (DNN) models has been widely studied. However, most of the existing literature only considers distributing the inference model while still training the model at the cloud. In this paper, we propose FTPipeHD, a novel DNN training approach that trains DNN models across distributed heterogeneous devices with the fault-tolerance mechanism. To accelerate the training with the time-varying computing power of each device, we optimize the partition points dynamically according to real-time computing capacities. We also propose a novel weight redistribution approach that replicates the weights to both the neighboring nodes and the central node periodically, which combats the failure of multiple devices during training while incurring limited communication costs. Our numerical results demonstrate that FTPipeHD is 6.8 times faster in training than the state-of-the-art method when the computing capacity of the best device is 10 times greater than the worst one. It is also shown that the proposed method is able to accelerate the training even with the existence of device failures.

The integrated backscatter communication (BackCom) and active communication (AC) scheme can improve wireless powered mobile edge computing (WPMEC) system performance in general single-user and multi-user scenarios. However, there is little research in the cooperation-assisted WPMEC scenario. In this paper, we consider a cooperation-assisted WPMEC system consisting of a source node (SN), a helper and a hybrid access point (HAP) integrated with MEC servers. An innovative user cooperation (UC) scheme with integrated BackCom and AC is proposed to enhance the system performance. As a relay, the helper can help the SN to transmit its computing tasks due to the poor communication link between the SN and the HAP. To be specific, we aim at maximizing the user energy efficiency (EE) by jointly optimizing backscatter reflection coefficient for BackCom, transmission power for AC, system time and tasks allocation while considering the minimum computation bits requirement, the channel capacity and energy constraints. Based on a fractional program, the EE maximization problem first is transformed to an equivalent one. Then, we exploit variable substitution and convex optimization to transform this non-convex problem into a convex problem. In addition, semi-closed form expressions of the optimal solution are deduced. An energy efficiency maximization algorithm is proposed to solve this problem. Simulation results demonstrate that the proposed scheme significantly improves the user EE than the existing schemes.

One of the key issues in mobile edge computing (MEC) is computation offloading, most policies of which are developed based on mathematical programming (MP). Due to the high computational complexity of iterative programming in MP-based policies, recent years have seen a popular trend to develop offloading policies based on deep reinforcement learning (DRL). However, on account of the poor generalization ability of DRL models in MEC environments with different network sizes and settings, it is difficult to directly apply DRL-based offloading policies in unseen MEC environments. Motivated by this, we propose a DRL-based environment-adaptive offloading framework (DEAT), including a size-adaptive scheme (SIED) and setting-adaptive component (SEAL). SIED leverages the idea of ‘time division multiplexing’ to adapt to varying MEC network sizes and order-unaware feature extraction to mitigate impacts of different size-changing orders. SEAL adopts system dynamics embedding and offloading policy embedding, which guide the finding of the closest pre-training MEC environment and offloading policy, respectively, to achieve fast setting-adaptation with only few exploring interactions in unseen MEC environments. Extensive experiments are conducted via both simulation and testbed to demonstrate the adaptation performance advantages of DEAT in unseen MEC environments compared to the state-of-the-art offloading approaches.

The combination of mobile communication and the Internet of Things (IoT) has made physical devices more intelligent, bringing great convenience to our lives. However, the deep integration of personal information and the Internet increases the risk of data leakage and is easily exploited maliciously. In addition, due to limited system resources, smart devices with lightweight design are required. Therefore, it is necessary to realize low-energy and effective abnormal behavior detection of IoT devices, but the existing detection methods have disadvantages such as leakage of user privacy, low accuracy, and difficulty in dynamically improving the effect. To address these issues, this paper proposes a dynamic interactive minor anomaly detection scheme called ADONIS based on Swarm Learning (SL). The scheme combines the concept of swarm defense and utilizes SL to achieve local data fusion, which improves the detection effect and protects user privacy. Moreover, the decentralized structure of SL can cope with the impact of single node damage to enhance the robustness of IoT services. Furthermore, we propose training and detection decoupling framework to achieve high accuracy, low energy consumption, and low latency. It improves the performance by fitting the training model with full data, and simplifies the complexity of the detection model using knowledge distillation. We also design a self-enhancing dynamic strategy based on the decoupling framework to maintain powerful detection capability through human-computer interaction (HCI) and continuous learning. The framework relies on traffic data to keep the model sensitive to new behavior through iterative training without disturbing the user. Finally, simulation experiments show that our proposed scheme can achieve 82.2% accuracy, reduce the average detection time to 8.22$ ms$, and simplify the model complexity by 15.9%. Compared with existing methods, ADONIS can provide lighter, safer and more accurate anomaly detection.

Due to the outstanding merits such as mobility, high maneuverability, and flexibility, Unmanned Aerial Vehicles (UAVs) are viable mobile power transmitters that can be rapidly deployed in geographically constrained regions. They are good candidates for supplying power to energy-limited Sensor Nodes (SNs) with Wireless Power Transfer (WPT) technology. In this paper, we investigate a UAV-enabled WPT system that transmits power to a set of SNs at unknown positions. A key challenge is how to efficiently gather the locations of SNs and design a power transfer scheme. We formulate a multi-objective optimization problem to jointly optimize these objectives: maximization of UAV's search efficiency, maximization of total harvested energy, minimization of UAV's flight energy consumption and maximization of UAV's energy utilization efficiency. To tackle these issues, we present a two-stage strategy that includes a UAV Motion Control (UMC) algorithm for obtaining the coordinates of SNs and a Dynamic Genetic Clustering (DGC) algorithm for power transfer via grouping SNs into clusters. First, the UMC algorithm enables the UAV to autonomously control its own motion and conduct target search missions. The objective is to make the energy-restricted UAV find as many SNs as feasible without any apriori knowledge of their information. Second, the DGC algorithm is used to optimize the energy consumption of the UAV by combining a genetic clustering algorithm with a dynamic clustering strategy to maximize the amount of energy harvested by SNs and the energy utilization efficiency of the UAV. Finally, experimental results show that the proposed algorithms outperform their counterparts.

This paper examines integrating jamming and secondary signals for covert communications in cognitive radio networks (CRNs), aiming to enhance covertness by using jamming and secondary signals in an overlay cooperative CRN. The scenario involves a primary base station (PBS) transmitting to a primary user (PU), with a secondary user transmitter (SU-Tx) acting as a cooperative jammer to obscure the message from a malevolent secondary user named “Willie.” During idle intervals on the primary channel, the SU-Tx opportunistically accesses it to transmit secondary signals, reinforcing the covert communication of primary signals. The study quantifies the detection error probability (DEP) experienced by Willie, considering perfect and statistical channel state information (CSI) scenarios. In the perfect CSI scenario, optimization has two phases. Phase I aims to maximize the signals-to-interference-plus-noise ratio (SINR) of the PU, subject to the warden DEP exceeding a specified threshold. Phase II uses an iterative search algorithm to optimize beamforming vectors, enhancing SINR. In the statistical CSI scenario, the goal is to maximize effective transmission throughput (ETT), measuring the information transmitted from PBS to PU under covert constraints. Numerical results validate the theoretical analysis.

Content caching in mobile networks is a highly promising technology for reducing traffic load latency and energy consumption levels. Its fundamental goal is to satisfy the supply-and-demand relationships between content providers and content-requesting users. However, previous research primarily focused on the optimization goals of mobile network operators, and although these caching strategies yield improved latency and energy consumption levels, they fall short of satisfying the diverse content demands of users in real-world scenarios. Therefore, this paper proposes a diversity-driven proactive caching strategy that considers multiple stakeholders' requirements, in which the cache hit rate, cache gain for network operators, and content diversity are jointly optimized. Specifically, a novel improved Latent Dirichlet Allocation (LDA) is designed for radio access network caching, which enables diverse topic associations. For user device caching at the network edge, a Gradient-Guided Contrastive Learning (GGCL) is proposed to optimize the multiple objectives of cache systems with limited labeled data resources. Finally, extensive experiments conducted on the MovieLens dataset demonstrate that the proposed method significantly outperforms other methods in various aspects, including content diversity, the cache hit rate, and some network performance metrics, such as the traffic load and cache gain.

In the collaborative intelligent transportation system, providing precise positioning services is costly. Reducing resource consumption and improving revenue are crucial to the development of positioning services. Therefore, a practical algorithm that combines cloud and edge network environments is necessary to improve the positioning services. Integrating network function virtualization and edge computing can provide users with more flexible and efficient services. Based on the above issues, we use the service function chain (SFC) to improve the positioning services provided in cloud-edge-vehicle collaborative networks (CEVCN). We propose a deep reinforcement learning-assisted SFC embedding algorithm and improve its performance through training. We construct a five-layer policy network to sense the environment of CEVCN and derive the optimal node selection strategy. Finally, we use the breadth-first search algorithm to solve the embedding scheme for virtual links. The simulation results show that our proposed algorithm has excellent performance. The long-term average revenue is improved by 21%, the long-term average revenue-cost ratio is improved by 13%, and the embedding rate is improved by 8%.

Vehicular Edge Computing (VEC) is the transportation version of Mobile Edge Computing (MEC). In VEC, task offloading enables vehicles to offload computing tasks to nearby Roadside Units (RSUs), thereby reducing the computation cost. Recent trends in task offloading cause a proliferation of studies in academia. However, the existing offloading schemes still face many challenges, such as high-dynamic network topology, massive and complex data, dynamic scenes with high-speed vehicles and low-latency requirements. Digital Twin (DT)-based VEC is emerging as a promising solution. It monitors the state of the VEC network in real time through mappings and interactions between the physical and virtual entities. Consequently, the task offloading scheme can make more reasonable offloading decisions at the physical layer and further improve the efficiency of VEC. Above all, we propose a VEC computing offloading scheme, namely, AdaptiveSwarm Intelligent Offloading Scheme Based on Digital-Twin-Assisted PRedictionInVEC (STRIVE). The VEC network architecture is established to combines DT with an improved Generative Adversarial Network (GAN). The powerful prediction ability of GAN is used to assist in constructing DT in the pre-processing phase, reducing the size of the decision space. To adapt to the dynamic nature of VEC, we establish an adaptive model to adjust the real-time parameter under various scenarios. Then, we deploy an improveDgenetIc simulatEd annealing-baSEd particLe swarm optimization (DIESEL) algorithm to task offloading decision-making, which can provide reliable computing services for vehicles at a lower cost. The simulation results demonstrate that the proposed scheme can effectively reduce computing delay and energy consumption compared with its counterparts.

In sixth-generation (6G) networks, unmanned aerial vehicles (UAVs) are expected to be widely used as aerial base stations (ABS) due to their adaptability, low deployment costs, and ultra-low latency responses. However, UAVs consume large amounts of power to collect data from multiple sensor nodes (SNs). This can limit their flight time and transmission efficiency, resulting in delays and low information freshness. In this paper, we present a multi-access edge computing (MEC)-integrated UAV-assisted wireless sensor network (WSN) with a laser technology-based energy harvesting (EH) system that makes the UAV act as a flying energy charger to address these issues. This work aims to minimize the age of information (AoI) and improve energy efficiency by jointly optimizing the UAV trajectories, EH, task scheduling, and data offloading. The joint optimization problem is formulated as a Markov decision process (MDP) and then transformed into a stochastic game model to handle the complexity and dynamics of the environment. We adopt a multi-agent deep Q-network (MADQN) algorithm to solve the formulated optimization problem. With the MADQN algorithm, UAVs can determine the best data collection and EH decisions to minimize their energy consumption and efficiently collect data from multiple SNs, leading to reduced AoI and improved energy efficiency. Compared to the benchmark algorithms such as deep deterministic policy gradient (DDPG), Dueling DQN, asynchronous advantage actor-critic (A3C) and Greedy, the MADQN algorithm has a lower average AoI and improves energy efficiency by 95.5%, 89.9%, 78.02% and 65.52% respectively.

Unmanned aerial vehicles (UAVs) are widely recognized as crucial supplementary component of 6G networks. Owing to the key attributes of UAVs (mobility, flexibility, and adjustable altitude), UAVs can serve as flying base stations (BSs), flying relays and mobile terminals in order to expand the service coverage and derive more applications. Softwarization is regarded as dominant attribute of network architecture of 6G, mainly realized by network function virtualization (NFV) and software defined networking (SDN). In this paper, we concentrate on researching the resource orchestration and allocation of end-to-end (E2E) slice services in softwarized UAVs-assisted 6G terrestrial networks. Problem models of UAVs-assisted 6G terrestrial networks and E2E slice are firstly introduced. Then, the problem formulation of resource orchestration and allocation of E2E slice is presented. Afterwards, one novel framework design, abbreviated as ReOrcAll-UAVs-6G, is detailed. When receiving one E2E slice, our ReOrcAll-UAVs-6G checks the available softwarized resources. If having available softwarized resources, our ReOrcAll-UAVs-6G turns to serving the slice and fulfilling slice’s tailored resource demands. During the orchestration and allocation phase, wireless and wired resource requests of this slice are considered and executed. Evaluation work and gained results of ReOrcAll-UAVs-6G and selected approaches are illustrated and analyzed. Gained results reveal that our ReOrcAll-UAVs-6G achieves apparent performance advantage, comparing with all selected approaches.

Federated learning offers a partial safeguard for participants’ data privacy. Nevertheless, the current absence of an efficient privacy-preserving federated learning technology tailored for the Internet of Things (IoT) poses a challenge. Numerous privacy-preserving federated learning frameworks have been proposed, primarily relying on homomorphic cryptosystems, yet their suitability for IoT remains limited. Furthermore, the application of federated learning in IoT confronts two significant obstacles: mitigating the substantial communication costs and communication failure rates, and effectively discerning and utilizing high-quality data while discarding low-quality data for collaborative modeling purposes. In order to address these challenges, this paper introduces a privacy-preserving optimal aggregation federated learning framework that relies on the utilization of the multi-key EC-ElGamal cryptosystem (MEEC) and the federated sum optimization algorithm (FSOA), which are characterized by their lightweight nature and fair properties. The proposed MEEC approach aims to tackle the issue of multi-key collaborative computing within the context of federated learning, thereby resulting in reduced communication costs and enhanced communication efficiency. This is achieved through the leverage of the EC-ElGamal cryptosystem, which is known for its ability to generate short keys and ciphertexts. Furthermore, this paper presents a dynamic federated learning framework that incorporates user dynamic quit and join algorithms. The primary objective of this framework is to mitigate the adverse effects of communication failures and enhance power computation on IoT devices. Additionally, an FSOA is devised to ensure the acquisition of optimal training data, thereby preventing the inclusion of low-quality data in the training process. Subsequently, the proposed scheme undergoes rigorous security analysis and performance evaluation. The obtained results unequivocally demonstrate that our scheme outperforms existing solutions in terms of security, practicality, and efficiency with lower communication and computational costs.

In the age of data science and connected devices of all kinds, users have gained a lot of convenience. However, the massive data generated by users from the communication network is faced with security problems such as intrusion, tampering, hijacking, etc. According to the properties of covert transmission, the network with data covert embedding becomes an effective means to solve these challenges. In this paper, we propose a four-dimensional space-based data multi-embedding mechanism to protect data in the network. Specifically, a feature extractor is first used to extract vectors for embedding. Next, a Schmitt-based four-dimensional space is constructed, two of which are used to embed robust data to hide confidential information. The other two dimensions are used to embed fragile data to detect whether the signal has been tampered with. According to the experimental comparison with other methods, it shows that the method proposed in this paper achieves satisfactory performance.

UAV (unmanned aerial vehicle) is rapidly gaining traction in various human activities and has become an integral component of the satellite-air-ground-sea (SAGS) integrated network. As high-speed moving objects, UAVs not only have extremely strict requirements for communication delay, but also cannot be maliciously controlled as a weapon by the attacker. Therefore, it is necessary to design an efficient and secure communication mechanism (ESCM) for the UAV network (a mobile ad hoc network composed of multiple UAVs). For high efficiency, ESCM provides a routing protocol based on the artificial bee colony (ABC) algorithm to accelerate communications between UAVs. Meanwhile, we use blockchain to guarantee the security of UAV networks. However, blockchain has unstable links in high-mobility networks resulting in low consensus efficiency and high communication overhead. Consequently, ESCM introduces digital twin (DT), which transforms the UAV network into a static network by mapping UAVs from the physical world into Cyberspace. This virtual UAV network is called CyberUAV. Then, in CyberUAV, we design a blockchain consensus based on network coding, named Proof of Network Coding (PoNC). Analysis and simulation show that the above modules in ESCM have advantages over existing schemes. Through ablation studies, we demonstrate that these modules are indispensable for efficient and secure communication of UAV networks.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has largely extended the border and capacity of artificial intelligence of things (AIoT) by providing a key element for enabling flexible distributed data inputs, computing capacity, and high mobility. To enhance data privacy for AIoT applications, federated learning (FL) is becoming a potential solution to perform training tasks locally on distributed IoT devices. However, with the limited onboard resources and battery capacity of each UAV node, optimization is required to achieve a large-scale and high-precision FL scheme. In this work, an optimized multi-UAV-assisted FL framework is designed, where regular IoT devices are in charge of performing training tasks, and multiple UAVs are leveraged to execute local and global aggregation tasks. An online resource allocation (ORA) algorithm is proposed to minimize the training latency by jointly deciding the selection decisions of clients and a global aggregation server. By leveraging the Lyapunov optimization technique, virtual energy queues are studied to depict the energy deficit. With the help of the actor-critic learning framework, a deep reinforcement learning (DRL) scheme is designed to improve per-round training performance. A deep neural network (DNN)-based actor module is designed to derive client selection decisions, and a critic module is proposed through a conventional optimization method to evaluate the obtained selection decisions. Moreover, a greedy scheme is developed to find the optimal global aggregation server. Finally, extensive simulation results demonstrate that the proposed ORA algorithm can achieve optimal training latency and energy consumption under various system settings.

Federated learning (FL) is a prominent paradigm of 6G edge intelligence (EI), which mitigates privacy breaches and high communication pressure caused by conventional centralized model training in the artificial intelligence of things (AIoT). The execution of multimodal federated perception (MFP) services comprises three sub-processes, including sensing-based multimodal data generation, communication-based model transmission, and computing-based model training, ultimately competitive on available underlying multi-domain physical resources such as time, frequency, and computing power. How to reasonably coordinate the multi-domain resources scheduling among sensing, communication, and computing, therefore, is vital to the MFP networks. To address the above issues, this article explores service-oriented resource management with integrated sensing, communication, and computing (ISCC). Specifically, employing the incentive mechanism of the MFP service market, the resources management problem is defined as a social welfare maximization problem, where the concept of “expanding resources” and “reducing costs” is used to enhance learning performance gain and reduce resource costs. Experimental results demonstrate the effectiveness and robustness of the proposed resource scheduling mechanisms.

The integration of blockchain and Web 3.0 technologies offers significant advancements in identity management and trust within decentralized Metaverse environments. Despite the rapid development of numerous Metaverse platforms, these environments often operate in isolation, lacking interoperability and cohesive security measures. This article proposes a novel architecture leveraging self-sovereign identity (SSI) principles and blockchain technology to achieve secure and interoperable interactions across different Metaverse platforms. Our framework encompasses a digital wallet application, an interactive virtual environment, and a secure blockchain-based backend infrastructure. We detail the system architecture, operational specifics, and demonstrate significant improvements in scalability, efficiency, and security through comprehensive performance evaluations. Additionally, we address the challenges of real-world implementation and propose viable solutions. By enhancing user control over personal data and enabling secure cross-platform interactions, our approach aims to foster a more secure, interoperable, and user-centric decentralized Metaverse.

As a promising technology, intelligent reflecting surface (IRS) enables future communications and networks to realize programmable data transmissions. Due to the untrustworthiness of the communication environment and the selfishness of wireless devices, secure and intelligent IRS resource management is still an open issue. In this paper, we aim to implement IRS resource scheduling with properties of security, intelligence, efficiency, and fairness. To realize the above goals, we propose the blockchain and multi-agent learning empowered incentive scheduling system for tamper-proof and undeniable IRS resource management. To overcome the low throughout and intensive computation issues of blockchain, we devise a hybrid framework combining traditional Satoshi-style and directed acyclic graph blockchain for IRS resource scheduling. Due to the storage limitation of wireless devices, an intelligent blockchain storage reduction mechanism is proposed, where a multi-dimensional multi-hierarchy feature-based scheme is designed to determine block storage priority. Based on this storage priority and device states, the selection of storage-reduction devices is formulated as a cooperative multi-agent decision problem. Then, a multi-agent deep reinforcement learning-driven scheme is proposed to determine reduction strategies. To facilitate IRS providers/subscribers participating in the proposed system and maintain the efficiency of resource scheduling, an auction-based incentive mechanism is devised. In this mechanism, we propose the IRS resource allocation scheme and the payment scheme to achieve economic robustness and high efficiency. Finally, security analysis and experiment analysis indicate the feasibility and effectiveness of the proposed IRS resource scheduling in intelligent reconfigurable networks.

Space-Air-Ground Integrated Networks (SAGIN) is considered as the key structure of the next generation network. The space satellites and air nodes are potential candidates to assist and offload the computing tasks. An Unmanned Aerial Vehicle (UAV) collects computing tasks from IoT devices and then makes online offloading decisions. However, UAVs belonging to different service providers compete for computing resources from ground base stations during task scheduling, resulting in extremely long queue delays and load imbalance. In this paper, we designed a task scheduling algorithm based on Proportional Fairness-Aware Auction with Proximal Policy Optimization (PFAPPO), which decouples the task scheduling process in competitive scenarios into two parts: resource allocation and task offloading decision-making. We first propose an auction algorithm to allocate computing resources reasonably to each UAV, after resource allocation is completed, the UAV learns its available computing resources at each offloading destination. Based on the heterogeneous characteristics of the tasks, the UAV makes intelligent offloading decisions using the distributed deep reinforcement learning PPO algorithm. The simulation results show that our proposed PFAPPO has obvious performance improvement compared with existing methods in terms of system profit, load balancing, and system fairness.

The Maritime Internet of Things (MIoTs) provides intelligent information services for marine scientific research, emergency response and environmental monitoring by leveraging its wide coverage and ubiquitous connectivity. However, challenging maritime communication conditions and limited sea-based network resources hinder MIoT from meeting the evolving network quality of service requirements of growing maritime activities. This poses a significant challenge to ensuring real-time and reliable transmission of mixed traffic flows. To address issues such as link contention and transmission delays in software-defined MIoT systems, a deterministic scheduling and highly reliable routing mechanism based on cross-layer design is proposed. First, a deterministic scheduling mechanism for mixed traffic flows is introduced, which effectively reduces transmission delays and improves the schedulability of data flows. Second, a high-reliability, low-latency routing mechanism based on Double Deep Q Network (DDQN) is proposed, which is capable of dynamically screening neighbouring nodes based on real-time link and node states, thus facilitating fast and high-quality path selection. Extensive simulation results show that DSMTF improves flow schedulability by 28% compared to traditional algorithms, while HRLDQ increases the network packet delivery rate by 25.8% and reduces the average end-to-end delay by 23.6%.

Despite the extensive use of IoT and mobile devices in the different applications, their computing power, memory, and battery life are still limited. Multi-Access Edge Computing (MEC) has recently emerged to address the drawbacks of these limitations. With MEC on the network's edge, mobile and IoT devices can offload their computing operations to adjacent edge servers or remote cloud servers. However, task offloading is still a challenging research issue, and it is necessary to improve the overall Quality of Service (QoS) and attain optimized performance and resource utilization. Another crucial issue that is usually overlooked while handling this matter is offloading an application that consists of dependent tasks. In this study, we suggest a Refined Whale Optimization Algorithm (RWOA) for solving the multiuser dependent tasks offloading problem in the Edge-Cloud computing environment with three objectives: 1- minimizing the application execution latency, 2- minimizing the energy consumption of end devices, and 3- the charging cost for used resources. We also avoid the traditional binary planning mechanisms by allowing each task to be partially processed simultaneously at three processing locations (local device, MEC, cloud). We compare RWOA with other Optimizers, and the results demonstrate that the RWOA has optimized the fitness by 52.7% relative to the second best comparison optimizer.

As electric vehicle (EV) ownership becomes more commonplace, partly due to government incentives, there is a need also to design solutions such as energy allocation strategies to more effectively support sustainable vehicle-to-grid (V2G) applications. Therefore, this work proposes an energy allocation strategy, designed to minimize the electricity cost while improving the operating revenue. Specifically, V2G is abstracted as a three-domain network architecture to facilitate flexible, intelligent, and scalable energy allocation decision-making. Furthermore, this work combines virtual network embedding (VNE) and deep reinforcement learning (DRL) algorithms, where a DRL-based agent model is proposed, to adaptively perceives environmental features and extracts the feature matrix as input. In particular, the agent consists of a four-layer architecture for node and link embedding, and jointly optimizes the decision-making through a reward mechanism and gradient back-propagation. Finally, the effectiveness of the proposed strategy is demonstrated through simulation case studies. Specifically, compared to the used benchmarks, it improves the VNR acceptance ratio, Long-term average revenue, and Long-term average revenue-cost ratio indicators by an average of 3.17%, 191.36, and 2.04%, respectively. To the best of our knowledge, this is one of the first attempts combining VNE and DRL to provide an energy allocation strategy for V2G.

With the continuous growth of network traffic, optical wireless communication (OWC) technology based on orbital angular momentum (OAM) can meet the needs of large-capacity modern communication and is an effective way to substantially increase wireless information transmission capacity. However, the OAM beam distortion caused by atmospheric turbulence in the actual link and the limitations caused by phase singularities are major challenges faced by the OAM-OWC system. In this paper, we address these issues and propose a low-complexity nonprobe adaptive optics (AO) compensation technique based on Y-net which can achieve high-accuracy distortion compensation and OAM mode demodulation simultaneously. In this approach, only one CCD is required for the Y-net-aided AO (Y-net AO) technique without a traditional probe path while satisfyingly balancing OAM-based optical transmission system complexity and transmission performance. Extensive simulations show that the proposed Y-net-aided AO technique can indeed decontaminate distorted OAM beams in both single- and multiplexed-channel OAM links. Furthermore, a noise model is established to analyze the robustness of the Y-net AO technique. The Y-net AO technique exhibits less system complexity and better anti-noise performance than the ordinary convolutional neural network (CNN)-based AO scheme. In summary, Y-net AO technology for OAM-OWC systems with high correction accuracy and low structural complexity is considered for effectively improving the transmission performance in this paper. Key AO technologies for the high-quality and innovative development of large-capacity communications are also expected to be formed.

In this paper, a novel hierarchical game framework for physical layer security (PLS) aware wireless communications with dynamic trilateral coalitions is studied. In the considered system, legitimate users (LUs) aim to transmit secret data to associated base stations (BSs) via uplink communications under the threat of eavesdroppers (EVs), while there also exists jammers (JAs) which may choose to form coalitions with either LUs for increasing their secrecy transmission rates or EVs for increasing their eavesdropping rates in exchange for potential rewards. Different from the existing work, we explore such complicated while dynamic coalition relationships under uncertainties of wireless systems (e.g., time-varying channel conditions), and formulate a hierarchical game integrated with a dynamic trilateral coalition formation game to model strategic interactions among LUs, JAs and EVs. Particularly, we first analyze stability conditions of the trilateral coalitions and propose a hedonic coalition selection and formation algorithm for reaching the stable coalition partition in each time slot. On top of this, we propose a deep reinforcement learning (DRL) based solution, which can achieve the equilibrium with long-term performance guarantees for the hierarchical game running over multiple time slots with dynamic evolutions. Simulations evaluate the proposed solution and show its superiority over counterparts.

As one of the indoor communication technologies, visible light communication (VLC) has drawn great attention for its advantages such as ultra-wide unlicensed spectrum, power saving and low complexity. The nature of the visible light propagation is an open channel, which is vulnerable to wiretapping. This paper investigates a secure VLC mechanism enabled by multiple light fixtures acting as friendly jammers. The goal of the friendly jammers is to diminish the capability of the eavesdropper to infer the undisclosed information, on the premise of causing minimal impact on the legitimate receiver. For this reason, an algorithm based on reinforcement learning is proposed to dynamically optimize the friendly jamming policy in realistic nonstationary environments. In order to resolve the difficult problem of the dimensional curse and to effectively represent the continuous state and action spaces, an algorithm based on deep reinforcement learning is devised, which utilizes deep convolutional neural networks to accelerate the convergence rate of the learning process. A differentiable neural dictionary is introduced to make full use of the experiences in similar anti-eavesdropping scenarios to improve the learning capability. Simulation results demonstrate that, the proposed schemes can achieve a higher secrecy rate and a lower bit error rate than some state-of-the-art schemes.

Massive random access of devices brings great challenge to the management of radio access networks. Most of the time, the access requests in the network is sporadic. Exploiting the bursting nature, sparse active user detection (SAUD) is an efficient enabler towards efficient active user detection. However, the sparsity might be deteriorated in case of high concurrent request periods. To dynamically coordinate the access requests, a reinforcement-learning (RL)-assisted scheme of closed-loop access control utilizing the access class barring (ACB) technique is proposed, where the control policy is determined through continuous interaction between the RL agent and the environment. The proposed RL agent can be deployed at the next generation node base (gNB), supporting rapid switching between heterogeneous vertical applications, such as mMTC and uRLLC services. Moreover, a data-driven scheme of deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces, where a replay buffer is applied for automatic large-scale data collection. An Actor-Critic framework is formulated to incorporate the strategy-learning modules into the intelligent control agent. Simulation results show that the proposed schemes can achieve superior performance in both access efficiency and user detection accuracy over the benchmark scheme for different heterogeneous services with massive access requests.

The growth of intelligent vehicular services, like augmented reality (AR) road simulation, underscores the need for rapid, multi-modal content generation. Generative artificial intelligence (GAI) models, known for their swift production of diverse artificial intelligence-generated content (AIGC), stand out as a prime solution. However, integrating cloud-centric GAI models into vehicular networks is fraught with challenges. Notably, to offer specialized generative edge intelligence (EI) and boost vehicular AIGC, GAI models need to tap into user data and utilize significant computation resources. Moreover, their deployment across vehicular networks is essential for proximity-based distributed inferences. Yet, edge devices are resource-limited, and data sharing can raise safety and privacy concerns. Addressing these challenges, this paper introduces GAI-IoV, an EI-enabled GAI framework facilitated through the cooperation between road-side units (RSUs) and vehicles. Subsequently, we propose the workflow for collaborative fine-tuning and distributed inference. On this basis, two pivotal vehicle-centric problems are then formulated: computation and communication resource allocation for federated fine-tuning (FFT) to optimize time and energy cost, and splitting strategy of shared and local inferences to optimize inference latency and content-generation capability. To solve these optimizations, we introduce a self-adaptive global best harmony search (SGHS) algorithm for resource allocation and a backward induction method for determining inference splitting strategy. Our experiments based on the Stable Diffusion v1-4 model vouch for a superior fine-tuning and inference capabilities of GAI-IoV. Furthermore, simulations underscore its resource utilization and distributed inference efficiency in dynamic vehicular scenarios.

Secret Image Sharing (SIS) is the technology that shares any given secret image by generating and distributing$n$shadow images in the way that any subset of$k$shadow images can restore the secret image. However, in the existing SIS schemes, the shadow images will be easily tampered and corrupted during the communication, which will pose serious security issues. Recently, blockchain has emerged as a promising paradigm in the field of data communication and information security. To securely communicate and effectively protect the secret image data in wireless networks, we propose a Blockchain-based Secure and Efficient Secret Image Sharing (BC-SESIS) scheme with outsourcing computation in wireless networks. In the proposed BC-SESIS scheme, the shadow images are encrypted and stored in the blockchain to prevent them from being tampered and corrupted. The identity authentication-enabled smart contract is deployed to achieve the$(k,n)$threshold for secret image restoring. Furthermore, to reduce the computational burden of smart contract and users, an efficient outsourcing computation method is designed to outsource the restoring task, which is securely implemented by agent miners in the encryption domain. Theoretical analysis and extensive experiments demonstrate that the BC-SESIS scheme can achieve desirable communication security and high computational efficiency in the wireless networks.

The rapid development of 6G millimeter-wave (mmWave) networks has introduced new challenges for network security. Adversarial attacks on beamforming algorithms in these networks can lead to severe communication performance degradation. This paper proposes an optimization framework for Deep Learning (DL) hyperparameters that enhances adversarial security in 6G mmWave networks through beam prediction. We develop a robust DL model that can adapt to various adversarial attacks and maintain high prediction accuracy. The proposed framework optimizes hyperparameters using hybrid Particle Swarm Optimization (PSO) with Multi-Verse Optimizer (MVO) for improved security. The framework is evaluated through extensive simulations, demonstrating its effectiveness in improving network security and robustness against adversarial attacks. Under normal conditions, the optimized model achieves the lowest mean squared error (MSE) of 9.4410E – 05 for beamforming codeword predictions. Subjected to Fast Gradient Sign Method (FGSM) adversarial attacks, the optimized model maintains the lowest MSE of 2.2910E – 03, indicating greater resilience against adversarial perturbations. With adversarial training, the optimized model achieves the lowest MSE of 2.7110E – 03, demonstrating the most robust defense against adversarial attacks. In contrast, the non-optimized model suffers significant performance degradation under adversarial and defended conditions. The source code is available at [1].

The dramatically increased wireless communication needs make non-renewable spectrum resources extremely scarce and costly. Consortium blockchain realizes trusted spectrum sharing among untrusted spectrum owners. Yet, most existing studies ignore spectrum sharing among blockchains, which greatly reduce spectrum utilization. In the paper, we focus on cross-chain spectrum sharing. We propose a Fine-grained Cross-chain Spectrum Sharing mechanism based on Oracle (FCSSO) to realize trusted and efficient cross-chain spectrum transactions. To guarantee benefits of spectrum owners, we design a fine-grained time partition method to decide spectrum renting time in transactions. The method reduces the waste of owners' available spectrum time caused by spectrum handoff. Extensive simulation proves the positive impact of the proposed fine-grained time partition method, and FCSSO outperforms two representative cross-chain mechanisms from two aspects: spectrum owners' benefits and spectrum utilization.

The automotive manufacturing industry faces challenges in production planning, but current heuristic algorithms and solvers have limitations in scalability and local optima. Moreover, data security concerns are often overlooked. To address these issues, this paper introduces the FAG-Scheduler, a federated reinforcement learning approach integrating asynchronous advantage actor-critic, gated recurrent unit algorithms, and federated learning. By sharing model parameters instead of raw data, data security is ensured among participants. The FAG-Scheduler achieves optimal solutions in under 5 seconds and demonstrates high adaptability to other manufacturing contexts. It presents potential applications with significant improvements over conventional methods.

Toward green Big Data System (BDS), one of the key requirements is to save energy consumption so that the system lifetime can be prolonged. Hence, the energy evaluation metric for the measurement of energy efficiency in green BDS, is very critical. Unfortunately, most current energy evaluation metrics are based on a single factor, which might be difficult to meet the diverse application requirements. In this paper, we propose a novel two-factor energy evaluation metric (TEEM) for green BDS. Specifically, the transmission distance and the modulation level are taken into account simultaneously, generating a metric named the bit-per energy consumption (BEC). Extensive simulation results demonstrate that the system performance in energy consumption can be more effectively evaluated by BEC.

As vehicular communication networks embrace metaverse beyond 5G/6G systems, the rich content update via the least interfered subchannel of the optimal frequency band in a hybrid band vehicle to everything (V2X) setting emerges as a challenging optimization problem. We model this problem as a tradeoff between multi-band VR/AR devices attempting to perform metaverse scenes and environmental updates to metaverse roadside units (MRSUs) while minimizing energy consumption. Due to the computational hardness of this optimization, we formulate an opportunistic band selection problem using a multi-armed bandit (MAB) that provides a good quality solution in real-time without computationally burdening the already stretched augmented/virtual reality (AR/VR) units acting as transmitting nodes. The opportunistic use of scheduling rich content updates at traffic signals and stand-still scenarios maps well with the formulated bandit problem. We propose a Dual-Objective Minimax Optimal Stochastic Strategy (DOMOSS) as a natural solution to this problem. Through extensive computer-based simulations, we demonstrate the effectiveness of our proposal in contrast to baselines and comparable solutions. We also verify the quality of our solution and the convergence of the proposed strategy.

With the advancement of cloud technology, the storage and computing overhead in large-scale biometric authentication is mitigated by outsourcing data to the cloud. Since biometric features serve as a unique identifier bound to each individual, transmitting them directly to the cloud may bring about serious privacy disclosure risks. To guarantee users' biometric features, there are many solutions have been proposed. However, most of them neglect to protect identity security. In light of the challenges, this paper proposes a strong privacy-preserving fingerprint authentication via clustering. Besides safeguarding fingerprint features, the scheme also blurs the identities of users to enable anonymity of identity. Meanwhile, the authentication efficiency of the proposed scheme is improved by vector processing of fingerprints and fast retrieval of clustered identities. Furthermore, a dual-server matching architecture effectively reduces the communication overhead of the service provider. The security analysis and experimental results indicate that the proposed scheme provides strong privacy preservation while maintaining high efficiency.

The multipath transmission structure decouples network services from a single transmission carrier, which has great potential for shaping a more secure and efficient 6G network. Existing multipath transmission schemes face challenges such as network heterogeneity, perception lag, and additional scheduling delay, which limits their ability to improve bandwidth aggregation capacity and information security. To address these issues, we propose the Sequential Reinforcement Evolution (SRE) scheme, which utilizes deep reinforcement learning to predict the value of future scheduling actions based on past network states. The SRE scheme regards improving bandwidth aggregation capacity and anti-eavesdropping ability as optimization goals, and designs a semi-symmetric attention recurrent neural network (SARNN) to better mine the sequential nature of the scheduling process. The SRE scheme utilizes approximately 500 million real network data points to pre-train the SARNN model, and performs cycle optimization during the actual deployment process. Experimental results show that SRE significantly outperforms state-of-the-art scheduling schemes with a 32% increase in bandwidth aggregation and a 117% increase in traffic security dispersion with minimal impact on latency.

Network delays cause a reduction in the Quality-of-Service (QoS) for Internet of Things (IoT) applications, and even render time-critical applications inoperative. The paper tackles the problem of forming fog federations that aim to improve the QoS. However, instabilities within fog federations might cause some providers to withdraw from the federation, and thus decrease the profit of the federations and the expected QoS. Moreover, federation formation techniques could potentially create privacy risks for end-users whose data is utilized in the process. This paper introduces a decentralized evolutionary game theoretic algorithm that tackles the problem of fog federation formation, as well as, providing a decentralized privacy-aware federated learning algorithm that predicts the QoS between fog servers for optimizing the formation procedure. The devised method provides better stability and increased QoS when compared to other benchmarks.

Neural networks have demonstrated remarkable success in learning and solving complex tasks in a variety of fields including cognitive cities. Nevertheless, the rise of those networks in modern computing has been accompanied by concerns regarding their vulnerability to adversarial attacks. In this work, we propose a novel gradient-free, gray box, incremental attack that targets the training process of neural networks. The proposed attack, which implicitly poisons the intermediate data structures that retain the training instances between training epochs acquires its high-risk property from attacking data structures that are typically unobserved by professionals. Hence, the attack goes unnoticed despite the damage it can cause. Moreover, the attack can be executed without the attackers' knowledge of the neural network structure or training data, making it more dangerous. The proposed attack was tested under a sensitive application of secure cognitive cities, namely, biometric authentication. The conducted experiments showed that the proposed attack is effective and stealthy. Finally, the attack effectiveness property was concluded from the fact that it was able to flip the sign of the loss gradient in the conducted experiments to become positive, which is noisy and unstable training. Moreover, the attack was able to decrease the inference probability in the poisoned networks compared to their unpoisoned counterparts by 15.37%, 14.68%, and 24.88% for the Densenet, VGG, and Xception, respectively. Finally, the attack retained its stealthiness despite its high effectiveness. This was demonstrated by the fact that the attack did not cause a notable increase in the training time, in addition, the Fscore values only dropped by an average of 1.2%, 1.9%, and 1.5% for the poisoned Densenet, VGG, and Xception, respectively.

The healthcare sector has undergone a significant transformation with the widespread adoption of Deep Neural Networks (DNN). However, due to privacy constraints and stringent latency requirements, online remote inference is not a viable option in healthcare scenarios. Many efforts have been conducted to enable local computation, such as network compression using pruning or DNN distribution among multiple resource-constrained devices. Yet, it is still challenging to conduct distributed inference due to the latency and energy overheads resulting from intermediate shared data. On the other hand, given that realistic healthcare systems use pre-trained models, local pruning and fine-tuning relying only on the scarce and biased data is not possible. Even pre-pruned DNNs are not efficient as they are not customized to the local load of data and the dynamics of devices. The dynamic and online pruning of DNN without fine-tuning is a promising solution; however, it was not considered in the literature as most well-known techniques do not perform well without adjustment. In this paper, driven by the data restrictions in healthcare sector, we propose a novel pruning strategy based on Explainable AI (XAI), with a target to enhance the pruned DNN performance without fine-tuning. Moreover, to maintain the highest possible accuracy, we propose to combine distribution and pruning techniques to perform online distributed inference assisted by dynamic pruning only when needed. Our experiments show the performance of our pruning criterion compared to other reference techniques, in addition to its ability to assist the distribution by reducing the shared data, while keeping high accuracy.

In recent years, a novel mobile computing paradigm has been evolving rapidly, with a host app allowing users to install and run mini-apps inside the app itself. However, the current classification mechanism of mini-apps is blurry and coarse-grained, making users unable to clearly understand mini-app functions, which can result in a series of privacy issues. In this study, an automatic convolutional neural network (CNN)-based classification approach is proposed for Wechatmini-apps. The proposed method integrates the static and dynamic features of WeChat mini-apps to achieve precise classification. Our approach was evaluated in a real-world testbed and the results showed that it can effectively classify Wechatmini-apps into proper categories, helping users better understand the functions of WeChat mini-apps while reducing user privacy violations.

Membership inference attack (MIA) has been proved to pose a serious threat to federated learning (FL). However, most of the existing membership inference attacks against FL rely on the specific attack models built from the target model behaviors, which make the attacks costly and complicated. In addition, directly adopting the inference attacks that are originally designed for machine learning models into the federated scenarios can lead to poor performance. We propose GBMIA, an attack model-free membership inference method based on gradient. We take full advantage of the federated learning process by observing the target model's behaviors after gradient ascent tuning. And we combine prediction correctness and the gradient norm-based metric for membership inference. The proposed GBMIA can be conducted by both global and local attackers. We conduct experimental evaluations on three real-world datasets to demonstrate that GBMIA can achieve a high attack accuracy. We further apply the arbitration mechanism to increase the effectiveness of GBMIA which can lead to an attack accuracy close to 1 on all three datasets. We also conduct experiments to substantiate that clients going offline and the overlap of clients' training sets have great effect on the membership leakage in FL.

Multi-signature scheme is a unique type of digital signature where a group of participants are capable of producing a signature interactively on a shared message, thus significantly reducing the signature size. This is especially important for Internet of Vehicles (IoV) systems where higher efficiency and lower costs are required during the communication. Most approaches so far, however, are developed by traditional methods such as the integer factoring assumption, which result in potential vulnerability to quantum computing attacks. Although a few lattice-based multi-signature candidates have been proposed, they either rely on hash-and-sign process with higher costs or may be compromised by larger size of public key and signature. Motivated by the Bimodal Lattice Signature Scheme (BLISS) model [1], we propose a new lattice-based multi-signature scheme (Multi-BLISS, MB) in this paper. Our scheme can also be transformed into an aggregate signature scheme (Aggregate MB, AMB) with similar level of performance. We evaluate both schemes by setting security levels of 128, 160 and 192 bits in the experiments, and the results demonstrate significant improvement on security and efficiency comparing to existing lattice-based multi-signature schemes.

Multipath transmission is an important solution in 6G wireless networks to improve communication resource convergence among heterogeneous networks. The appearance of out-of-order (OFO) packets is a problem in multipath transmission, especially in heterogeneous wireless networks, where network states fluctuate frequently. Existing schedulers attempt to solve the OFO problem by designing an “in-order” strategy. However, these “in-order” strategies lose their roles in jittered networks, resulting in poor throughput performance. In this study, we propose a novel packet scheduling strategy, called a multipath-state machine (MSM) scheduler. MSM prioritizes improving throughput utilization over ensuring packets arrive in order. Based on whether links are fully utilized, MSM divides link utilization states into 5 phases, including establishment, idle, busy, congestion, and risk. MSM prefers underutilized links. Furthermore, MSM chooses the one with the shortest transmission delay when multiple paths are in the same utilization state. Experiments show that MSM provides a more stable and higher bandwidth utilization than typical algorithms in various networks with heterogeneous characteristics such as delay, bandwidth, and jitter.

Mobile edge computing (MEC) enables the Internet of Things (IoT) with seamless integration of multiple application services. Federated learning is increasingly considered to improve training accuracy in MEC-IoT while circumventing the disclosure of private data, where the IoT nodes collaboratively train a machine learning model without disclosing their private data. In this paper, we propose a new cyber-epidemic attack that progressively manipulates federated learning and reduces the training accuracy of the benign MEC-IoT. The proposed cyber-epidemic attack explores adversarial graph autoencoders (GACE) to generate malicious local model updates that extract correlated features with the benign local and global models. The proposed GACE attack epidemically infects all the benign IoT nodes along with the training iterations in federated learning, while highly enhancing concealment of the attack.

The increasing number of applications and devices in the Sixth-generation (6G) networks and the diversity of mobile data, architectures, and technologies make security and privacy a critical concern. Advanced metaheuristics algorithms (MHAs) have recently become a viable solution for optimizing security and privacy in wireless networks, combining game theory and convex optimization, and several other advanced models. As a subfield of Artificial Intelligence (AI), MHAs are inspired by concepts from Evolutionary Algorithms (EAs), Trajectory-based Algorithms (TAs), and Swarm Intelligence (SI). Recent implementations of MHAs in the 6G networks have effectively solved complex security and privacy problems. This study examines MHAs’ utilization in addressing security and privacy challenges in 6G networks. The paper provides a comprehensive overview of MHAs and their use in solving security and privacy problems in 6G. The current limitations of the literature are also identified, and avenues for further research are suggested. The reader will have a clear image of the needed technologies and tools for securing 6G networks using MHAs.

In this paper, we increase the availability and integration of devices and models together in the learning process to enhance the convergence of federated learning (FL) models. The majority of the literature suggested client selection techniques to accelerate convergence and boost accuracy. However, none of the existing proposals have focused on the flexibility to deploy and select clients as needed, wherever and whenever that may be while serving multiple FL models. Due to the extremely dynamic surroundings, some devices are actually not available to serve as clients in FL, which affects the availability of data for learning and the applicability of the existing solution for client selection. In this paper, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process while supporting multiple models. We make use of the containerization technology such as Docker to build efficient environments using IoT and mobile devices serving as volunteers. Furthermore, Kubernetes is used for orchestration. The performed experiments using the Mobile Data Challenge (MDC), MNIST, KDD datasets, and the Localfed framework illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients with less discarded rounds and more available data of each running FL application.

Internet of Things (IoT)-based smart factories offer the manufacturing sectors a great opportunity to embrace the fourth industrial revolution (Industry 4.0). The real-time monitoring of manufacturing operations in an Industry 4.0 needs to be ensured by the deployed technologies, like Artificial Intelligence (AI) and Big Data analytics. The overall purpose is to improve the outcomes of the production process. However, Industry 4.0 becomes vulnerable to different potential attacks as the communication takes place via public environments. In this article, an authentication and key agreement method has been suggested to secure the communication that can occur in a Fog-based Industry 4.0 environment. The security proposal provides secure mutual authentication along with key establishment between various smart industrial devices and fog servers, as well as between fog servers and cloud servers. The security analysis and comparative study reveal that the proposed method can mitigate various potential attacks, and it also offers important security and functionality attributes as compared to those for other competing schemes.

A cyber-physical system (CPS) is a promising paradigm in 5G and future 6G networks that controls physical components through computing and communication while ensuring efficacy, intelligence, and security. The number of smart mobile devices or sensors in smart cities is growing very fast. These devices can process applications in real-time only for a short time due to limited resource capacity. The Mobile Edge Computing (MEC) paradigm is a prominent solution that allows devices to offload intensive tasks and allocate resources. However, the terrestrial MEC servers will be overwhelmed and unable to meet the requirements of 6G technologies for ultra-low-latency applications and mobile devices. Aerial-borne MEC servers have recently supported ultra-reliable, low-latency communication applications and mobile devices in an emergency scenario by providing resources and relaying them to a cloud server. In the intelligent aerial-enabled smart city CPS (S2CPS), decisionmaking tasks such as resource allocation, association, and ensuring trust between links are challenging, and the optimization problem is multi-objective. Therefore, we proposed a hierarchical, deep federated learning-empowered, energy-efficient resource allocation for aerial-enabled S2CPS to minimize the overall energy consumption while considering the quality of service of user devices and the privacy of task offloading in a dynamic environment. We validated the proposed framework through extensive simulations, proving it outperformed the baseline algorithms.

Federated Learning (FL) is a revolutionary privacy-preserving distributed learning framework that allows a small group of users to cooperatively build a machine-learning model using their own data locally. Smart cities are areas that can generate high volume and critical data, which has the potential to revolutionize federated learning. Nevertheless, it is highly challenging to select a trustworthy group of clients to collaborate in model training. The utilization of a random selection technique would pose many threats due to malicious clients’ targeted and untargeted attacks. Such vulnerability may cause attacks and poisoning in the produced model. To address this problem, we present a mutual trust client-server selection approach based on matching game theory and bootstrapping mechanisms for federated learning in smart cities. Our solution entails the creation of: (1) preference functions for federated servers and smart devices (i.e., IoT/IoV) that enables them to sort each other based on trust score, (2) light feedback-base technique that leverages the cooperation of multiple client devices to assign trust value to the newly connected federated server, and (3) intelligent matching algorithms consider trust preferences of both parties in their design. According to our simulation results, our technique outperforms the baseline selection approach VanillaFL in terms of increasing the trust level and hence the global accuracy of the federated learning model and optimizing the number of untrusted selected clients.

Recently, the benefits of moving computation to the edge were researched and proven. The paradigm of Mobile Edge Computing (MEC) provides a clear view of how offloading core network burden leads to the shortening of data access latencies, which is primal for supporting services at the edge. Increasing the density of edge data centers for supporting such requests is uneconomical. However, it is important to have a clear resource allocation strategy in such a way that user scheduling is considered. Therefore, in this paper, we study and prove how resource scheduling at the edge can substantially affect network performance. To this end, we focus on the stochastic nature of the wireless links and reduce this problem to a stochastic game. We design a software edge controller called Edge-Boss that resides at one base station and controls the wireless links available at each base station. In addition, the model ensures that Edge-Boss achieves an equilibrium that enables the Base stations (BS) to provide a higher performance (as this equilibrium acts as an incentive for the BS). Using simulations we show that Edge-Boss achieves latency reductions and a high network throughput.

In emergency rescue scenarios, unmanned aerial vehicle (UAV) communications with reconfigurable intelligent surfaces (RIS) is a way to enhance the communications link. However, the key challenges lie in the acquisition of channel information and the design of beamforming due to the limited energy storage and high density integrated antenna array. To solve this problem, we propose an energy minimization (EM) based beam training scheme to optimize the energy consumption of the communication system. Specifically, we consider the RIS assisted UAV communication system with emergency rescue. Considering the constraints of RIS phase-shift, system communication rate and energy efficiency, we propose an optimization problem to minimize the system transmission power, and obtain the optimal solution. The simulation results show that the proposed optimization algorithm can ensure the minimum communication requirements and reduce the transmission power of the system.

Federated learning (FL) is increasingly considered to circumvent the disclosure of private data in mobile edge computing (MEC) systems. Training with large data can enhance FL learning accuracy, which is associated with non-negligible energy use. Scheduled edge devices with small data save energy but decrease FL learning accuracy due to a reduction in energy consumption. A trade-off between the energy consumption of edge devices and the learning accuracy of FL is formulated in this proposed work. The FL-enabled twin-delayed deep deterministic policy gradient (FL-TD3) framework is proposed as a solution to the formulated problem because its state and action spaces are large in a continuous domain. This framework provides the maximum accuracy ratio of FL divided by the device’s energy consumption. A comparison of the numerical results with the state-of-the-art demonstrates that the ratio has been improved significantly.

Metaverse-empowered wireless system design is considered one of the promising candidates for the design of foreseeable future wireless systems. Therefore, in this paper, we consider energy-efficient (i.e., green) deployment of the metaverse at the network edge. We propose a framework for the deployment of the metaverse at the network edge using the concept of clustering over social networking. Then, we define a cost function that accounts for the energy consumption. We formulate an optimization problem to minimize energy consumption by optimizing clustering and resource allocation. The main problem is decomposed into two sub-problems: (a) clustering and (b) resource allocation. For both clustering and resource allocation, heuristic algorithms are proposed. Finally, we present numerical results.

Decision tree (DT) models are widely used in medical applications where the size of the data sets is usually small or medium. Moreover, DT ensemble models are preferred over single DT models because of their higher accuracy in spite of the need for more overhead due to using multiple trees. Several schemes have been proposed for privacy-preserving cloud-based medical diagnosis using ensemble models. However, these schemes suffer from several limitations. First, they suffer from high computation/communication overheads due to using inefficient public-key cryptosystems. Second, none of them can simultaneously protect the intellectual property of the model and preserve the privacy of the patients’ data and diagnosis results. Finally, they do not provide inherent access control for the outsourced model and micropayment, in which only the registered patients can use the model and pay for the service. In this article, we develop a lightweight and privacy-preserving cloud-based medical diagnosis scheme using ensemble models with high accuracy and acceptable overhead. Using our scheme, the model owner can control the patients who can use the model. Also, for each classification operation, patients must make a micro-payment to pay for the diagnosis service. Our analysis indicates that our scheme can protect the model’s intellectual property and diagnose diseases without leaking any sensitive information about the patients’ medical data and the diagnosis results. Our experimental results demonstrate that our scheme requires less communication/computation overhead compared to the existing schemes.

The rise of chronic disease patients and the pandemic pose immediate threats to healthcare expenditure and mortality rates. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, leveraging the recent advances of Internet of Things and smart sensors. Meanwhile, reinforcement learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for distinct applications and services. Thus, this article presents a comprehensive survey of the recent models and techniques of RL that have been developed/used for supporting Intelligent-healthcare (I-health) systems. It can guide the readers to deeply understand the state-of-the-art regarding the use of RL in the context of I-health. Specifically, we first present an overview of the I-health systems’ challenges, architecture, and how RL can benefit these systems. We then review the background and mathematical modeling of different RL, deep RL (DRL), and multiagent RL models. We highlight important guidelines on how to select the appropriate RL model for a given problem, and provide quantitative comparisons, showing the results of deploying key RL models in two scenarios that can be followed in monitoring applications. After that, we conduct an in-depth literature review on RL’s applications in I-health systems, covering edge intelligence, smart core network, and dynamic treatment regimes. Finally, we highlight emerging challenges and future research directions to enhance RL’s success in I-health systems, which opens the door for exploring some interesting and unsolved problems.

In the past several years, the world has witnessed an acute surge in the production and usage of smart devices which are referred to as the Internet of Things (IoT). These devices interact with each other as well as with their surrounding environments to sense, gather and process data of various kinds. Such devices are now part of our everyday’s life and are being actively used in several verticals, such as transportation, healthcare, and smart homes. IoT devices, which usually are resource-constrained, often need to communicate with other devices, such as fog nodes and/or cloud computing servers to accomplish certain tasks that demand large resource requirements. These communications entail unprecedented security vulnerabilities, where malicious parties find in this heterogeneous and multiparty architecture a compelling platform to launch their attacks. In this work, we conduct an in-depth survey on the existing intrusion detection solutions proposed for the IoT ecosystem which includes the IoT devices as well as the communications between the IoT, fog computing, and cloud computing layers. Although some survey articles already exist, the originality of this work stems from the three following points: 1) discuss the security issues of the IoT ecosystem not only from the perspective of IoT devices but also taking into account the communications between the IoT, fog, and cloud computing layers; 2) propose a novel two-level classification scheme that first categorizes the literature based on the approach used to detect attacks and then classify each approach into a set of subtechniques; and 3) propose a comprehensive cybersecurity framework that combines the concepts of explainable artificial intelligence (XAI), federated learning, game theory, and social psychology to offer future IoT systems a strong protection against cyberattacks.

Federated learning is gaining popularity as a distributed machine learning method that can be used to deploy AI-dependent Internet of Things applications while protecting client data privacy and security. Due to the differences of clients, a single global model may not perform well on all clients, so the personalized federated learning method, which trains a personalized model for each client that better suits its individual needs, becomes a research hotspot. Most personalized federated learning research, however, focuses on data heterogeneity while ignoring the need for model architecture heterogeneity. Most existing federated learning methods uniformly set the model architecture of all clients participating in federated learning, which is inconvenient for each client’s individual model and local data distribution requirements, and also increases the risk of client model leakage. This article proposes a federated learning method based on co-training and generative adversarial networks (GANs) that allows each client to design its own model to participate in federated learning training independently without sharing any model architecture or parameter information with other clients or a center. In our experiments, the proposed method outperforms the existing methods in mean test accuracy by 42% when the client’s model architecture and data distribution vary significantly.

In this article, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of having all the data in one location, FL, which maintains the ability to learn over decentralized data sets, combines privacy and technology. Until the model converges, the server combines the updated weights obtained from each data set over a number of rounds. The majority of the literature suggested client selection techniques to accelerate convergence and boost accuracy. However, none of the existing proposals have focused on the flexibility to deploy and select clients as needed, wherever and whenever that may be. Due to the extremely dynamic surroundings, some devices are actually not available to serve as clients in FL, which affects the availability of data for learning and the applicability of the existing solution for client selection. In this article, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process. We make use of the containerization technology, such as Docker, to build efficient environments using Internet of Things and mobile devices serving as volunteers. Furthermore, Kubernetes is used for orchestration. A multiobjective optimization problem representing the client and model deployment is solved using the genetic algorithm (GA) due to its evolutionary strategy. The performed experiments using the mobile data challenge (MDC) data set and the Localfed framework illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients whenever and wherever needed with less discarded rounds and more available data.

With the development of human–machine interactions, users are increasingly evolving toward an immersion experience with multidimensional stimuli. Facing this trend, cross-modal collaborative communication is considered an effective technology in the Industrial Internet of Things (IIoT). In this article, we focus on open issues about resource reuse, pair interactivity, and user assurance in cross-modal collaborative communication to improve Quality of Service (QoS) and users’ satisfaction. Therefore, we propose a novel architecture of modal-aware resource allocation to solve these contradictions. First, taking all the characteristics of multimodal into account, we introduce network slices to visualize resource allocation, which is modeled as a Markov decision process (MDP). Second, we decompose the problem by the transformation of probabilistic constraint and Lyapunov Optimization. Third, we propose a deep reinforcement learning (DRL) decentralized method in the dynamic environment. Meanwhile, a federated DRL framework is provided to overcome the training limitations of local DRL models. Finally, numerical results demonstrate that our proposed method performs better than other decentralized methods and achieves superiority in cross-modal collaborative communications.

The phenomenal increase in the usage of Internet promotes the quality of trust in the scope of the Internet of Things (IoT). Trust is beneficial in the provision of an effective, reliable, scalable, and trustworthy environment to users of the IoT network, where they can share their private information with each other on a secure communication platform. For successful communications among the Internet users, trust is an important factor to provide them with private infrastructures and secure environments, where exchanging data among devices becomes more easy and trustworthy. Therefore, trust management is a backbone for the successful and secure transmission of data among various nodes in a large-scale IoT network. To overcome the security issues, latency, and risk of malicious activities, a lightweight approach is proposed for those nodes in Industrial IoT that cannot maintain security. LightTrust utilizes a centralized trust agent to generate and manage trust certificates that allow nodes to communicate for a specific time without performing trust computations. Trust agents also maintain a trust database to store the current trust degree for the aggregation/propagation purposes. Trust between two nodes is developed by direct observations in terms of compatibility, cooperativeness, and delivery ratio, whereas recommendations are used to develop trust in the context of indirect observations, i.e., experience or previous knowledge. The comparative simulations of the proposed and existing approaches are also performed whereby the results illustrate that the proposed approach efficiently maintains resilience and robust environments.

Federated learning (FL) enables an effective and private distributed learning process. However, it is vulnerable against several types of attacks, such as Byzantine behaviors. The first purpose of this work is to demonstrate mathematically that traditional arithmetic-averaging model-combining approach will ultimately diverge to an unstable solution in the presence of Byzantine agents. This article also proposes a low-complexity, decentralized Byzantine resilient training mechanism. The proposed technique identifies and isolates hostile nodes rather than just mitigating their impact on the global model. In addition, the suggested approach may be used alone or in conjunction with other protection techniques to provide an additional layer of security in the event of misdetection. The suggested solution is decentralized, allowing all participating nodes to jointly identify harmful individuals using a novel cross-check mechanism. To prevent biased assessments, the identification procedure is done blindly and is incorporated into the regular training process. A smart activation mechanism based on flag activation is also proposed to reduce the network overhead. Finally, general mathematical proofs combined with extensive experimental results applied in a healthcare electrocardiogram (ECG) monitoring scenario show that the proposed techniques are very efficient at accurately predicting heart problems.

With the rapid development of edge intelligence in wireless communication networks, mobile-edge networks (MENs) have been broadly discussed in academia. Supported by considerable geographical data acquisition ability of mobile Internet of Things (IoT), the MENs can also provide spatial locations-based social service to users. Therefore, suggesting reasonable points-of-interest (POIs) to users is essential to improve user experience of MENs. As the simple user-location data is usually sparse and not informative, existing literature attempted to extend feature space from two perspectives: 1) contextual patterns and 2) semantic patterns. However, previous approaches mainly focused on internal features of users, yet ignoring latent external features among them. To address this challenge, in this article, a deep distributed-learning-based POI recommendation (Deep-PR) method is proposed for situations of MENs. In particular, hidden feature components from both local and global subspaces are deeply abstracted via representative learning schemes. Besides, propagation operations are embedded to iteratively reoptimize expressions of the feature space. The successive effect of the above two aspects contributes a lot to more fine-grained feature spaces, so that a recommendation accuracy can be ensured. Two types of experiments are also carried out on three real-world data sets to assess both efficiency and stability of the proposed Deep-PR. Compared with seven typical baselines with respect to four evaluation metrics, obtained results of the overall performance of the Deep-PR are excellent.

Remote monitoring systems analyze the environment dynamics in different smart industrial applications, such as occupational health and safety, and environmental monitoring. Specifically, in Industrial Internet of Things (IIoT) systems, the huge number of devices and the expected performance put pressure on resources, such as computational, network, and device energy. Distributed training of machine and deep learning (ML/DL) models for intelligent industrial IoT applications is very challenging for resource limited devices over heterogeneous wireless networks (HetNets). Hierarchical federated learning (HFL) performs training at multiple layers offloading the tasks to nearby multiaccess edge computing (MEC) units. In this article, we propose a novel energy-efficient HFL framework enabled by wireless energy transfer (WET) and designed for heterogeneous networks with massive multiple-input–multiple-output (MIMO) wireless backhaul. Our energy-efficiency approach is formulated as a mixed-integer nonlinear programming (MINLP) problem, where we optimize the HFL device association and manage the wireless transmitted energy. However due to its high complexity, we design a heuristic resource management algorithm, namely, H2RMA, that respects energy, channel quality, and accuracy constraints, while presenting a low-computational complexity. We also improve the energy consumption of the network using an efficient device scheduling scheme. Finally, we investigate device mobility and its impact on the HFL performance. Our extensive experiments confirm the high performance of the proposed resource management approach in HFL over HetNets, in terms of training loss and grid energy costs.

Unmanned aerial vehicles (UAVs)-assisted mobile-edge computing (MEC) communication system has recently gained increasing attention. In this article, we investigate a 3-D multi-UAV trajectory optimization based on ground devices (GDs) selecting the target UAV for task computing. Specifically, we first design a 3-D dynamic multi-UAV-assisted MEC system in which GDs have real-time mobility and task update. Next, we formulate the system communication, computation, and flight energy consumption as objective functions based on fairness among UAVs. Then, to pursue fairness among UAVs, we theoretically deduce and mathematically prove the optimal GDs’ selectivity and offloading strategy, that is, how GDs select the optimal UAV for task offloading and how much to offload. While ensuring the optimal offloading strategy and GDs’ selectivity between UAVs and GDs at each step, we model UAV trajectories as a sequence of location updates of all UAVs and apply a multiagent deep deterministic policy gradient (MADDPG) algorithm to find the optimal solution. Simulation results demonstrate that we achieve the minimum energy consumption under the premise of fairness and the efficiency of model processing tasks.

With the wide application of Internet of Things and blockchain, research on smart contracts has received increased attention, and security threat detection for smart contracts is one of the main focuses. This article first introduces the common security vulnerabilities in blockchain smart contracts, and then classifies the vulnerabilities detection tools for smart contracts into six categories according to the different detection methods: 1) formal verification method; 2) symbol execution method; 3) fuzzy testing method; 4) intermediate representation method; 5) stain analysis method; and 6) deep learning method. We test 27 detection tools and analyze them from several perspectives, including the capability of detecting a smart contract version. Finally, it is concluded that most of the current vulnerability detection tools can only detect vulnerabilities in a single and old version of smart contracts. Although the deep learning method detects fewer types of smart contract vulnerabilities, it has higher detection accuracy and efficiency. Therefore, the combination of static detection methods, such as deep learning method and dynamic detection methods, including the fuzzy testing method to detect more types of vulnerabilities in multi-version smart contracts to achieve higher accuracy is a direction worthy of research in the future.

The Internet of Things (IoT) is composed of a large number of miniaturized devices interconnected through the Internet. These devices, equipped with sensing, computing, and communication capabilities, can be used to remotely control the environment or the monitored infrastructure. However, IoT devices usually only have limited resources, and thus designing a lightweight security authentication protocol for them is a challenge. This article proposes an identity authentication protocol between embedded devices and server. The protocol uses the elliptic curve encryption algorithm and realizes the anonymity of the device by hashing their IDs and prevents the server from replay attacks by adding security attributes timestamp. We prove the security of the protocol and its resistance to security attacks and also formally verify it using the AVISPA tool. In addition, through experimental comparison with existing protocols, we demonstrate the performance superiority of the proposed protocol.

In recent years, ubiquitous semantic Metaverse has been studied to revolutionize immersive cyber-virtual experiences for augmented reality (AR) and virtual reality (VR) users, which leverages advanced semantic understanding and representation to enable seamless, context-aware interactions within mixed-reality environments. This survey focuses on the intelligence and spatiotemporal characteristics of four fundamental system components in ubiquitous semantic Metaverse, i.e., artificial intelligence (AI), spatiotemporal data representation (STDR), Semantic Internet of Things (SIoT), and semantic-enhanced digital twin (SDT). We thoroughly survey the representative techniques of the four fundamental system components that enable intelligent, personalized, and context-aware interactions with typical use cases of the ubiquitous semantic Metaverse, such as remote education, work and collaboration, entertainment and socialization, healthcare, and e-commerce marketing. Furthermore, we outline the opportunities for constructing the future ubiquitous semantic Metaverse, including scalability and interoperability, privacy and security, performance measurement and standardization, as well as ethical considerations and responsible AI. Addressing those challenges is important for creating a robust, secure, and ethically sound system environment that offers engaging immersive experiences for the users and AR/VR applications.

Conventional systems are usually constrained to store data in a centralized location. This restriction has either precluded sensitive data from being shared or put its privacy on the line. Alternatively, federated learning (FL) has emerged as a promising privacy-preserving paradigm for exchanging model parameters instead of private data of Internet of Things (IoT) devices known as clients. FL trains a global model by communicating local models generated by selected clients throughout many communication rounds until ensuring high learning performance. In these settings, the FL performance highly depends on selecting the best available clients. This process is strongly related to the quality of their models and their training data. Such selection-based schemes have not been explored yet, particularly regarding participating clients having high-quality data yet with limited resources. To address these challenges, we propose in this article FedAUR, a novel approach for an adaptive upgrade of clients resources in FL. We first introduce a method to measure how a locally generated model affects and improves the global model if selected for aggregation without revealing raw data. Next, based on the significance of each client parameters and the resources of their devices, we design a selection scheme that manages and distributes available resources on the server among the appropriate subset of clients. This client selection and resource allocation problem is thus formulated as an optimization problem, where the purpose is to discover and train in each round the maximum number of samples with the highest quality in order to target the desired performance. Moreover, we present a Kubernetes-based prototype that we implemented to evaluate the performance of the proposed approach.

In the 5G/B5G network paradigms, intelligent medical devices known as the Internet of Medical Things (IoMT) have been used in the healthcare industry to monitor remote users’ health status, such as elderly monitoring, injuries, stress, and patients with chronic diseases. Since IoMT devices have limited resources, mobile edge computing (MEC) has been deployed in 5G networks to enable them to offload their tasks to the nearest computational servers for processing. However, when IoMTs are far from network coverage or the computational servers at the terrestrial MEC are overloaded/emergencies occur, these devices cannot access computing services, potentially risking the lives of patients. In this context, unmanned aerial vehicles (UAVs) are considered a prominent aerial connectivity solution for healthcare systems. In this article, we propose a multiagent federated reinforcement learning (MAFRL)-based resource allocation framework for a multi-UAV-enabled healthcare system. We formulate the computation offloading and resource allocation problems as a Markov decision process game in federated learning with multiple participants. Then, we propose an MAFRL algorithm to solve the formulated problem, minimize latency and energy consumption, and ensure the quality of service. Finally, extensive simulation results on a real-world heartbeat data set prove that the proposed MAFRL algorithm significantly minimizes the cost, preserves privacy, and improves accuracy compared to the baseline learning algorithms.

Multiaccess edge computing has emerged as a powerful paradigm for increasing the computation performance of mobile devices (MDs). Applying nonorthogonal multiple access (NOMA) to MEC can further improve the spectrum efficiency and reduce offloading delays caused by the upload congestion. In this article, we examine the joint computation offloading and resource allocation problem in the NOMA–MEC system, which benefits from the combination of NOMA and MEC. Our optimization objective is to minimize the computational overhead (the weighted sum of the execution delay and the energy consumption) in dynamic environments with time-varying wireless fading channels. The optimization problem is formulated as a mixed-integer programming (MIP), which involves jointly optimizing the task offloading decisions, channel assignment, and transmit power allocation. To solve such an optimization problem, we formalize the task offloading and the resource allocation as a Markov decision process (MDP). Then, we propose a deep reinforcement learning (DRL)-based approach, which combines multiple deep neural networks (DNNs) to directly approximate different statistical models for continuous and discrete control. The simulation results demonstrate that the proposed approach can rapidly converge and efficiently decrease the total computational overhead compared to other baseline approaches in different scenarios.

In recent years, edge computing, as an important pillar for future networks, has been developing rapidly. Task offloading is a key part of edge computing that can provide computing resources for resource-constrained devices to run computing-intensive applications, accelerate computing speed, and save energy. An efficient and feasible task offloading scheme can not only greatly improve the Quality of Experience (QoE) but also provide strong support and assistance for 5G/B5G networks, the Industrial Internet of Things (IIoT), computing networks, etc. To achieve these goals, this article proposes an adaptive edge task offloading scheme assisted by service deployment (SD-AETO) focusing on optimizing the energy utilization ratio (EUR) and the processing latency. In the preimplementation stage of the SD-AETO scheme, a service deployment scheme is invoked to assist with task offloading considering each service’s popularity. The optimal service deployment scheme is obtained by using the approximate deployment graph (AD-graph). Furthermore, a task scheduling and queue offloading design procedure is proposed to complete the SD-AETO scheme based on task priority. The task priority is generated by corresponding service popularity and task offloading. Finally, we analyze our SD-AETO scheme and compare it with related approaches, and the results show that our scheme has a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network.

Federated learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things (IoT) devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. Although several approaches have been proposed in the literature to overcome the problem of random selection, most of these approaches follow a unilateral selection strategy. In fact, they base their selection strategy on only the federated server’s side, while overlooking the interests of the client devices in the process. To overcome this problem, we present in this articleFedMint, an intelligent client selection approach for FL on IoT devices using game theory and bootstrapping mechanism. Our solution involves the design of: 1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors, such as accuracy and price; 2) intelligent matching algorithms that take into account the preferences of both parties in their design; and 3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices. We compare our approach against theVanillaFLselection process as well as other state-of-the-art approach and showcase the superiority of our proposal.

Resilient and secure ultrareliable low-latency communications (URLLCs) over radio interface is expected to play a crucial role in next-generation Industrial Internet of Things scenarios. However, attacking wireless pilot signals has been a potential easy way to interrupt URLLC services. In this work, we propose a random structured code to encode and decode pilot signals on multidimensional physical resources, and also design a quantum learning framework to make this code secure and reliable. Specifically, the code suggests using random encoding with little structures to disperse the effect of attacks. We find that the decoding process can be modeled as a computing trap if the group spatial channel features are employed. The security problem is, therefore, transformed as random computing with redundancy. We employ a quantum algorithm to learn the computing trap model such that the computing redundancy can be removed quickly while the dispersed attack can be eliminated. In this respect, we can prove the existence of the quantum black-box model corresponding to the computing trap, and derive a precise expression of computing performance. Based on the result, we can formulate novel analytical closed-form expressions of system failure probability to characterize the reliability of the URLLC. Numerical results show that the proposed system can maintain ultrahigh reliability and low latency against attacks on wireless pilots.

Traditional terrestrial wireless communication networks cannot support the requirements for high-quality services for artificial intelligence applications such as smart cities. The space–air–ground-integrated network (SAGIN) could provide a solution to address this challenge. However, SAGIN is heterogeneous, time-varying, and multidimensional information sources, making it difficult for traditional network architectures to support resource allocation in large-scale complex network environments. This article proposes a service provision method based on service function chaining (SFC) to solve this problem. Network function virtualization (NFV) is essential for efficient resource allocation in SAGIN to meet the resource requirements of user service requests. We propose a federated learning (FL)-based algorithm to solve the embedding problem of SFCs in SAGIN. The algorithm considers different characteristics of nodes and resource load to balance resource consumption. Then, an SFC scheduling mechanism is proposed that allows SFC reconfiguration to reduce the service blocking rate. Simulation results show that our proposed FL-VNFE algorithm is more advantageous compared to other algorithms, with 12.9%, 2.52%, and 10.5% improvement in long-term average revenue, acceptance rate, and long-term average revenue–cost ratio, respectively.

Facing the explosive growth of data, the introduction of cloud computing in the Space-Air-Ground Integrated Internet of Things (SAGIIoT) can solve the problem of limited computing power of the terminals. At the same time, data security on the cloud is also a focus that cannot be ignored. Secure outsourcing computing is helpful in improving privacy preserving. Due to the wide applicability of$K $-means clustering, outsourcing computing for$K $-means has become a major research hotspot in industry and academia. Most of the existing work on outsourcing$K $-means clustering is based on homomorphic encryption, which has a high computational overhead due to the mathematical puzzles’ nature of homomorphic encryption. In addition, the high computational overhead of designing a verification algorithm based on homomorphic encryption is unacceptable. To address the above issues, we design a${K}$-means clustering outsourcing algorithm by sparse matrix transformation, which can verify the deceptive behavior of cloud while achieving high efficiency. In this article, we theoretically prove the accuracy, security, efficiency, and verifiability of the proposed algorithm. Extensive experiments indicate that our algorithm is efficient.

Digital twins (DTs), defined as the virtual representation of a real-world entity or system, act as a mirror to provide a way to simulate, predict physical behaviors, and possibly control the real-world entity where applicable. Originating in the industry, advances in computing capacity and recent progress in artificial intelligence (AI)-based analytics make DTs attractive to a broader set of use cases including mobile networks.

6G communication networks are expected to become an integral part of the infrastructure needed for developing a smart society in the future. Addressing the challenges on the road towards realizing 6G network requirements in terms of quality of service, user experience, and security, is therefore of utmost importance. The digital twin (DT) technology can potentially improve the efficiency, reliability, and security of 6G networks. Digital twins for mobile networks (DTMNs) are seen as a key factor in harnessing the full benefits of 6G. Using digital twins can help address several problems, including network optimization, fault diagnosis, and fault management. Furthermore, DTMNs can characterize the physical entities in a 6G network and their relationships to each other, build their virtual models, and use simulation, learning, and reasoning capabilities to make predictions and support informed decision-making,

The past few years have witnessed an exponential growth of diverse Internet of Things (IoT) devices as well as compelling applications ranging from industrial production to medical care. Dramatic advances in IoT technology not only brought enormous economic opportunities but also challenges (e.g., privacy and security vulnerabilities). Recently, with the appearance of blockchain technology, the integration of IoT and blockchain (BCoT) is considered a promising solution to address these issues. Blockchain provides a secure and scalable data management framework for IoT devices. However, the huge computation and energy cost of the consensus process in blockchain prevents it from being directly applied as a generic platform. To overcome this challenge, in this article, we propose a cloud mining pool-aided BCoT architecture, where the IoT devices can rent the computing resources from the cloud mining pools to offload the mining process. Based on this architecture, we study the mining pool selection problem and analyze the colony behaviors of IoT devices with different pooling strategies. We propose a centralized evolutionary game-based pool selection algorithm for the sake of maximizing the system utility. Considering the non-cooperative relationship among multiple miners, we also propose a lightweight distributed reinforcement learning algorithm, named the ‘WoLF-PHC’ algorithm.

In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted frequency-modulated (FM) differential chaos shift keying (DCSK) scheme with simultaneous wireless information and power transfer (SWIPT), called RIS-FM-DCSK-SWIPT scheme, for low-power, low-cost, and high-reliability wireless communication networks. In particular, the proposed scheme is developed under a non-linear energy-harvesting (EH) model which can accurately characterize the practical situation. The proposed RIS-FM-DCSK-SWIPT scheme has an appealing feature that it does not require channel state information, thus avoiding the complex channel estimation. We further derive the closed-form theoretical expressions for the energy shortage probability and bit error rate (BER) of the proposed scheme over the multipath Rayleigh fading channel. In addition, we investigate the influence of key parameters on the performance of the proposed transmission scheme in two different scenarios, i.e., RIS-assisted access point (RIS-AP) and dual-hop communication (RIS-DH). Finally, we carry out various Monte-Carlo experiments to verify the accuracy of the theoretical derivation, illustrate the performance advantage of the proposed scheme, and give some design insights for future study.

To achieve reliable and efficient transmissions in free-space optical (FSO) communication, this paper designs a new protograph low-density parity-check (PLDPC) coded generalized spatial multipulse position modulation (GSMPPM) system over weak turbulence channels. Specifically, we investigate the PLDPC code, generalized space shift keying (GSSK) modulation, and MPPM constellation. First, we propose a type of novel GSMPPM constellations that intelligently integrates the GSSK into MPPM, referred to as asymmetric dual-mode (ADM) constellations, so as to improve the performance of the PLDPC-coded GSMPPM system. Furthermore, exploiting a protograph extrinsic information transfer (PEXIT) algorithm, we construct a type of improved PLDPC code, referred to as I-PLDPC code, which outperforms the existing PLDPC codes over weak turbulence channels. Analytical and simulation results show that the proposed ADM constellations and the proposed I-PLDPC code can obtain noticeable performance gains over their counterparts. Therefore, the proposed PLDPC-coded GSMPPM system with ADM constellations is competent to satisfy the high-reliability requirement for FSO applications.

The emergence of new communication applications adds high heterogeneity to 5G-networks. With the increase of heterogeneity, handover of user equipment between different service HetNets is frequent. It must smoothly realize user-free switching to provide services continuously. Although the 3 rd Generation Partnership Project (3GPP) has proposed a standard protocol for this scenario, it is found that these protocols cannot satisfy key forward/backward secrecy, lacks mutual authentication, etc. Further, it can be subjected to replay, DoS and other attacks. To alleviate these problems, we propose a mask random array protocol, MRSA. For efficient, secure handover authentication in 5G HetNets, we first design a verification mechanism called mask array, which depends on a random number self-circulating encryption structure. The mechanism can not only check the identity of the communication entity but also evaluate the freshness of the message. Second, we devise the mask array-based key derivation method to ensure the whole mechanism's key security. Third, formal proof and automated analysis are established to verify the efficiency and safety of the proposed MRSA protocol. Finally, function and robustness analysis illustrate the ability to resist attacks, while the simulation base station communication analysis shows the efficiency of the protocol from three aspects of data, time and energy. MRSA has significant performance advantages compared to existing schemes in 5G HetNets.

Fog computing-based Internet of Things (IoT) architecture is useful for various types of delay efficient network communications and services, like digital healthcare. However, there are privacy and security issues with the fog computing-based healthcare systems, which can further increase the risk of leakage of sensitive healthcare data. Therefore, a security mechanism, such as access control for fog computing-based healthcare systems, is needed to protect its data against various potential attacks. Moreover, the blockchain technology can be used to solve the digital healthcare’s data integrity related problems. The use of Artificial Intelligence (AI) further makes the system more effective in case of prediction of health related diseases. In this paper, an AI-enabled secure communication mechanism in fog computing-based healthcare system (in short, AISCM-FH) has been proposed. The security analysis of the proposed AISCM-FH is provided using the standard random oracle model and also with the heuristic (non-mathematical) security analysis. A pragmatic study determines the impact of the proposed AISCM-FH on key performance indicators. Moreover, we include a detailed performance comparison of AISCM-FH with other relevant existing schemes to show that it has low communication and computation costs, and provides superior security and extra functionality attributes as compared to those for other competing existing approaches.

The advancement of wireless connectivity in smart cities will enhance connections between their various key elements. Federated intelligent health monitoring systems inside autonomous vehicles will achieve smart cities’ goal of improving the quality of life. This paper proposes a novel cooperative health emergency response system within Cooperative Intelligent Transportation Environment, namely, C-HealthIER. C-HealthIER is a cooperative health intelligent emergency response system that aims to reduce the time of receiving the first emergency treatment for passengers with abnormal health conditions. C-HealthIER continuously monitors passengers’ health and conducts cooperative behavior in response to health emergencies by vehicle-to-vehicle and vehicle-to-infrastructure information sharing to find the nearest treatment provider. A conducted simulation that integrates three different tools (Veins, SUMO, and OMNET++) to simulate the proposed system showed that C-HealthIER reduces the total time to receive the emergency treatment by at least 92.5% and the time to receive the first emergency treatment by at least 73.2% compared to the time taken by AutoPilot mode in self-driving cars. C-HealthIER also reduces the travel distance to the first emergency treatment place by 40.9% and thus reduces the travel time by 43.8% compared to receiving the treatment at the same hospital in the AutoPilot mode.

Technological interventions are becoming commonplace in everyday vehicles. But utilization of biosignals that can enhance the overall driving experience is still limited. Microsleep is one such issue that needs intervention, owing to the difficulty in its detection and social acceptance of using wearable BCI devices during transit. Microsleep is a short duration of sleep that lasts from few to several seconds. It could occur unconsciously without the person in context realizing it. This, therefore, happens before the deep sleep and could also occur when performing critical tasks such as driving on a highway. By using modern-day advancements in Internet of Things (IoT) and Machine Learning, we can provide efficient solutions to prevent accidents due to microsleep during vehicular transit. However, it is noteworthy that distinguishing microsleep using a single channel system is a challenge. We have explored this using datasets provided by International BCI Competition Committee. Given the fact that the participants’ values might not match the exact scenario, approaches for exploiting transitory phases using ANN/CNN have been developed and discussed in this paper. Transitory phases could include Wakefulness$\leftrightarrow $Non-Rapid Eye Movement-1 phase (NREM-1). Results show ≈95% increase in mean statistical agreements, which are represented by kappa values (CNN NREM$1~\rightarrow $CNN Transition) and ≈77% increase in mean kappa (ANN NREM$1~\rightarrow $ANN Transition). Hence, this work gives an initial indication whether classifiers trained on night sleep data can be used for microsleep detection in more real-world scenarios.

Device positioning has generally been recognized as an enabling technology for numerous vehicular applications in intelligent transportation systems (ITS). The downlink time difference of arrival (DL-TDOA) technique in cellular networks requires range information of geographically diverse base stations (BSs) to be measured by user equipment (UE) through the positioning reference signal (PRS). However, inter-cell interference from surrounding BSs can be particularly serious under poor network planning or dense deployments. This may lead to a relatively longer measurement time to locate the UE, causing an unacceptable location update rate to time-sensitive applications. In this case, PRS muting of certain wireless resources has been envisioned as a promising solution to increase the detectability of a weak BS. In this paper, to reduce UE measurement latency while ensuring high location accuracy, we propose a muting strategy managed by positioning functions that utilizes a combination of optimized pseudo-random sequences (CO-PRS) for multiple BSs to coordinate the muting of PRS resources. The original sequence is first truncated according to the muting period, and a modified greedy selection is performed to form a set of control sequences as the muting configurations (MC) with balance and concurrency constraints. Moreover, efficient information exchange can be achieved with the seeds used for regenerating the MC. Extensive simulations demonstrate that the proposed scheme outperforms the conventional random and ideal muting benchmarks in terms of measurement latency by about 30%, especially when dealing with severe near-far problems in cellular networks.

In the scenario of limited energy supply, Unmanned Aerial Vehicles (UAVs) enabled communication systems must make efficient use of energy in order to provide long-term service. In this paper, we propose a global energy efficiency maximization (GREEN) strategy for multi-UAV enabled communication systems. In such systems, a group of UAVs communicates with their associated ground terminals (GTs) by using a UAV-enabled interference channel (UAV-IC). In particular, we optimize the UAVs' trajectory control by jointly considering both the communication throughput and the total energy consumption of the whole system. We aim to maximize the global energy efficiency (GEE) of a task for multi-UAV communications, in which the problem is challenging to optimally solve due to its non-convex nature and strongly coupled variables. To tackle this problem, first, we investigate and propose a global energy-efficient optimization problem based on the fly-hover-communicate protocol. Second, we extend our proposed solution from the single UAV-enabled system to multiple UAV-GT pairs cases. In addition, we consider the general scenario in which the UAVs also communicate while flying. Based on the successive convex approximation technique and the path discretization method, the GREEN strategy is designed for optimizing UAV trajectories in this scenario. The simulation results show that the proposed strategy can achieve significantly higher GEE than the benchmark schemes for multi-UAV enabled communications.

The emergence of Digital Twin Edge Networks (DTENs) achieves the mapping of real physical entities to digital models of cyberspace. By offloading real-time mobile data to Mobile Edge Computing (MEC) servers for processing and modeling, communication-efficient Digital Twin (DT) services could be achieved. However, the spatio-temporal dynamic DT service demand stochastically generated by mobile users easily causes service congestion, which challenges the long-term DT service stability. Meanwhile, current DT services still lack long-term effective incentive designs for participants. To solve these issues, we design an incentive-based congestion control scheme for stochastic demand response in DTENs. First, we adopt the Lyapunov optimization theory to decompose the long-term congestion control decision into a sequence of online edge association decisions, with no need for future system information. We then present a contract-based incentive design to optimize the long-term profit of the DT service provider, comprehensively considering the delay sensitivity, incentive compatibility, and individual rationality. Finally, experimental simulations are carried out to verify the superiority of the proposed scheme with the base station dataset of Shanghai Telecom. Theoretical and simulation analysis demonstrates that compared with benchmarks, our scheme could effectively avoid long-term service congestion with an arbitrarily near-optimal profit.

Timely and accurate cellular traffic prediction is difficult to achieve due to the complex spatial-temporal characteristics of cellular traffic. The latest approaches mainly aim to model local spatial-temporal dependencies of cellular traffic based on deep learning techniques but lack the consideration of diverse global spatial-temporal correlations hidden in cellular traffic. To tackle this issue, we propose a novel multi-view spatial-temporal graph network (MVSTGN), which combines attention and convolution mechanisms into traffic pattern analysis, enabling the comprehensive excavation of spatial-temporal characteristics. Specifically, the MVSTGN realizes the above statement from three spatial-temporal views: 1) From a global spatial view, two spatial attention modules are proposed to capture the global spatial correlations between different regions at node and trend levels; 2) From a global temporal view, a temporal attention module is employed to capture and encode global temporal correlations between traffic at different times; 3) From a local spatial-temporal view, a dense convolution module is developed to further excavate the local spatial-temporal dependencies in cellular traffic. Consequently, a successful cellular traffic prediction strategy is constructed to fully explore the spatial-temporal characteristics from multiple views. The experimental results on a popular real-world cellular traffic dataset demonstrate that the MVSTGN achieves obvious improvements over baselines.

Software-Defined Vehicular Network (SDVN) is a networking architecture that can provide centralized control for vehicular networks. However, the design for routing policies in SDVNs is generally influenced by several limitations, such as frequent topological changes, complex service requests, and long model training time. Intelligent Digital Twin-based Software-Defined Vehicular Networks (IDT-SDVN) can overcome these weaknesses and maximize the advantages of the conventional SDVN architecture by enabling the controller to construct virtual network spaces and provide virtual instances of corresponding physical objects within the Digital Twin (DT). In this paper, we propose a junction-based hierarchical routing scheme in IDT-SDVN, namely, intelligent digital twin hierarchical (ELITE) routing. The proposed scheme is conducted in four phases: policy training and generation in the virtual network, and deployment and relay selection in physical networks. First, the policy learning phase employs several parallel agents in DT networks and derives multiple single-target policies. Second, the generation phase combines the learned policies and generates new policies based on complex communication requirements. Third, the deployment phase selects the most suitable generated policy according to the real-time network status and message types. A road path is calculated by the controller based on the selected policy and then sent to the requester vehicle. Finally, the relay selection phase is utilized to determine relay vehicles in a hop-by-hop process along the selected path. Simulation results demonstrate that ELITE achieves substantial improvements in terms of packet delivery ratio, end-to-end delay, and communication overhead compared with its counterparts.

5G-and-beyond networks are designed to fulfill the communication and computation requirements of various industries, which requires not only transporting the data, but also processing them to meet/address diverse key performance indicators (KPIs). Network Function Virtualization (NFV) has emerged to enable this vision by: (i) collecting the requirements of diverse services, using graphs of Virtual Network Functions (VNFs); and (ii) mapping these requirements into network management decisions. Because of the latter, we need to efficiently allocate computing and network resources to support the desired services, and because of the former such decisions must be jointly optimized considering all KPIs associated with supported services. Thus, this paper proposes an optimized, intelligent network slicing framework to maintain a high performance of network operation by supporting diverse and heterogeneous services, while meeting new KPIs, e.g., reliability, energy consumption, and data quality. Different from the existing works, which are mainly designed considering traditional metrics like throughput and latency, we present a novel methodology and resource allocation schemes that enable high-quality selection of radio points of access, VNF placement and data routing, as well as data compression ratios, from the end users to the cloud. Our results depict the efficiency of the proposed framework in enhancing the network performance when compared to baseline approaches that consider partial network view or fair resource allocation.

With the Industrial Internet of Things (IIoT), mobile devices (MDs) and their demands for low-latency data communication are increasing. Due to the limited resources of MDs, such as energy, computation, storage, and bandwidth, IIoT systems cannot meet MDs’ quality of service (QoS) and security requirements. Recently, UAVs have been deployed as aerial base stations in the IIoT network to provide connectivity and share resources with MDs. We consider a resource trading environment where multiple resource providers compete to sell their resources to MDs and maximize their profit by continually adjusting their pricing strategies. Multiple MDs, on the other hand, interact with the environment to make purchasing decisions based on the prices set by resource providers to reduce costs and improve QoS. We propose a novel intelligent resource trading framework that integrates multi-agent deep reinforcement Learning (MADRL), blockchain, and game theory to manage dynamic resource trading environments. A consortium blockchain with a smart contract is deployed to ensure the security and privacy of the resource transactions. We formulated the optimization problem using a Stackelberg game. However, the formulated optimization problem in the multi-agent IIoT environment is complex and dynamic, making it difficult to solve directly. Thus, we transform it into a stochastic game to solve the dynamics of the optimization problem. We propose a dynamic pricing algorithm that combines the Stackelberg game with the MADRL algorithm to solve the formulated stochastic game. The simulation results show that our proposed scheme outperforms others to improve resource trading in UAV-assisted IIoT networks.

With the growing demand for openness, scalability, and granularity, mobile network function virtualization (NFV) has emerged as a key enabler for the most of mobile network operators. NFV decouples network functions from hardware devices. This decoupling allows network services, called Virtualized Network Functions (VNFs), to be hosted on commodity hardware which simplifies and enhances service deployment and management for providers, improves flexibility, and leads to efficient and scalable resource usage, and lower costs. The proper placement of VNFs in the hosting infrastructures is one of the main technical challenges. This placement significantly influences the network’s performance, reliability, and operating costs. The VNF placement is NP-Hard. Therefore, there is a need for placement methods that can cope with the complexity of the problem and find appropriate solutions in a reasonable duration. The primary purpose of this study is to provide a taxonomy of optimization techniques used to tackle the VNF placement problems. We classify the studied papers based on performance metrics, methods, algorithms, and environment. Virtualization is not limited to simply replacing physical machines with virtual machines or VNFs, but may also include micro-services, containers, and cloud-native systems. In this context, the second part of our article focuses on the placement of Containers Network Functions (CNFs) in edge/fog computing. Many issues have been considered as traffic congestion, resource utilization, energy consumption, performance degradation, etc. For each matter, various solutions are proposed through different surveys and research papers in which each one addresses the placement problem in a specific manner by suggesting single objective or multi-objective methods based on different types of algorithms such as heuristic, meta-heuristic, and machine learning algorithms.

Security and privacy concerns keep growing with the successful development of Internet of Things (IoT) and the booming deployment of smart homes. IoT devices are utilized cooperatively to enable the interactions between home surroundings and users’ daily lives, containing forensically-valuable information about what happens in smart homes, which can help introduce digital forensics into smart homes to alleviate the growing concerns. However, current IoT devices, apps, and platforms usually do not provide built-in capabilities for digital forensics. To overcome this limitation, we propose a non-intrusive solution (i.e., bringing no modification to IoT devices, apps, and platforms) of digital forensic service to provide Forensics-as-a-Service (FaaS) for smart homes. First, it leverages side-channel analysis on sniffed network traffic to monitor commands, actions, and states of IoT devices. Then, it introduces provenance graphs (i.e., causal graphs) for smart home modeling to provide a holistic and overall explanation of smart homes. Machine learning (ML) techniques are applied to overcome the deficiency of a non-intrusive solution as it suffers from challenges in data collection and smart home modeling. Finally, it conducts forensic analysis based on scalable, reusable policies that are designed for graph-based smart home modeling. We implement a prototype of our forensic service and evaluate it in a real-world smart home. The evaluation results show that our forensic service can effectively collect forensic data for smart home modeling and conduct forensic analysis to explain security risks in smart homes.

The future needs of the telecommunication system lie in deploying a heterogeneous ultra-dense network with varied topographical use cases. However, this increase in ultra-denseness in 5g and beyond poses several challenges in resource allocation, requiring an accurate learning-based prediction. This paper proposes a novel framework using Federated Learning (FL) and Distributed Machine Learning (DML) for Mobile Edge based resource provisioning to User Equipment (UEs). This work formulates the correlation-based novel procedures between UEs in applying Federated and Distributed Machine Learning through Kolmogorov tests for predicting SNR. The correlations of the distribution obtained through the Kolmogorov test check the extent of Independent and Identically Distributed (IID) - ness between modelled data and evaluate the global model for resource provisioning accuracy. Further, correlation-based DML is also employed to balance the computational load of a mobile edge, which acts as a small cell base station and a computational node. In this approach, we account for correlation-based resource predictive model training to balance the uniform computational load by data distribution methods among the neighbouring mobile Edge SCBS nodes for computation. Together with both DML and FL, we create a novel Framework for resource prediction with minimal time for achieving high accuracy without over-fitting.

With the accelerated popularization of 5G applications, accurate cellular traffic prediction is becoming increasingly important for efficient network management. Currently, the latest algorithms for cellular traffic prediction generally neglect extraction of the shallow features of cellular traffic and the prediction accuracy is hence limited. Therefore, we propose a global-local spatial-temporal transformer network (GLSTTN) that can fully excavate diverse spatial-temporal characteristics of cellular traffic for accurate cellular traffic prediction. Specifically, GLSTTN achieves this goal by constructing two modules: the global spatial-temporal module and the local spatial-temporal module. In the global spatial-temporal module, GLSTTN captures global correlations using stacked spatial-temporal blocks, where each block is composed of one spatial transformer and one temporal transformer. A skip connection is then used in each block to strengthen feature propagation. In the local spatial-temporal module, GLSTTN fully extracts the local spatial-temporal dependencies hidden in globally encoded features using densely connected convolutional neural networks. Extensive experiments demonstrate that GLSTTN achieves more accurate cellular traffic prediction than existing approaches on a real-world cellular traffic dataset.

The number of IoT devices is growing rapidly, and the interaction between devices and servers is also more frequent. However, IoT devices are often at the edge of the network, which leads their communications with the server to be completely exposed, making it more vulnerable to attacks. Moreover, IoT devices have limited energy and computational resources. Therefore, we propose in this paper a lightweight authentication and key exchange protocol with anonymity for IoT devices. The proposed scheme supports mutual authentication between IoT devices and the server. We verify the security of the protocol through formal and informal analyses. Finally, we compare security and performance with other protocols, which shows that our protocol has the advantages of being lightweight and secure.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is a promising technology to provide computational services to ground terminal devices (TDs) in remote areas or for emergency incidents with its flexibility and mobility. This paper aims to maximize the UAV’s energy efficiency while considering the fairness of offloading. We formulate this optimization problem by jointly considering the UAV flight time, the UAV 3D trajectory, the TD binary offloading decisions, and the time allocated to TDs, which is a mixed-integer nonlinear programming problem. The problem is transformed into two sub-problems and we propose a PDDQNLP (parametrized dueling deep Q-network and linear programming) algorithm based on the combination of deep reinforcement learning (DRL) and linear programming (LP) to address them. For the first sub-problem, a DRL-based algorithm is used to optimize the TD offloading decisions, the UAV trajectory, and the UAV flight time. The action space is hybrid that contains discrete actions (e.g., binary offloading) and continuous actions (e.g., UAV flight time). Therefore, we parameterize the action space and propose the PDDQN algorithm, which combines the DDPG algorithm for handling the continuous action space and the Dueling DQN algorithm for handling the discrete action space. According to the solution obtained from the first sub-problem, the LP is used to adjust the time allocated to TDs in the second sub-problem. The numerical results show that the PDDQNLP algorithm outperforms its counterparts.

In Internet of Things (IoT)-based e-Health Systems (IoTEHS), medical devices form a large network that continuously sense and share the healthcare data with the nearby edge devices or cloud servers. The health data is subsequently made available to various IoTEHS stakeholders (such as doctors, nurses and patients) to track and monitor patients under observation. However, the entire IoTEHS stakeholders communicate with each other over a wireless unsecured public communication channel. This is a major security and privacy loophole wherein the attacker can exploit the vulnerability of the system and can launch various attacks on the ongoing communication. Motivated by the aforementioned challenges, a secure data dissemination scheme using AI and blockchain is proposed. In this scheme, the transaction collected through healthcare sensors installed around the patients premises act as data sets that is forwarded to the nearby edge devices. The collected data is first filtered using AI-based intrusion detection system located at the edge of the network. Second, a secure health monitoring network is designed using blockchain. Specifically, the filtered or normal transactions are transmitted to centralized cloud servers where the smart contact-enabled consensus mechanism is used to validate the transactions. Once the transaction gets validated, it is stored on distributed InterPlanetary File System (IPFS) of cloud and returned transaction hash is stored on the blockchain ledger located at edge devices making data exchange faster. The detailed experimental investigation demonstrates that the proposed schemes are efficient (in terms of computing and processing time) as well as its resistance to a variety of security attacks.

Federated Learning (FL) is a type of Machine Learning (ML) technique in which only learned models are stored on a server to sustain data security. The approach does not gather server-side data but rather directly shares only the models from scattered clients. Due to the fact that clients of FL frequently have restricted connection bandwidth, it is necessary to optimize the communication between servers and clients. FL clients frequently interact through Wi-Fi and must operate in uncertain network situations. Nevertheless, the enormous number of weights transmitted and received by existing FL aggregation techniques dramatically degrade the accuracy in unstable network situations. We propose a federated GWO (FedGWO) algorithm to reduce data communications. The proposed approach improves the performance under unstable network conditions by transferring score principles rather than all client models' weights. We achieve a 13.55% average improvement in the global model's accuracy while decreasing the data capacity required for network communication. Moreover, we show that FedGWO achieves a 5% reduction in accuracy loss compared to FedAvg and Federated Particle Swarm Optimization (FedPSO) methods when tested on unstable networks.

Mobile Cloud Computing (MCC) also known as on-demand computing uses cloud computing to deliver applications to mobile devices. This new computational paradigm model which plays a big part in the Internet of Things (IoT), has increased its popularity even more during Covid-19 pandemic and became a necessity when schools, businesses and hospitals must work remotely. We can access and process remote data which are stored over the cloud server in real-time by connecting to a wireless network. For accessing any cloud server, a mutual authentication and key agreement between a mobile user and a cloud server provider is required. However, existing authentication schemes for MCC fail to provide user anonymity, server anonymity and user untraceability. Therefore, we propose a Lightweight Authentication Scheme with User Anonymity (LASUA) which artfully employs Elliptic Curve Cryptography (ECC), random number, time stamps, one-way hash functions, concatenation, XOR operations and fuzzy extractor for biometric to enable various security features including anonymity and resistance against various attacks. LASUA utilises the hardness of ECC to provide top-notch security with low computation and communication cost, a perfect solution for resource constrained devices.

Wireless enabling technologies in critical infrastructures are increasing the efficiency of communications. Most of these technologies are vulnerable to jamming attacks. Jamming attacks are among the most effective countermeasures to attack and compromise their availability. Jamming is an interfering signal that limits the intended receiver from correctly receiving the messages. Localizing a jammer deployed by the adversary in wireless sensor networks becomes difficult, if not impossible, due to the inaccessibility of the affected sensors in the network. This paper proposes an effective yet efficient jammer localization scheme where battery-free Radio-Frequency Identification (RFID) sensor tags harvest the energy from the signal emitted by a powerful jammer. We compute the distance and estimate the actual jammer location based on the power received at each energy-harvesting node. We conduct extensive simulations campaign to test and illustrate the effectiveness of the proposed scheme. Finally, we demonstrate the possibility of deploying the proposed scheme with off-shelf equipment and consuming only 0.2175 mJ,

Dispatching and control information freshness conducts an important impact on the training accuracy of distributed energy dispatching and control model. Poor information freshness will increase the loss function of the training model, and reduce the reliability and economy of dispatching and control. Simplified power internet of things can provide plug-and-play and multi- mode fusion communication support, but it still faces challenges of the coupling of model training and data transmission as well as the difficulty in guaranteeing dispatching and control information freshness. In this paper, a semi-distributed federated learning- based framework for dispatching and control model training decision-making is proposed, and a dispatChing and control informAtion fReshness-aware batch size Optimization aLgorithm (CAROL) is presented. CAROL leverages deep Q network and dispatching and control information freshness awareness to learn the batch size optimization strategy. CAROL can minimize model loss function while guaranteeing long-term dispatching and control information freshness constraints. Compared with existing feder- ated learning algorithms, CAROL achieves superior performance in global loss function and information freshness.

Multi-mode power internet of things (PIoT) provides spatio-temporal coverage for low-carbon operation in smart park through combining various communication media. Heterogeneous resources are dynamically and intelligently managed to improve resource utilization and achieve anti-eavesdropping. However, resource management in multi-mode power IoT confronts challenges such as the mutual contradiction in joint communication and security quality of service (QoS) guarantee and the inadaptability to low-carbon services. In this paper, we propose an Adaptive learNing-based secure and enerGy-awarE resource management aLgorithm (ANGEL) to optimize multi-mode channel selection and power splitting for artificial noise (AN)-based anti-eavesdropping. Based on deep actor-critic (DAC) and “win or learn fast (WoLF)” mechanism, ANGEL can realize multi-attribute QoS guarantee, adaptive resource management, and security enhancement. Simulation results demonstrate its superior performance in energy consumption, secrecy capacity, and adaptability to differentiated low-carbon services.

In 6G networks, unmanned aerial vehicles (UAVs) can serve as aerial flying base stations (AFBS) with aerial mobile edge computing (AMEC) server capabilities. AFBS is an increasingly popular solution for delivering time-sensitive applications, extending network coverage, and assisting ground base stations in the healthcare systems for remote areas with limited infrastructure. Furthermore, the UAVs are deployed in the healthcare system to support the Internet of medical things (IoMT) devices in data collection, medical equipment distribution, and providing smart services. However, ensuring the privacy and security of patients' data with the limited UAV resources is a major challenge. In this paper, we present a federated deep reinforcement learning framework for resource allocation in UAV-enabled healthcare systems, where IoMT devices send their trained model parameters without transmitting sensitive raw data to the AMEC server. In the proposed framework, the IoMT device is associated with AFBS based on the quality of the data and its demand in order to maximize learning efficiency and accuracy. This work aims to minimize the computation costs of the IoMT devices while considering UAV resources and the fairness of UAV coverage. Simulation results prove that our proposed algorithm outperforms other baseline algorithms in learning accuracy and computational cost.

With the widespread use of mobile devices, security and privacy concerns have been attracting increasing attention. However, it is challenging for Android users to apply the appropriate settings to protect their devices. Therefore, a cloud-based solution is proposed in this paper, which works based on crowdsourcing to generate recommended security and privacy settings for Android users. Following the suggestions of security experts, ordinary users can readily understand how to properly set up their devices for security enforcement. Our solution collects security-related data from experts, and a weight-based algorithm is introduced to determine data priority and credibility. Finally, the recommended security and privacy settings are created based on these data. The evaluation results demonstrate that our solution can effectively improve the robustness and reliability of Android devices.

Recently, IoT devices have become affordable to support various types of applications which have encouraged their usability in data collection, behavior tracking, and pattern analysis to gain knowledge to achieve certain goals. The Internet of Behavior (IoB) allows organizations and individuals to achieve all of this simultaneously. Various technologies and approaches can be used to support IoB systems to operate efficiently, such as 6G networks and decentralized systems structure that support IoT-based systems to distribute operations across devices and influence each device individually. Therefore, this paper proposes a decentralized IoB framework for achieving energy sustainability by tracking, analyzing, and influencing IoT devices’ behavior. The collected results from an extensive decentralized IoB electrical power consumption experiment show that the decentralized system achieved higher accuracy compared to the centralized system, thus sending 3.5% fewer alerts and saving 3.4% more power for 3 sub-meters over a period of 500 hours.

General Purpose Vision System (GPVS) is a task-agnostic vision-language system that inputs an image and a question from which the system recognizes the tasks to be performed and outputs bounding boxes, confidence scores, and text outputs to answer the question. While much attention to GPVS has been recently given in the computer vision field, its medical field applications are still in their infancy. This paper presents MED-GPVS, a customized deep learning-based GPVS on biomedical images to perform various vision tasks, such as object detection and visual question answering, on medical images to facilitate precision medicine/e-health services. Our envisioned MED-GPVS takes an image and a natural language text as inputs, and then outputs bounding boxes, confidence scores, and generates a caption (i.e., the answer to the posed query). For example, if a medical image of a patient’s abdomen is presented to MED-GPVS followed by the question: "does the picture contain stomach?", MED-GPVS should ideally provide the answer "yes" along with a prediction box and prediction score on the image. We utilize the multilingual SLAKE dataset, which was annotated by expert physicians with a full semantic label, to validate the performance of MED-GPVS under various scenarios involving different biomedical image-based diagnoses. For the visual question answering (VQA) task, MED-GPVS demonstrates encouraging performance with significantly high accuracy of 82.41%.

During the recent years, online users, particularly in social networks, have witnessed an upsurge in racism, sexism, and other types of aggressive and cyberbully content, which are often manifested through offensive, abusive, or hateful speech and harassment. This can lead to severe physical and psychological stress in young children and adolescents, leading to even suicides and negatively affecting social policies. Therefore, there is a significant need to identify and regulate harassing content posted on the Internet in a smart, automated, and accurate manner. With this aim, in this paper, we design and develop a hierarchical framework comprising machine learning algorithms in order of higher computational complexity to adaptatively switch among them for efficiently detecting hateful and abusive content. We combine simple machine learning models such as Naive Bayes/Logistic Regression classifiers with customized calibration and Expectation-Maximization (EM) algorithms, and compare them with the much stronger deep learning techniques. Our proposed hierarchical framework demonstrates a significant improvement of the automated detection of abusive contents in social networks with a relatively small twitter dataset in contrast with the deep learning-based counterpart, namely the Bidirectional Encoder Representations from Transformers (BERT) model, training of which typically requires a much higher volume of labeled documents to detect abusive comments.

Federated Learning (FL) has attracted a lot of attention in numerous applications due to recent data privacy regulations and increased awareness about data handling issues, combined with the ever-increasing big-data sizes. This paper proposes a server-less, robust FL training mechanism that allows any set of participating data-owners to train a neural network (NN) model collaboratively without the assistance of any central node and while being resilient to Byzantine attacks. The proposed approach makes use of a dual-way update mechanism to allow each node to take a model forwarding decision towards a global collaborative decision of isolating any malicious updates. The efficiency of the proposed approach in detecting cardiac irregularities is verified using simulation results conducted based on the Physikalisch-Technische Bundesanstalt Database electro-cardiogram (PTBDB ECG) dataset.

With the vehicle-to-grid and computing capabilities, a parked electric vehicle (EV) has a dual role, namely being an energy prosumer as well as a computing node for accommodating computation-offloading services. This dual-role feature of EVs yields a new computing paradigm named Electric Vehicle Edge Computing (EVEC). To ease the implementation of EVEC, we propose a fine-grained EV management approach to jointly provide parking guidance for EVs and control their charging/discharging power in parking lots. We formulate a bilevel optimization problem where the top-level problem optimizes the matching between EVs and parking lots from the perspective of computation offloading, and the bottom-level problem optimizes the control of EV charging/discharging power from the view of power networks. We transform the bilevel optimization problem into a single-level form, which is a nonconvex mixed-integer nonlinear programming problem, and we further tackle it by linearization techniques. Finally, we provide numerical results to demonstrate the efficiency and effectiveness of our approach.

In this paper, we propose a new ultra-wideband (UWB)-based simultaneous localization and wall-mapping (SLAM) system, which adopts two-way ranging optimization on UWB anchor and tag nodes to track the target's real-time movement in an unknown area. The proposed UWB-based SLAM system captures time difference of arrival (TDoA) of the anchor nodes' signals over a line-of-sight propagation path and reflected paths. The real-time location of the UWB tag is estimated according to the real-time TDoA measurements. To minimize the estimation error resulting from background noise in the two-way ranging, a Least Squares Method is implemented to minimize the estimation error for the localization of a static target, while Kalman Filter is applied for the localization of a mobile target. An experimental testbed is built based on off-the-shelf UWB hardware. Experiments validate that a reflector, e.g., a wall, and the UWB tag can be located according to the two-way ranging measurement. The localization accuracy of the proposed SLAM system is also evaluated, where the difference between the estimated location and the ground truth trajectory is less than 1 meter.

The advancement in technology has brought to life the concept of Autonomous vehicles (AV). The primary goal of AV is to reduce driving stress and provide comfort to the occupants. Since AVs can drive themselves, it poses a question of passenger security. Furthermore, AVs are connected to an open network like a public Internet to communicate to the outer world, raising security and privacy concerns. Skillful attackers can effortlessly infiltrate the vehicle by injecting malware which can disrupt the regular operation of the entire AV system. A Deep Learning (DL) and Blockchain framework is proposed for AV to resolve the aforementioned security challenges. The network traffic is continuously monitored, and the malware binaries are converted to grey-scale images, which are then classified by Convolutional Neural Network (CNN) employed in the DL model. The CNN architecture, ResNet50V2, has been tested and proves to be efficient in detecting malware with an accuracy of 97.56%.

Drug Discovery is a process by which new potential drugs are discovered and clinically trialed for commercial medicinal purposes. It has several stages of development, where each stage requires a prescribed time for its completion. The stages of drug development are discovery and development, pre-clinical research, clinical development, Food and Drug Administration (FDA) review, and post-market monitoring. The first three stages themselves take nearly 6.5 years. These stages take a huge time in cases where there is an urgent need for a drug. For example, during the COVID-19 pandemic, there was an urgent need for a vaccine. Many research institutes worked$24 \times 7$to develop a vaccine, but it still took a considerable time to get to a bare minimum vaccine. To tackle this problem, we propose DuBloQ, a novel methodology for drug discovery using Q-Learning. Our Q-Learning model consists of a generator and a predictor model. The generator generates a set of Simplified Molecular Input Line Entry System (SMILES) strings and the Predictor predicts its logp values. Based on the logp values, the reward for the generator is provided to improve its performance. We integrate this model with a blockchain User Interface (UI) that ensures security and privacy. We achieved an accuracy of 76.1% for the generator model.

In this paper, a novel transmission scheme, referred to as difference-based spatial modulation (DBSM), is proposed to improve the performance of spatial modulation (SM) within multi-antenna systems. The DBSM delivers two adjacent symbols in an arbitrary transmission, where one of the two symbols is radiated from a single transmit antenna. Different from conventional SM, the DBSM selects the active transmit antenna based on the difference between the two adjacent symbols. Although the other symbol is not physically radiated, the information carried by it is conveyed not only through the radiated symbol but also through the selection of active transmit antenna. For the purpose of performance evaluation, the proposed DBSM is compared with conventional SM in terms of bit error rate for the same system settings and the same multi-antenna configurations. Illustrative simulation results substantiates that our DBSM outperforms conventional SM while achieving the same data rate, specifically in the systems with low-order modulation.

5G networks are designed not only to transport data, but also to process them while supporting a vast number of services with different key Performance Indicators (KPIs). Network virtualization has emerged to enable this vision, however it calls for designing efficient computing and network resource allocation schemes to support diverse services, while jointly considering all KPIs associated with these services. Thus, this paper proposes a dynamic network slicing and resource allocation framework that aims at maintaining high-level network operational performance, while fulfilling diverse services’ requirements and KPIs, e.g., availability, reliability, and data quality. Differently from the existing works, which are designed considering traditional metrics like throughput and latency, we present a novel methodology and resource allocation schemes that enable high-quality selection of radio points of access, resource allocation, and data routing from end users to the cloud. Our results depict that the proposed solutions could obtain the best trade-off between diverse services’ requirements when compared to baseline approaches that consider partial network view or fair resource allocation.

With the advent of Internet of Things (IoT) systems, privacy and integrity of messages are becoming critical issues and are threatened, especially with mobile sensors. The broadcast nature of wireless communications increase information leakage in the presence of eavesdroppers. This paper proposes a hybrid Non-Orthogonal Multiple Access (NOMA)/Orthogonal Multiple Access (OMA)- based IoT systems to improve the data transmission security of moving sensors. We derive the Key Agreement Probability (KAP) expression of the proposed scheme, and we investigate the corresponding Secrecy Outage Probability (SOP) and the Average Bit Rate (ABR), when compared to pure NOMA and pure OMA transmission schemes. Simulation results are used to validate the derived expression and to evaluate the performance of the proposed scheme in terms of KAP, SOP, and ABR.

Federated Learning (FL) is a machine learning approach that enables private and decentralized model training. Although FL has been shown to be very useful in several applications, its privacy constraints cause a lack of model update transparency which makes it vulnerable to several types of attacks. In particular, based on detailed convergence analyses, we show in this paper that when the traditional model-combining scheme is used, even a single Byzantine node that keeps sending random reports will cause the whole FL model to diverge to non-useful solutions. A low complexity model combining approach is also proposed to stabilize the FL system and make it converge to a suboptimal solution just by controlling the model norm. The Physikalisch-Technische Bundesanstalt extra-large electrocardiogram (PTB-XL ECG) dataset is used to validate the findings of this paper and show the efficiency of the proposed approach in identifying heart anomalies.

Federated Learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. It is a promising solution for telemonitoring systems that demand intensive data collection, for detection, classification, and prediction of future events, from different locations while maintaining a strict privacy constraint. Due to privacy concerns and critical communication bottlenecks, it can become impractical to send the FL updated models to a centralized server. Thus, this paper studies the potential of hierarchical FL in Internet of Things (IoT) heterogeneous systems. In particular, we propose an optimized solution for user assignment and resource allocation over hierarchical FL architecture for IoT heterogeneous systems. This work focuses on a generic class of machine learning models that are trained using gradient-descent-based schemes while considering the practical constraints of non-uniformly distributed data across different users. We evaluate the proposed system using two real-world datasets, and we show that it outperforms state-of-the-art FL solutions. Specifically, our numerical results highlight the effectiveness of our approach and its ability to provide 4–6% increase in the classification accuracy, with respect to hierarchical FL schemes that consider distance-based user assignment. Furthermore, the proposed approach could significantly accelerate FL training and reduce communication overhead by providing 75–85% reduction in the communication rounds between edge nodes and the centralized server, for the same model accuracy.

With the development of visual programming languages, researchers pay attention to the automatic evaluation of visual projects. Previous work focus on the code evaluation but ignored another essential part—the visualization results. Scratch is a widely used programming platform, and projects created on it are displayed in the form of cartoon clips. It is valuable to explore the visual aesthetics embodied in these clips to fill the gap in the assessment system. We propose a model that predicts the human view scores of cartoon clips created on Scratch. Our method is divided into two steps to evaluate the aesthetic of the sequence images that compose cartoon clips. First, we train an image classification network to predict the relative aesthetics of individual images. Then we construct an aesthetic space for the sequence image and improve the rating within a specific range. We put forward ScratchGAN to generate a Scratch-cartoon-style aesthetic analysis data set for training the classification network. Experimental results show that our Generative Adversarial Network framework can well transform photos into a Scratch-cartoon style. The single image assessment network can generate predictions that fit human cartoon aesthetic opinions. Our method achieves satisfactory results in the aesthetic evaluation of sequence cartoon images.

Modern communication and computing technology is the basic support of the industrial intelligent systems (IIS). As a key component of IIS, the smart port is essential to be offered low-complexity and high-reliability communication service, especially for driverless engineering vehicles. However, it is combined and nonconvex to find the optimal association between vehicles and the road side units (RSUs). Besides, due to the mobility of vehicles and the severe path loss of mmWave links, beam switching and reassociation between vehicles and RSUs are required frequently, which brings a great challenge to the communication for the IIS. A low complexity closed-loop strategy based on distributed cooperation for mmWave communication in IIS is proposed in this study, in which user association and beam tracking with the assistance of beam pools is proposed. Many-to-many user association is established based on distributed multiagent reinforcement learning, where the vehicle can independently select the set of serving RSUs based on the local observation without information exchange with others, reducing the signaling overhead and computational complexity while improving system throughput. Furthermore, multipoint-cooperation soft switching of beams based on beam tracking improves the reliability of mmWave communication with the smaller training cost. Extensive analysis and simulation results demonstrate that the proposed solution significantly reduces the complexity of the mmWave communication while improving the throughput and stability in IIS.

The security and privacy of the network in Internet of Things (IoT) systems are becoming more critical as we are more dependent on smart systems. Considering that packets are exchanged between the end user and the sensing devices, it is then important to ensure the security, privacy, and integrity of the transmitted data by designing a secure and a lightweight authentication protocol for IoT systems. In this article, in order to improve the authentication and the encryption in IoT systems, we present a novel method of authentication and encryption based on elliptic curve cryptography (ECC) using random numbers generated by fuzzy logic. We evaluate our novel key generation method by using standard randomness tests, such as: frequency test, frequency test with mono block, run test, discrete Fourier transform (DFT) test, and advanced DFT test. Our results show superior performance compared to existing ECC based on shift registers. In addition, we apply some attack algorithms, such as Pollard’s$\rho $and Baby-step Giant-step, to evaluate the vulnerability of the proposed scheme.

Internet of Things (IoT) systems are becoming core building blocks for different services and applications supporting every day’s life. The heterogeneous nature of IoT devices and the complex use scenarios make it hard to build secure and private IoT systems. Physical-layer security (PLS) can lead to efficient solutions reducing the impact of the increasing security threats. In this work, we propose a new PLS-based IoT transmission scheme that offers a highly secured transmission probability, low-computational complexity, and reduced power consumption. We utilize 3-D stochastic geometry to model a more realistic IoT system and test our proposed scheme in different practical scenarios, where sensors, access points (APs), and eavesdroppers are randomly located in 3-D space. We focus on the system performance, in terms of secrecy outage probability (SOP) and secured successful transmission probability (SSTP), using tight closed-form expressions. An optimization problem is developed to deduce the optimal sensors’ transmit power, the APs’ density, and the maximum number of transmission tentative, when maximizing the SSTP. The proposed scheme outperforms the baseline retransmission scheme, in terms of SOP and SSTP based on analytical and simulation results.

LEACH-like clustering protocols focus mainly on the low-power, low-rate, and low-wakeup network applications, and work in a multiround clustering strategy that causes frequent handovers of cluster heads (CHs), thus less support for real-time services that require stable cluster topologies. Besides, these protocols are faced with respective drawbacks, such as suboptimality of selected heads, costly node-base station (BS) energy overheads, lack of runtime cluster maintenance, etc. This article proposes RANCE, a randomly centralized and on-demand clustering protocol, aiming at prolonging nodes’ clustered time to support internodes collaboration while being energy efficient in mobile ad hoc networks. First, RANCE designs a randomly centralized CH selection mechanism in which every node in the local wireless network is eligible to initiate the centralized CH selection, so that the self-organizing characteristics of mobile ad hoc nodes can be utilized for head selection optimization. Second, taking into account the wireless volatility caused by changes of topology, obstacles, signal strength, etc., the fine-grained cluster relationships maintenance is provided by means of multilevel aliveness and adaptive bidirectional heartbeat packets. Third, RANCE works in an event-driven and on-demand manner instead of a time-triggered manner in LEACH-like protocols, to reduce the impact on continuous services caused by frequent CH handovers among all nodes. Simulation results show that RANCE provides longer clustered time (over 99% of nodes’ lifetime in networks more than 100 nodes) and good clustering scalability with high consistency at minimum energy cost, and exhibits good potentials in mobile wireless environments that are infrastructureless/poor for continuous missions.

Due to recent privacy trends and the increase in data breaches in various industries, it has become imperative to adopt new technologies that support data privacy, maintain accuracy, and ensure sustainability at the same time. The healthcare industry is one of the most vulnerable sectors to cyberattacks and data breaches as health data are highly sensitive and distributed in nature. The use of IoT devices with machine learning models to monitor the health status has made the challenge more acute, as it increases the distribution of health data and adds a decentralized structure to healthcare systems. A new privacy-preserving technology, namely, federated learning (FL), is promising for such a challenge as implementing solutions that integrate FL with deep learning, for healthcare applications that rely on IoT, provides several benefits by mainly preserving data privacy, building robust and high accuracy models, and dealing with the decentralized structure, thus achieving sustainability. This article proposes a deep FL (DFL) framework for healthcare data monitoring and analysis using IoT devices. Moreover, it proposes an FL algorithm that addresses the local training data acquisition process. Furthermore, it presents an experiment to detect skin diseases using the proposed framework. The extensive results collected show that the DFL models can preserve data privacy without sharing it, maintain the decentralized structure of the system made by IoT devices, improve thearea under the curve(AUC) of the model to reach 97%, and reduce the operational costs (OC) for service providers.

The Internet of Things (IoT) is used to interconnect a massive number of heterogeneous resource-constrained smart devices. This makes such networks exposed to various types of malicious attacks. In particular, jamming attacks are among the most common harmful attacks to IoT networks. Therefore, an anti-jamming power allocation (PA) strategy is first proposed in this article for health monitoring IoT networks by exploiting the game theory to minimize the worst case jamming effect under multichannel fading. This strategy uses an iterative algorithm based on gradient descent to identify the Nash Equilibrium (NE) of the game. An artificial neural network (ANN) model is also proposed to accelerate the convergence of the algorithm making it more suitable for IoT networks. Furthermore, novel data population (DP), extension, and balancing techniques are proposed to enhance the efficiency of the proposed strategy in combating jamming attacks even for network configurations that were never used in the training phase. In addition, time and spatial diversities are exploited using a heterogeneous iterative algorithm to enhance the security of the network.

With the rapid growth of edge-assisted solutions in Internet of Things (IoT) networks, connected healthcare progressively relies on such solutions. This refers to systems in which all the healthcare stakeholders are connected to each other. These systems employ novel technologies, such as IoT, edge computing, and artificial intelligence (AI) to convert conventional health systems to more effective, appropriate, and customized intelligent systems. However, such systems encounter many restrictions and require new policies. By moving the computation and processing closer to the data sources and end-users, fog becomes edge computing which can reduce latency, bandwidth usage, and energy consumption. To the best of our knowledge, there is no systematic and methodological research in this scope that investigates the existing studies considering various vital and relevant factors. Thus, this survey aims to examine the state-of-the-art research in this area. We have reviewed a significant number of papers in this area and divided them into two main taxonomies, patient-centric and process-centric techniques. Furthermore, essential factors, such as available data sets and parameters like accuracy, mobility, and data rates are described and examined. Our aim is to bridge the gap between edge computing and connected healthcare solutions by discussing the challenges and highlighting future trends.

The emergence of mobile IPv6 (MIPv6) significantly affected how we live and work, while it still faces more security threats than traditional wireless networks. On the other hand, most mobile devices have constrained computing and storage resources. The network environment is complex, and the network topology also changes very frequently. Although various security protocols have been proposed for authentication in MIPv6, there are still some challenges. First, most of the subsisting authentication schemes cannot work in resource-constrained environments. Second, each of these authentication protocols has some defects, which may lead to serious consequences. So it is valuable and crucial to conduct security analysis at the design stage of protocols. Currently, most researchers attempt adopting informal methods, which are not as effective and suitable as formal methods. Some researchers have been conscious of the advantages of using formal methods to verify protocols. However, the approaches are too complex to understand for those who are not familiar with formal methods. In light of these challenges, we propose a lightweight MIPv6 authentication scheme for environments with low resources. We conduct a security analysis and performance comparison of the proposed authentication scheme. In particular, we use the SVO logic to formally analyze its security. We also explain how to use this formal method, which can be regarded as an example to better illustrate the application of formal analysis in MIPv6 authentication schemes.

With the wide application of Internet of Things (IoT) devices, security attacks against their firmware often occur, which has attracted more attention from the research community. Firmware is an important part of IoT devices, and attacks against them is one of the main means to destroy them. Therefore, firmware security is considered a core of the overall devices’ security. At present, most of the firmware vulnerabilities have a small number of related samples, so it is difficult to use machine learning methods to generate detectors for some of them. Therefore, based on the collected data of related firmware vulnerabilities, this article proposes a firmware vulnerability homology detection method based on the clonal selection algorithm. We design the numerical and structural characteristics of vulnerability functions, train a detector for each function separately, and improve the recall rate of vulnerability detection. Compared with existing machine learning methods, this method only depends on the affinity between the objective function and the detector, which avoids the requirement of a large number of sample data sets. Finally, relevant experiments are carried out to verify the effectiveness of the method.

The increasing need for economic, safe, and sustainable smart manufacturing combined with novel technological enablers has paved the way for artificial intelligence (AI) and big data in industries. This implies a substantial integration of AI, Industrial Internet of Things (IIoT), Robotics, big data, Blockchain, and 5G communications in support of smart manufacturing and the dynamical processes in modern industries. In this article, we provide a comprehensive overview of different aspects of AI and big data in Industry 4.0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective toward deployment of Industry 5.0. In detail, we highlight and analyze how the duo of AI and big data is helping in different applications of Industry 4.0. We also highlight key challenges in a successful deployment of AI and big data solutions in smart industrial applications with a particular emphasis on data-related issues, such as availability, bias, auditing, management, interpretability, communication, and different adversarial attacks and security issues. Finally, we explore the significance of AI and big data toward Industry 4.0 applications through panoramic reviews and discussions. This work is expected to provide a baseline for future research in the domain.

Unmanned aerial vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, which is not the case of traditional systems relying on direct observations obtained from fixed cameras and sensors. Furthermore, thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications. However, deep neural networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed onboard. In this article, we propose a DNN distribution methodology within UAVs to enable data classification in resource-constrained devices and avoid extra delays introduced by the server-based solutions due to data communication over air-to-ground links. The proposed method is formulated as an optimization problem that aims to minimize the latency between data collection and decision-making while considering the mobility model and the resource constraints of the UAVs as part of the air-to-air communication. We also introduce the mobility prediction to adapt our system to the dynamics of UAVs and the network variation. The simulation conducted to evaluate the performance and benchmark the proposed methods, namely, optimal UAV-based layer distribution (OULD) and OULD with mobility prediction (OULD-MP), was run in an HPC cluster. The obtained results show that our optimization solution outperforms the existing and heuristic-based approaches.

The satellite-assisted Internet of Things (IoT) communication is considered a key component of the 6G network, and low Earth orbit (LEO) satellite is the leading choice of IoT-related satellites due to its minimum delay. Hybrid requests, including immediate reservation (IR) and advanced reservation (AR) services in LEO satellite netoworks cause the occurrence of resource fragments (RFrags), which adversely affect the network performance. To alleviate the degradation of network performance caused by resource fragmentation, a routing, wavelength, and time-slots assignment algorithm, which is named elastic resource allocation algorithm based on the dispersion degree (ERA-DD), is proposed in this article. Possible resource fragmentation types are analyzed and a fragmentation description named dispersion degree (DD) is designed. In the DD, the number of free resource blocks is accurately described by the state jumps (SJs) of adjacent resource slots, and the numerical relationship between SJs and free resource blocks is proposed and proved. Besides, restrictions on the selection priority of candidate schemes in the ERA-DD algorithm are analyzed. Finally, the traffic blocking rate, the wavelength utilization, the average communication delay, and the average initial delay are evaluated by simulation. The results demonstrate that RFrags are more fully utilized compared with the maximum total link spectrum consecutiveness (MTLSC) algorithm. The traffic blocking rate can be reduced by 32.5% and wavelength utilization can be increased by 1.6%.

Unmanned aerial vehicles (UAVs) have attracted much attention lately and are being used in a multitude of applications. But the duration of being in the sky remains to be an issue due to their energy limitation. In particular, this represents a major challenge when UAVs are used as base stations (BSs) to complement the wireless network. Therefore, as UAVs execute their missions in the sky, it becomes beneficial to wirelessly harvest energy from external and adjustable flying energy sources (FESs) to power their onboard batteries and avoid disrupting their trajectories. For this purpose, wireless power transfer (WPT) is seen as a promising charging technology to keep UAVs in flight and allow them to complete their missions. In this work, we leverage a multiagent deep reinforcement learning (MADRL) method to optimize the task of energy transfer between FESs and UAVs. The optimization is performed by carrying out three essential tasks: 1) maximizing the sum-energy received by all UAVs based on FESs using WPT; 2) optimizing the energy loading process of FESs from a ground BS; and 3) computing the most energy-efficient trajectories of the FESs while carrying out their charging duties. Furthermore, to ensure high-level reliability of energy transmission, we use directional energy transfer for charging both FESs and UAVs by using laser beams and energy beam-forming technologies, respectively. In this study, the simulation results show that the proposed MADRL method has efficiently optimized the trajectories and energy consumption of FESs, which translates into a significant energy transfer gain compared to the baseline strategies.

Due to the high maneuverability and flexibility, unmanned aerial vehicles (UAVs) have been considered as a promising paradigm to assist mobile edge computing (MEC) in many scenarios including disaster rescue and field operation. Most existing research focuses on the study of trajectory and computation-offloading scheduling for UAV-assisted MEC in stationary environments, and could face challenges in dynamic environments where the locations of UAVs and mobile devices (MDs) vary significantly. Some latest research attempts to develop scheduling policies for dynamic environments by means of reinforcement learning (RL). However, as these need to explore in high-dimensional state and action space, they may fail to cover in large-scale networks where multiple UAVs serve numerous MDs. To address this challenge, we leverage the idea of “divide-and-conquer” and propose HT3O, a scalable scheduling approach for large-scale UAV-assisted MEC. First, HT3O is built with neural networks via deep RL to obtain real-time scheduling policies for MEC in dynamic environments. More importantly, to make HT3O more scalable, we decompose the scheduling problem into two-layered subproblems and optimize them alternately via hierarchical RL. This not only substantially reduces the complexity of each subproblem, but also improves the convergence efficiency. Experimental results show that HT3O can achieve promising performance improvements over state-of-the-art approaches.

Edge-supported Industrial Internet of Things (IIoT) has recently received significant attention since the edge computing can greatly improve the service quality of IIoT applications. However, edge servers are not fully trusted and are often deployed at the edge of the network. Therefore, there are some security challenges that need to be addressed. For edge-supported IIoT, a privacy-preserving range query is one of the most important functional requirements. Recently, some privacy-preserving range query solutions have been proposed in different fields. However, most of them only support single-dimensional range query, which are inefficient for the requirement of multidimensional range query. To address these problems, we propose a privacy-preserving multidimensional range query scheme for edge-supported IIoT, called Edge-PPMRQ, in this article. In Edge-PPMRQ, a novel range division algorithm is designed, through which the multidimensional ranges can be merged into one range, so as to achieve multidimensional range query through one query request. In addition, Edge-PPMRQ also supports the range queries for continuous, discontinuous, and arbitrary boundary ranges. The detailed security analysis proves that Edge-PPMRQ is privacy preserving for the query ranges, the query results, and the sensed data of IIoT devices. Furthermore, extensive comparison experiments also illustrate that Edge-PPMRQ is efficient in communication and computation.

A digital-twin (DT)-enabled Internet of Medical Things (IoMT) system for telemedical simulation is developed, systematically integrated with mixed reality (MR), 5G cloud computing, and a generative adversarial network (GAN) to achieve remote lung cancer implementation. Patient-specific data from 90 lung cancer with pulmonary embolism (PE)-positive patients, with 1372 lung cancer control groups, were gathered from Qujing and Dehong, and then transmitted and preprocessed using 5G. A novel robust auxiliary classifier GAN (rAC-GAN)-based intelligent network is employed to facilitate lung cancer with the PE prediction model. To improve the accuracy and immersion during remote surgical implementation, a real-time operating room perspective from the perception layer with a surgical navigation image is projected to the surgeon’s helmet in the application layer using the DT-based MR guide clue with 5G. The accuracies of the area under the curve (AUC) of our new intelligent IoMT system were 0.92 and 0.93. Furthermore, the pathogenic features learned from our rAC-GAN model are highly consistent with the statistical epidemiological results. The proposed intelligent IoMT system generates significant performance improvement to process substantial clinical data at cloud centers and shows a novel framework for remote medical data transfer and deep learning analytics for DT-based surgical implementation.

Mobile edge computing (MEC) can provision augmented computational capacity in proximity so as to better support Industrial Internet of Things (IIoT). Tasks from the IIoT devices can be outsourced and executed at the accessible computational access point (CAP). This computing paradigm enables the computing resources much closer to the IIoT devices, and thus satisfy the stringent latency requirement of the IIoT tasks. However, existing works in MEC that focus on task offloading and resource allocation seldom consider the load balancing issue. Therefore, load balance aware task offloading strategies for IIoT devices in MEC are urgently needed. In this article, software-defined network (SDN) technology is adopted to address this issue, since the rule-based forwarding policy in SDN can help determine the most suitable offloading path and CAP for undertaking the computation. To this end, we formulate an optimization problem to minimize the response latency in the proposed SDN-assisted MEC architecture. A greedy algorithm is put forward to obtain the approximate optimal solution in polynomial time. Simulation has been carried out to evaluate the performance of the proposed approach. The simulation results reveal that our approach outstands other approaches in terms of the response latency.

Recent developments in the Internet of Vehicles (IoV) enable interconnected vehicles to support ubiquitous services. Various emerging service applications are promising to increase the Quality of Experience (QoE) of users. On-board computation tasks generated by these applications have heavily overloaded the resource-constrained vehicles, forcing it to offload on-board tasks to other edge intelligence (EI)-assisted servers. However, excessive task offloading can lead to severe competition for communication and computation resources among vehicles, thereby increasing the processing latency, energy consumption, and system cost. To address these problems, we investigate the transmission-awareness and computing-sense uplink resource management problem and formulate it as a time-varying Markov decision process. Considering the total delay, energy consumption, and cost, quantum-inspired reinforcement learning (QRL) is proposed to develop an intelligence-oriented edge offloading strategy. Specifically, the vehicle can flexibly choose the network access mode and offloading strategy through two different radio interfaces to offload tasks to multiaccess edge computing (MEC) servers through WiFi and cloud servers through 5G. The objective of this joint optimization is to maintain a self-adaptive balance between these two aspects. Simulation results show that the proposed algorithm can significantly reduce the transmission latency and computation delay.

Mobile onboard target detection system with autonomous drone-assisted Internet of Things, due to its inherent agility and coverage-effective deployment, is beneficial in city management, ecosystem monitoring, etc. However, most advanced detection methods become inefficient or even malfunction, since the computation resources for online detection in such a high-altitude dynamic environment are far beyond the capability of the drone. To this end, we design and implement ODTDS—an online drone-based target detection system that performs online data processing and autonomous navigation simultaneously with restricted resources. Specifically, to prolong detection durations with limited energy providing for continuous processing and flying, we develop an adaptive motion planner for autonomous and energy-efficient navigation. Meanwhile, to perform online target detection from complex environments, we propose a hybrid method integrating feature pyramid feedback with the speed up robust features, to enhance the background information to achieve high accuracy on the restricted computation platform of the drone. Based on these two designs, our system can address resource-constrained challenges to accomplish detection missions autonomously. Finally, we implement an ODTDS prototype and evaluate it through outdoor high-altitude experiments. The results of various performance evaluations can demonstrate the effectiveness of our system.

The explosive growth of Internet of Vehicle (IoV) applications has made information security a significant issue. Mobile IoV users are dynamic and the communication environment is very complex, which makes it very difficult to guarantee real-time secrecy communication performance. Thus, a reliable and effective evaluation and prediction of secrecy performance is critical. In this article, we have derived novel expressions for secrecy performance. A grey wolf optimization generalized regression (GWO-GR) algorithm is proposed to predict the secrecy performance and carry out the secrecy performance assessment. A generalized regression (GR) neural network is designed. Out of the input and output layers, the proposed GR network has a pattern layer and a summation layer, which can obtain a global convergence of network results. To further optimize the GR network, the grey wolf optimization algorithm is used to obtain the best spread factor for it, which can accelerate its rapid convergence. Through the simulated numerical results, we can obtain: 1) the proposed GWO-GR prediction algorithm is shown to provide better performance prediction results than other machine-learning-based methods; 2) in particular, the prediction accuracy is improved by 17.7%; and 3) the execution time has an 88.9% reduction.

Applications consisting of a group of modular tasks can be offloaded to the multiaccess edge computing (MEC) for lower delay and energy consumption. In a dynamic MEC system, the fine-grained cooperative and dynamic offloading solution is necessary for the scenario of reusing tasks among devices. Considering the transmission cooperation, shared wireless bandwidth, and changing task queues on devices and edge servers, we formulate a joint offloading optimization problem to minimize the long-term average task execution cost. Although deep reinforcement learning (DRL) is a popular method for the dynamic problem, existing DRL algorithms are not suitable for our problem because of the hybrid discrete-continuous action spaces and constraints among action dimensions. Therefore, we propose a hybrid average reward proximal policy optimization (hybrid-ARPPO) algorithm to jointly optimize the offloading decisions, cooperative transmission ratios, and edge server assignments. First, we decompose our offloading problem into two subproblems. One is a tractable linear programming problem for continuous transmission ratios, and the other is a Markov decision process (MDP) only with discrete actions for offloading decisions and server assignments. Second, we take the expected average reward as the performance measure and deprecate the discount factor, which can reduce the work of tuning algorithms. Third, we design an action mask layer in the policy network of hybrid-ARPPO to filter invalid actions. Extensive experiments show the effectiveness of our hybrid-ARPPO in different system scales and task arrival patterns.

Personalized content sharing will inevitably become one of the core applications of mobile Internet of Things. However, the existing strategies for content sharing are far from effective content personalization, since they either fail to protect the diversity of shared content or harm the enthusiasm of users to participate in cooperation. How to optimize the tradeoff between content personalization and sharing efficiency thus becomes an extremely challenging problem. To circumvent this dilemma, we propose a social-aware personalized content-sharing strategy based on mobile crowdsensing (MCS), which specially introduces positive network externalities derived from MCS and the social network. Specifically, we design a two-stage pricing-participation game to model the interactions between mobile users and a profit-making service provider. By solving the subgame-perfect Nash equilibrium (NE) of the proposed game, an efficient participation mechanism and an optimal-pricing strategy are developed. First, users’ decision selection of whether to join MCS is modeled as a social-aware MCS participation game (SA-MPG), and two algorithms for solving the Pareto-optimal NE of SA-MPG are designed. Subsequently, the pricing issue for network operators is investigated by exploiting the supermodularity of SA-MPG. Stochastic network model and real-world data set-based simulations corroborate the significant gain of our proposed strategy.

Internet of Things (IoT) connects massive physical devices to capture and collect useful data, which are used to make accurate decisions by taking advantage of the machine learning techniques. However, the collected data may contain users’ sensitive information. When guaranteeing the utility of data, we need to consider privacy of users’ data. To balance the utility and the privacy of data, the existing approaches usually adopt the privacy-preserving signature technology, where the privacy-preserving data are classified by a designated converter (data processor) interacting with a semihonest verifier (data center). However, for the malicious behavior of the data center and data processor, this kind of approach is insufficient. To prevent the malicious data center/data processor while guaranteeing the utility and privacy of data, we propose blockchain-based auditable privacy-preserving data classification (PPDC) scheme for IoT. We put forth a new controllably linkable group signature (CL-GS) to balance the utility and privacy of data and take advantage of blockchain to audit the correctness of privacy-preserving data classification against malicious data processor/data center. We formalize the system model of the auditable privacy-preserving data classification in the blockchain setting and its security model. Then, we present a concrete construction and prove its security in the random oracle model. Finally, we deploy a prototype system to evaluate the performance ofPPDC.

Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT). On the one hand, the learning accuracy of FL can be improved by selecting the IoT devices with large data sets for training, which gives rise to a higher energy consumption. On the other hand, the energy consumption can be reduced by selecting the IoT devices with small data sets for FL, resulting in a falling learning accuracy. In this article, we formulate a new resource allocation problem for privacy-preserving EdgeIoT to balance the learning accuracy of FL and the energy consumption of the IoT device. We propose a new FL-enabled twin-delayed deep deterministic policy gradient (FL-DLT3) framework to achieve the optimal accuracy and energy balance in a continuous domain. Furthermore, long short-term memory (LSTM) is leveraged in FL-DLT3 to predict the time-varying network state while FL-DLT3 is trained to select the IoT devices and allocate the transmit power. Numerical results demonstrate that the proposed FL-DLT3 achieves fast convergence (less than 100 iterations) while the FL accuracy-to-energy consumption ratio is improved by 51.8% compared to the existing state-of-the-art benchmark.

Nowadays, the compelling applications of the Internet of Things (IoT) bring unexpected economic benefits to our daily lives. But at the same time, it also poses huge challenges to service providers. Diverse proprietary hardware (i.e., firewall and code conversion) have to be deployed in networks for meeting different applications’ requirements. Recently, network functions virtualization (NFV) is considered a promising technique. In the NFV-enabled architecture, network services can be implemented via a set of orderly virtual network functions (VNFs) on standardized compute nodes, which is termed service function chains (SFCs). However, with the explosion of IoT applications, embedding multiple SFCs in a shared NFV-enabled infrastructure becomes a challenging problem. Centralized schemes suffer from the scalability and private issue, while distributed schemes suffer from the nonconvergence problem. In this article, we propose a hybrid intelligent control architecture, which adopts the centralized training and distributed execution paradigm. A centralized critic is introduced to ease the training process of the distributed network nodes. Besides, considering the competitive behavior of users, we formulate the resource allocation problem as a multiuser competition game model. Based on this, we proposed a multiagent reinforcement learning-based SFCs deployment algorithm.

By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry 4.0 promotes integrating cyber–physical worlds through cyber–physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT enables interaction with the digital image of the industrial physical objects/processes to simulate, analyze, and control their real-time operation. DT is rapidly diffusing in numerous industries with the interdisciplinary advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics. However, the existing literature lacks in identifying and discussing the role and requirements of these technologies in DT-enabled industries from the communication and computing perspective. In this article, we first present the functional aspects, appeal, and innovative use of DT in smart industries. Then, we elaborate on this perspective by systematically reviewing and reflecting on recent research trends in next-generation (NextG) wireless technologies (e.g., 5G-and-Beyond networks) and design tools, and current computational intelligence paradigms (e.g., edge and cloud computing-enabled data analytics, federated learning). Moreover, we discuss the DT deployment strategies at different communication layers to meet the monitoring and control requirements of industrial applications. We also outline several key reflections and future research challenges and directions to facilitate industrial DT’s adoption.

Summary With advanced information technologies and industrial intelligence, Industry 4.0 has been witnessing a large scale digital transformation. Intelligent transportation plays an important role in the new era and the classic vehicle routing problem (VRP), which is a typical problem in providing intelligent transportation, has been drawing more attention in recent years. In this article, we study multidepot VRP (MDVRP) that considers the management of the vehicles and the optimization of the routes among multiple depots, making the VRP variant more meaningful. In addressing the time efficiency and depot cooperation challenges, we apply the artificial bee colony (ABC) algorithm to the MDVRP. To begin with, we degrade MDVRP to single‐depot VRP by introducing depot clustering. Then we modify the ABC algorithm for single‐depot VRP to generate solutions for each depot. Finally, we propose a coevolution strategy in depot combination to generate a complete solution of the MDVRP. We conduct extensive experiments with different parameters and compare our algorithm with a greedy algorithm and a genetic algorithm (GA). The results show that the ABC algorithm has a good performance and achieve up to 70% advantage over the greedy algorithm and 3% advantage over the GA.

A secure energy efficient approach is proposed to connect Internet of Things (IoT) sensors that operate with limited power resources. This is done by optimizing simultaneously the energy efficiency, the communication rate and the network security while limiting the potential data leakage and tracking the finite battery status evolution. The proposed model uses spatial diversity in addition to artificial jamming introduced by an intermediate device to forward the data from the sensors to the destination and to secure the communication links without draining the rechargeable batteries. The energy harvested by the source is also maximized without affecting the security level of the network. The outage secrecy capacity is derived to evaluate the security level. Furthermore, the system power stability is analyzed using Markov chains and statistical approaches to validate the efficiency of the proposed technique in maintaining the system in a self-sufficient mode and making it operate without the assistance of external power resources.

To realize high energy-efficient, high spectrum-efficient, and high-throughput data transmission, a multi-carrier differential chaos shift keying communication system with hybrid index modulation, referred to asHIM-MC-DCSK system, is proposed in this paper. In the HIM-MC-DCSK system, we intelligently integrate the carrier-number-index technique and carrier-index technique into the MC-DCSK modulation in order to significantly boost the transmission efficiency in wireless communication systems. Especially, we use a pair of orthogonal signals to represent the active and inactive subcarriers in the HIM-MC-DCSK system so as to exploit all the subcarriers to transmit information bits. In addition, the theoretical bit-error-rate (BER) expressions of the HIM-MC-DCSK system are derived over additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels. Also, the data rate, spectrum efficiency, energy efficiency, and complexity of the proposed system are carefully analyzed. Monte-Carlo simulations not only verify the accuracy of the theoretical analysis but also illustrate the superiority of the proposed system.

Numerous new applications have been proliferated with the mature of 5G, which generates a large number of latency-sensitive and computationally intensive mobile data requests. The real-time requirement of these mobile data has been accommodated well by fog computing in the past few years, mainly through offloading tasks to fog nodes in the vicinity. On the other hand, the user-privacy hidden in the Internet-of-Things (IoT) data has not been sufficiently considered in the presence of insecure fog nodes. It is risky to offload an entire mission-critical task to just one fog node or several fog nodes owned by the same service provider (SP), especially when the SP is marked with low-security credit and tends to collect data information of users for malicious use. To address this issue, we classify IoT user tasks based on their security requirements, divide them into different numbers of smaller fragments, and, finally, offload the segments of a task to multiple fog nodes owned by the same or various SPs according to their security requirements. The selected fog nodes will collaboratively serve the divided fragments to avoid the possible damage caused by the leak of sensitive data due to compromised fog nodes of malicious SPs. For this, we propose an integer linear programming (ILP) model and a dynamic programming algorithm to maximize the number of successfully served IoT data tasks with satisfactory security requirements while minimizing the end-to-end transmission delay. The numerical results show that the proposed ILP model and algorithm can significantly increase the successful provisioning ratio for tasks with high-security requirements.

The Internet of Things (IoT) is experiencing explosive growth and has gained extensive attention from academia and industry in recent years. However, most of the existing IoT infrastructures are centralized, which may cause the issues of unscalability and single-point-of-failure. Consequently, decentralized IoT has been proposed by taking advantage of the emerging technology called blockchain. Voting systems are widely adopted in IoT, for example a leader election in wireless sensor networks. Self-tallying voting systems are alternatives to unsuitable, traditional centralized voting systems in decentralized IoT. Unfortunately, self-tallying voting systems inherently suffer from fairness issues, such as adaptive and abortive issues caused by malicious voters. To address these issues, in this article, we introduce a framework of the self-tallying voting system in decentralized IoT based on blockchain. We propose a concrete construction and prove that the proposed system satisfies all the security requirements, including fairness, dispute-freeness, and maximal ballot secrecy. We simulate the algorithms on a laptop, an Android phone, and a Raspberry Pi to test the time consumption and evaluate the gas cost of each algorithm in a private blockchain as well. The implementation results demonstrate the practicability of our system.

Advanced persistent threats (APT) have novel features such as long-term latency, precision strikes and uncertain strategies. APT poses severe threats to the resource-limited edge devices in advanced networks. Cyber threat intelligence (CTI) conducts data analysis on attack strategies by artificial intelligence (AI) and generates threat intelligence to optimize the detection model and guide defense strategies. However, AI lacks explanations for the decisions and thus reduces the transparency and performance of the detection model. Besides, the tradeoff between the detection accuracy and the computational resource limitation of edge devices needs an optimal and rapid dynamic resource allocation method, which edge game and AI can help. In this paper, we propose an explainable intelligence-driven APT edge defense mechanism. The proposed mechanism provides guidelines and explanations for designing the defense strategy and resource allocation scheme of the edge defender to detect APT. The edge defense strategy model is based on edge Bayesian Stackelberg game and CTI. Meanwhile, we implement a DRL-based resource allocation scheme to meet rapid response requirements at the edges. We demonstrate that the proposed mechanism can improve the protection level of edges and defense capability against APT through extensive experiments.

Industry 5.0 is rapidly growing as the next industrial evolution, aiming to improve production efficiency in the 21stcentury. This evolution relies mainly on advanced digital technologies, including Industrial Internet of Things (IIoT), by deploying multiple IIoT devices within industrial systems. Such a setup increases the possibility of threats, especially with the emergence of IIoT botnets. This can provide attackers with more sophisticated tools to conduct devastating IIoT attacks. Besides, machine learning (ML) and deep learning (DL) are considered as powerful techniques to efficiently detect IIoT attacks. However, the centralized way in building learning models and the lack of up-to-date datasets that contain the main attacks are still ongoing challenges. In this context, multiaccess edge computing (MEC) and federated learning (FL) are two promising complementary technologies. MEC brings computing capabilities at the edge of the industrial systems, while FL leverages the edge resources to enable a privacy-aware collaborative learning, especially in multiindustrial systems context. In this article, we design a novel MEC-based framework to secure IIoT applications leveraging FL, called FedGame. Specifically, FedGame enables multiple MEC domains to collaborate securely to deal with an IIoT attack, while preserving the privacy of IIoT devices. Moreover, a noncooperative game is formulated on the top of FedGame, to enable MEC nodes acquiring the needed virtual resources from the centralized MEC orchestrator, to deal with each type of IIoT attacks. We evaluate FedGame using real-world IIoT attacks; the experimental results show not only the accuracy of FedGame against centralized ML/DL schemes while preserving the privacy of Industrial systems but also its efficiency in providing required MECs resources and, thus, dealing with IIoT attacks.

With the growth of the number of Internet of Things (IoT) devices and the emergence of new applications, satisfying distinct QoS in the same physical network becomes more challenging. Recently, with the advance of network functions virtualization and software-defined networking (SDN) technologies, the network slicing technique has emerged as a promising solution. It can divide a physical network into multiple virtual networks, therefore providing different network services. In this article, to meet distinct QoS in industrial IoT, we design a network slicing architecture over the SDN-based long-range wide area network. The SDN controller can dynamically split the network into multiple virtual networks according to different business requirements. On this basis, we proposed a deep deterministic policy gradient (DDPG) based slice optimization algorithm. It enables LoRa gateways to intelligently configure slice parameters (e.g., transmission power and spreading factor) to improve the slice performance in terms of QoS, energy efficiency, and reliability. In addition, to accelerate the training process across multiple LoRa gateways, we leverage the transfer learning framework and design a transfer learning-based multiagent DDPG algorithm.

With the expected explosive use of the Internet of Everything in sixth generation (6G), the cybertwin network is able to convert user information to digital assets and provide extensive services. However, protecting and enhancing privacy of the processed and transmitted data in cybertwin-driven 6G is still in its infancy. Federated learning (FL) is a nascent distributed machine learning paradigm that is able to facilitate privacy protection in cybertwin networks. In a cybertwin network, imbalanced data distribution of the clients can increase the bias of the global model and sacrifice the performance of the FL model. Prior research work dealing with imbalanced data requires extra data information exchanged between clients and the server, which increases the risk of privacy leakage. To avoid privacy leakage, we design an estimation algorithm to determine the distribution of local data collected at the clients without the awareness of specific raw data. We consider two scenarios in FL: 1) the server could receive the individual trained model for each selected device and 2) the server could receive the aggregated model from the selected clients. We formulate two device selection problems to improve the training performance of the aforementioned scenarios. We develop two online learning algorithms to tackle the selection problems for both individual model uploading and aggregated model uploading. The proposed algorithms are conducted on the server, thereby avoiding privacy leakage and extra computation at the clients. We validate the effectiveness of the proposed client selection algorithms with sufficient experiments in cybertwin-driven 6G networks.

5G-and-beyond (B5G) networks are moving toward the higher end of the millimeter-wave (mmWave) spectrum (i.e., from 25 to 100 GHz) to support integrated communications and ranging (ICAR) services in next-generation factory deployments. The ICAR services in factory deployments require extreme bandwidth/capacity and large ranging coverage, which a mmWave-B5G system can fulfill using massive multi-input and multioutput (mMIMO), beamforming, and advanced ranging techniques. However, as mmWave signal propagation is sensitive to harsh channel conditions experienced in typical indoor factory environments, there is a growing interest in the realistic mmWave indoor channel modeling to evaluate the practical scope of the mmWave-B5G systems. In this article, we study and implement a 3-D stochastic channel model using the baseline third-generation partnership project model. Our channel model employs the time-cluster spatial-lobe (TCSL) technique and utilizes the temporal and spatial statistics to create the channel impulse response (CIR), reflecting realistic indoor factory conditions. Using the generated CIR, we present the performance analysis of an mmWave-B5G system in terms of power delay profile, path loss, communication and ranging coverage, and mMIMO channel capacity.

Digital twin (DT) provides accurate guidance for multidimensional resource scheduling in 5G edge computing-empowered distribution grids by establishing a digital representation of the physical entities. In this article, we address the critical challenges of DT construction and DT-assisted resource scheduling such as low accuracy, large iteration delay, and security threats. We propose a federated learning-based DT framework and present a Secure and lAtency-aware dIgital twin assisted resource scheduliNg algoriThm (SAINT). SAINT achieves low-latency, accurate, and secure DT by jointly optimizing its total iteration delay and loss function, and leveraging abnormal model recognition (AMR). SAINT enables intelligent resource scheduling by using DT to improve the learning performance of deep Q-learning. SAINT supports access priority and energy consumption awareness due to the consideration of long-term constraints. Compared with state-of-the-art algorithms, SAINT has superior performance in cumulative iteration delay, DT loss function, energy consumption, and access priority deficit.

The aerial capabilities and flexibility in movement of Unmanned Aerial Vehicles (UAVs) has enabled them to adaptively provide both traditional and more contemporary services. In this article, we introduce a solution that integrates the capabilities of both UAVs and Unmanned Ground Vehicles (UGVs) to provide both intelligent connectivity and services to both aerial and ground connected devices. A cooperative solution is adopted that considers nodes’ power and movement constraints. The UAV and UGV cooperative process ensures continuous power availability to UAVs to support seamless and continuous service availability to end-devices. A Federated Learning (FL) approach is adopted at the edge to ensure accurate and up-to-date service provisioning in accordance with the surrounding environment and network constraints. Moreover, Blockchain technology is used to decentralize the provisioning and control aspects, and ensure authenticity and integrity. Extensive simulations are conducted to test the soundness and applicability of the proposed solution. Results show significant improvement in terms of connectivity, service availability, and UAV energy enhancements when compared to traditional mobile and vehicular communication techniques.

Artificial Intelligence is in transition as the fast convergence of digital technologies and data science holds the promise to liberate consumer data and provide a faster and more cost-effective way of improving human initiatives. Particularly, artificial intelligence (AI) is heavily influencing autonomous vehicles nowadays. The data driven-based AI autonomous vehicles have the potential to reshape the expectations of human’s actions, the way that companies’ stakeholders collaborate, and revamp business models in the various industries.