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.

Aiming at the real-time computing problems in large-scale internet of things devices (IoTDs) scenarios, a framework for terahertz (THz) -based mobile edge computing (MEC) network with multi-unmanned aerial vehicles (UAV) collaboration is proposed. In this framework, a utility model based on latency and connection scheduling is first presented. Its significance lies in enabling high-priority tasks to obtain more computing resources, thereby reducing computing latency. Then, we formulate an optimization problem that jointly optimizes connection scheduling, computing resource allocation, and UAV flight trajectories under the objective of maximizing the average utility of IoTDs. To solve this Mixed Integer Nonlinear Programming Problem (MINLP), we use Deep Reinforcement Learning (DRL) based on learning rate decay and Prioritized Experience Replay (PER) to optimize the UAVs trajectories, and design a low-time complexity heuristic algorithm to solve the connection scheduling and resolve the computing resource allocation by an iterative algorithm. Subsequently, to evaluate the performance of our proposed algorithm, we compare it with Soft Actor-Critic (SAC), Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), and Particle Swarm Optimization (PSO). Simulation results show that our proposed algorithm significantly improves the average utility of IoTDs and reduces the latency of high-priority tasks. Besides, our proposed algorithm has better convergence than the above algorithms.

The deep vision sensing has been a practical tool in early disease detection, and this work aims at an important branch of ocular disease recognition. Although a number of researchers had paid attention to it during past years, fine-grained ocular feature extraction always remains a challenge. To handle with this issue, this work benefits from comprehensive ability of the convolution-Transformer structure (Conformer), and proposes vision sensing-driven intelligent ocular disease detection using conformer-based dual fusion. On the one hand, the proposal combines technical advantages of convolution and visual Transformer to more accurately fuse local subtle features and global representation information in images. On the other hand, the proposal significantly improves accuracy and robustness of the model by optimizing depth and width. Simulation experiments on real-world ocular disease image datasets show that the proposed model exhibits higher performance in ocular disease detection compared to other methods. Numerical results show that it improves the detection accuracy by 1% to 3.7% compared to several mainstream baseline methods. This research result not only promotes the development of ocular disease detection, but also provides more reliable technical support for accurate diagnosis of ophthalmic diseases.

Abstract Mobile Crowdsensing (MCS) is considered to be a key emerging example of a smart city, which combines the wisdom of dynamic people with mobile devices to provide distributed, ubiquitous services and applications. In MCS, each worker tends to complete as many tasks as possible within the limited idle time to obtain higher income, while completing a task may require the worker to move to the specific location of the task and perform continuous sensing. Thus the time and location information of each worker is necessary for an efficient task allocation mechanism. However, submitting the time and location information of the workers to the system raises several privacy concerns, making it significant to protect both the temporal and spatial privacy of workers in MCS. In this article, we propose the Task Allocation with Temporal‐Spatial Privacy Protection (TASP) problem, aiming to maximize the total worker income to further improve the workers' motivation in executing tasks and the platform's utility, which is proved to be NP‐hard. We adopt differential privacy technology to introduce Laplace noise into the location and time information of workers, after which we propose the Improved Genetic Algorithm (SPGA) and the Clone‐Enhanced Genetic Algorithm (SPCGA), to solve the TASP problem. Experimental results on two real‐world datasets verify the effectiveness of the proposed SPGA and SPCGA with the required personalized privacy protection.

Abstract Recently, face recognition based on homomorphic encryption for privacy preservation has garnered significant attention. However, there are two major challenges with homomorphic encryption methods: the security and efficiency of face recognition systems. We present a more efficient and secure PUM (Privacy preserving security Using Multi‐key homomorphic encryption) mechanism for facial recognition. By integrating feature grouping with parallel computing, we enhance the efficiency of homomorphic operations. The use of multi‐key encryption ensures the security of the facial recognition system. This approach improves the security and speed of facial recognition systems in cloud computing scenarios, increasing the original 128‐bit security to a maximum of 1664‐bit security. In terms of efficiency, comparing encrypted images takes only 0.302 s, with an accuracy rate of 99.425%. When applied to a campus scenario, the average search time for a facial template library containing 700 encrypted features is approximately 1.5 s. Consequently, our solution not only ensures user privacy but also demonstrates superior operational efficiency and practical value. In comparison to recently emerged ciphertext facial recognition systems, our solution has demonstrated notable enhancements in both security and time efficiency.

Giant AI models, typically trained and deployed centrally in the cloud, demand significant computational resources, posing privacy risks for the Internet of Things (IoT), particularly in the era of 6G-driven connectivity. federated learning (FL) mitigates this by enabling local training and server-side aggregation, fostering 6G Symbiotic IoT while preserving privacy in 6G networks. However, data heterogeneity (DH) in multimodal settings remains a formidable challenge, degrading model performance. While prior studies attribute DH to uneven data distributions, our empirical analysis reveals that hard samples also drive DH, manifesting across both local and global models. To address this, we propose MCMFL, a multimodal FL framework leveraging Monte Carlo dropout to quantify sample uncertainty and identify hard samples. Exploiting 6G’s excellent capabilities, MCMFL optimizes the local loss function and introduces MC dropout-based aggregation, a robust aggregation algorithm, enhancing the model’s resilience to hard samples. Extensive experiments show that MCMFL demonstrates superior performance, outperforming baseline aggregation methods by up to 5.38% on CIFAR-100, leading local enhancement baselines by 3.14% on TinyImageNet-200 and achieving the highest score of 4.79 in MTBenchmark for large language model. By shifting the focus from data distribution to sample-level uncertainty, MCMFL provides a novel framework for deploying large AI models via FL in IoT scenarios, mitigating the critical challenge of DH and enhancing model robustness.

The Internet of Vehicles (IoV) is playing a pivotal role in advancing intelligent transportation systems. Deploying the drone as edge nodes in IoV networks has emerged as a promising solution to enhance the communication coverage and energy efficiency (EE). However, the existing drone deployment and resource allocation strategies often lack the necessary intelligence and adaptability to respond to the dynamic traffic conditions. To address these challenges, we leverage machine learning (ML) technology to optimize EE by jointly optimizing small base station (SBS) dormancy and drones’ 3-D positioning—a problem recognized as NP-hard. To tackle this problem, we propose an energy-efficient multi-drone 3-D deployment with SBS dormancy (MUD-SBSD) algorithm, which decomposes the problem into two manageable phased issues. First, a dormant strategy based on the base station centrality (BSC) metric is developed to switch SBSs to a dormant state during low-traffic periods. Second, the horizontal positions of drones are optimized using the k-means algorithm, followed by determining the optimal drone heights via the genetic algorithm (GA). Extensive simulations validate the proposed algorithm can achieve a 41% improvement in EE and a 15% increase in communication coverage rate compared to the existing strategies. These results not only highlight the effectiveness of the proposed solution but also underscore its relevance in enhancing the performance and sustainability of IoV networks, paving the way for more intelligent and responsive transportation systems.

The vehicle road cooperation system is designed to enable intelligent and collaborative communication between vehicles and vehicles, infrastructure, and pedestrians to reduce road accidents and improve road efficiency. As a critical component of this, vehicle-to-vehicle (V2V) communication is expected to achieve efficient information interaction between vehicles and support services, such as collision warning and operation assistance. However, due to the high mobility of vehicles and channel fading, V2V communication suffers from link instability, high latency, and low-resource utilization. To address these issues, this article proposes a high-reliability low-latency intelligent geographic routing (HRLLIGR) protocol based on the greedy perimeter stateless routing (GPSR) protocol to improve its performance in such highly dynamic networks. The main mechanisms of HRLLIGR include a reliable greedy forwarding algorithm based on the evaluation of link stability metrics, a low-latency area prediction forwarding algorithm for routing voids, and an intelligent routing update mechanism to reduce routing overhead. Simulation results suggest that the HRLLIGR protocol outperforms traditional routing protocols, such as ad-hoc on-demand distance vector (AODV), optimized link state routing (OLSR), and GPSR regarding reliability, latency, and overhead. Specifically, compared to the GPSR protocol, it achieves 13.7% improvement in packet delivery rate, 21.3% reduction in average end-to-end latency, and 15.1% decrease in routing overhead.

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.

Autonomous aerial vehicle (AAV)-enabled Internet of Things (IoT) exhibits great application potential with its wide coverage, flexible network topology, and diversified services. However, ensuring communication security and efficient spectrum resource utilization in multiuser access scenarios is challenging, given the open nature of AAV channels and the proliferation of communication devices in IoT. To address the above challenges, this article proposes a novel reconfigurable intelligent surface (RIS)-aided AAV collaborative communication framework, where RIS-equipped AAV flexibly serves multiple users. In this work, a rate splitting multiple access (RSMA)-based secure transmission scheme is proposed, where the split public information serves both as useful signals and noise to disrupt eavesdropping. For the proposed scheme, a sum secrecy rate maximization problem is formulated and solved by optimally deploying the AAV’s location, designing the RIS’s phase shift, and power allocation. For this nonconvex problem with a couple of variables, we decompose it and form three separate subissues. Specifically, leveraging the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques, we first exploit an iterative algorithm for optimizing beamforming vectors and phase-shift matrix of RIS, and the optimal position of the AAV is obtained according to the deep deterministic policy gradient (DDPG). Then, we design an alternating optimization (AO) framework for joint solving. Finally, simulation results validate the efficacy of the proposed scheme in enhancing security, e.g., relative to the nonorthogonal multiple access (NOMA) scheme and benchmark scheme, the secrecy rate of the proposed scheme increased by 29.7% and 71.9%, respectively.

The Industrial Internet of Things coupled complex networks (IIoT-CCNs) contain a large and diverse number of nodes, whose states are inherently random and complex. In this context, federated learning can play a significant role. It allows different nodes or subsets of the IIoT-CCNs to train local models without the need to transfer all their raw data to a central server. This paper firstly establishes a more general IIoT-CCNs model, where the communication channel may not be completely opened between coupled nodes. When integrating federated learning, the nodes can collaboratively train a global model while safeguarding their own data sovereignty. Secondly, a pinning impulsive controller is designed to make IIoT-CCNs realize synchronization. Thirdly, by utilizing regroup method and step-function methods, some useful and novel synchronization stability conditions have been obtained. In the federated learning framework, these stability conditions can be adapted to ensure the convergence of the collaborative learning process. Then, a simulation is conducted to make sure the correctness of results. Finally, the application of synchronization with regard to responsible image communication is executed. The feature values of node data can be extracted through the image encryption algorithm, and then using synchronized chaotic sequences obtained from encryption to ensure the security of image information in the transmission process. Even in a federated learning-enabled IIoT-CCNs, the encrypted data and synchronized sequences can be managed in a way that respects the privacy and security requirements. It also ensures that legitimate recipients can simultaneously obtain the correct keys according to the image encryption algorithm. By utilizing the histogram and adjacent pixel correlation analysis to verify the effectiveness of the encryption scheme.

The integration of Cognitive Internet of Things (IoT) sensors with autonomous aerial vehicles (AAVs) has transformed industrial sectors, such as monitoring, logistics, and infrastructure inspection. However, the advancement of visual synthesis technologies like generative adversarial networks and diffusion models has introduced significant risks by enabling the creation of highly realistic AI-manipulated content, making the detection of falsified imagery increasingly challenging. Existing detection methods, largely based on convolutional neural networks (CNNs), focus primarily on global image features and often overlook crucial relational connections, limiting their robustness and generalization. To overcome these limitations, we propose a novel dual-stream architecture that integrates global feature extraction with relational feature learning. By combining the CLIP model with a graph-based topology, our approach identifies hard-to-detect samples and processes them through a graph convolutional network (GCN) to capture both structural and relational information. Extensive evaluations validate the robustness and generalization ability of our method across various generative models and real-world perturbations. This approach offers a scalable and reliable solution to ensure data integrity in industrial IoT systems, helping to preserve societal trust in AI-driven applications.

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.

Amid a rapidly evolving information era, large-scale multimodal knowledge discovery in computational social systems emerges as a key research domain. Large language models (LLMs) play a crucial role in this field, providing contextual understanding and task adaptability. Yet, centralized training of LLM raises privacy concerns. Federated learning (FL) offers a distributed alternative, but it struggles with data heterogeneity and security issues related to model parameters. To this end, we propose a robust aggregation method that leverages the relative total distance of models to improve global model performance in heterogeneous settings, complemented by Cheon-Kim-Kim-Song (CKKS) encryption to secure parameters against parameter stealing without performance loss. Extensive numeric results show our approach excels in LLM testing, scoring 3.74 on MTBenchmark and 8.17 on Vicuna, outperforming state-of-the-art FL methods against data heterogeneity challenges. It also achieves consistent gains on image datasets such as SVHN, CIFAR10, MNIST, TinyImageNet200, and CIFAR100, TinyImageNet200. In summary, our method offers an effective solution for secure multimodal data analysis in computational social systems.

In recent years, data-driven remote medical management has received much attention, especially in application of survival time forecasting. By monitoring the physical characteristics indexes of patients, intelligent algorithms can be deployed to implement efficient healthcare management. However, such pure medical data-driven scenes generally lack multimedia information, which brings challenge to analysis tasks. To deal with this issue, this paper introduces the idea of ensemble deep learning to enhance feature representation ability, thus enhancing knowledge discovery in remote healthcare management. Therefore, a multiview deep learning-based efficient medical data management framework for survival time forecasting is proposed in this paper, which is named as "MDL-MDM" for short. Firstly, basic monitoring data for body indexes of patients is encoded, which serves as the data foundation for forecasting tasks. Then, three different neural network models, convolution neural network, graph attention network, and graph convolution network, are selected to build a hybrid computing framework. Their combination can bring a multiview feature learning framework to realize an efficient medical data management framework. In addition, experiments are conducted on a realistic medical dataset about cancer patients in the US. Results show that the proposal can predict survival time with 1% to 2% reduction in prediction error.

The unmanned aerial vehicle-assisted 6G supported intelligent transportation systems (UAV-assisted 6G-ITS) have great potential to make transportation systems efficient, smart, and sustainable. However, when connected and autonomous vehicles communicate with UAVs, it can lead to issues such as energy consumption and overlapping interference, which can affect system performance. Therefore, this paper proposes an interference tolerance-based energy harvesting (EH) resource allocation (IT-EHRA) strategy, aiming to improve the energy efficiency (EE) of UAV-assisted 6G-ITS and mitigate the overlapping interference. Firstly, we established an interference hypergraph model and analyzed the types and relationships of interference in the network. Based on this model, an interference tolerance method was designed to alleviate overlapping interference. Then, an EH optimization model with imperfect channel state information (CSI) is proposed based on the interference model, aiming to maximize the network EE and reduce energy consumption. Finally, we adopt the IT-EHRA algorithm to reduce the impact of imperfect CSI and use duality theory to obtain the optimal solution, thereby achieving maximum network EE. Simulation results show that the algorithm effectively ensures the EE requirements of the network, improves the throughput of the network, and promotes the sustainable development of the network.

To accommodate the proliferation of Internet-of-Things (IoT) applications, next-generation wireless communication networks, particularly the sixth-generation (6G), are expected to offer excellent support for the massive access of machine-type communication (MTC). In this paper, we investigate the grant-free random access (GFRA) employing orthogonal preambles in cell-free massive multiple-input multiple-output (mMIMO), which shows immense potential for enabling massive connectivity. In particular, we take into account the capture effect, defined as successful decoding despite preamble collisions, when the received signal-to-interference-plus-noise ratio (SINR) exceeds a predefined threshold. To this end, we develop an analytical framework to model GFRA with the capture effect adopting stochastic geometry. Subsequently, approximate analytical expressions for the received SINR and the access success probability for the typical GFRA frame structure are derived. Furthermore, leveraging these theoretical expressions, we formulate an optimization problem to determine the optimal preamble length that maximizes effective throughput. Simulation results validate the accuracy of our theoretical analyses and demonstrate the superior access performance of the optimized frame structure, whereas a frame structure with a constant preamble length does not consistently attain maximum effective throughput across varying user densities.

Incorporating an active reconfigurable intelligent surface on an autonomous aerial vehicles (AAVs), denoted as an aerial reconfigurable intelligent surface (ARIS), introduces a novel dimension for secure transmissions. Given the constraint of limited battery capacity in AAVs, energy management emerges as a key challenge within AAV networks. In response, we propose a secure energy efficiency (SEE) transmission scheme for ARIS networks, where active ARIS is strategically deployed to enhance information security. In addition, a SEE optimal problem is formulated by considering the imperfect wiretap channel state information to optimize the active beamforming vector and the ARIS position. For this non-convex problem, we first reformulate the fractional SEE objective into an equivalent form and subsequently decompose it into two distinct subproblems: optimizing the AAV’s position and designing the active beamforming. For the AAV’s position optimization, we propose a sophisticated deep deterministic policy gradient algorithm that enables the AAV to autonomously determine the optimal ARIS position through a self-learning strategy. Regarding beamforming design, we transform this aspect into a quadratic constrained quadratic programming problem and design an alternating direction multiplier method to optimize the reflection coefficient. Subsequently, an alternating optimization algorithm is proposed to synergistically solve these subproblems. Empirical simulations validate our proposed scheme, indicating an improvement in SEE of up to 47.2%. This significant improvement underscores the efficacy of the proposed ARIS-assisted secure transmission scheme in enhancing both security and energy efficiency in AAV networks.

This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS’s phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV’s trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.

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.

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.

With the development of internet of things (IoT) technology and its wide application in urban traffic, the next-generation vehicle-to-everything (V2X) communication network should support high-capacity, ultra-reliable, and low-latency massive information exchange to provide unprecedentedly diverse user experiences. The development of the sixth-generation (6G) mobile communication technology will pave the way for realizing this vision. Reconfigurable intelligent surfaces (RISs), a critical 6G technology, is expected to make a big difference in V2X communications when used in conjunction with unmanned aerial vehicles (UAVs), allowing for extremely increased communication capacity and reduced latency. We propose a UAV-enhanced RIS-assisted V2X communication architecture (UR-V2X) suitable for urban three-dimensional (3D) IoT traffic and design an adapted MAC protocol UR-V2X-MAC to accomplish communication resource allocation and scheduling. The UAVs are used as access points and resource allocation centers, while the RISs are used as passive relays to assist V2X communication in proposed architecture. To improve the performance of UR-V2X-MAC, we use a distributed optimization algorithm in the message report phase of the protocol to maximize the system capacity by allocating the transmit power and alternately optimizing the RIS phase shift matrix. We analyze the delay and system capacity characteristics under different parameter settings through theoretical derivation and protocol performance simulation. Analysis and simulation results are presented to demonstrate that UR-V2X-MAC achieves a reduction in communication delay and a significant increase in system capacity through detailed design and alternate optimization compared to the existing V2X MAC protocol and no-RIS case.

Effective data management schemes have always been the major demand in universal industrial Internet of Things (IoT) systems, especially in resource-constrained scenarios. In realistic wastewater treatment process (WTP), only limited monitoring data resource can be available due to some digital constraint. Aiming at this practical issue, this work explores utilization of deep neural network to deal with such practical issue in the objective situation. Therefore, a deep learning-based data management scheme for intelligent control of WTP under resource-constrained IoT systems, is proposed in this paper. Firstly, a specific data encoding and preprocessing approach is developed for the objective business scenario. Then, the detailed workflow of a deep neural network structure is applied to predict key intermediate parameters which can further guide control decision. Finally, a comprehensive series of experiments are conducted on a real-world dataset which covers a range of one year. Both efficiency and robustness of the proposal are tested by introducing several performance metrics. The results show that it can have proper prediction effect in such resource-constrained environment, which can facilitate following intelligent control operations.

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.

Healthcare systems, empowered by the integration of Artificial Intelligence (AI) and Internet of Things networks, are undergoing significant advancements, ushering in a new era of enhanced treatment experiences and improved quality of life. Edge computing plays a pivotal role as an architectural enabler; however, it also presents numerous energy-related challenges spanning sensors, communication, and edge devices. One of the most formidable challenges is the proliferation of complex communication protocols across various devices, including sensors, reconfigurable intelligent surfaces, smart devices, and edge servers, leading to substantial carbon emissions and energy consumption. To address this challenge, this paper introduces a low-carbon, sustainable edge architecture leveraging AI techniques. Specifically, we develop a deep learning-based radio frequency fingerprint access protocol to facilitate real-time and energy-efficient device access between smart devices and edge gateways. Building upon this foundation, we propose a hybrid quantum-classical optimization algorithm to achieve green data transmission at lower layers for artificial intelligence of things healthcare systems. Simulation results demonstrate that our optimized architecture achieves over 99% identification accuracy using a signal dataset of 50GB obtained from real-world smart devices and practical gateways in a real-world environment, all while maintaining energy-efficient data delivery.

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.

For the future 6G-enabled massive Internet of Things (mIoT), how to effectively manage spectrum resources to support huge data traffic under the large-scale overlapping caused by the dense deployment of massive devices is the imperative challenge. In this paper, a novel hypergraph interference model is designed, and two reinforcement learning (RL)-based resource management algorithms in the 6G-enabled mIoT are proposed to enhance the network throughput and avoid overlapping interference. Then, based on the hypergraph interference model, the resource management problem of execution network throughput maximization is theoretically formulated under large-scale overlapping interference scenarios. To handle this problem, we convert it into a Markov decision process (MDP) model and then deal with this MDP model through the advantage actor-critic (A2C)-based resource management algorithm and asynchronous advantage actor-critic (A3C)-based resource management algorithm, which aim to maximize network throughput of the spectrum resource allocation among massive devices. The simulation results verify that the proposed algorithms can not only avoid large-scale overlapping interference but also improve the network throughput.

Abstract With oceanic reverberation and a large amount of data being the main sources of interference for underwater acoustic target detection, it is difficult to obtain a more robust detection performance by relying on the traditional constant false alarm rate (CFAR) detection method. An adaptive sonar CFAR detection method based on a back propagation (BP) neural network is proposed. The method combines the artificial intelligence algorithm and the traditional detection algorithm, and uses the classification ability of the algorithm to select the detection algorithm, which can effectively improve the adaptation ability of the algorithm and the environment and the false alarm control ability. The method combines the artificial intelligence algorithm and the traditional detection algorithm, and uses the classification ability of the algorithm to select the detection algorithm, which can effectively improve the adaptation ability of the algorithm and the environment and the false alarm control ability. This method uses a BP neural network to train the target echo signal to complete the clutter background classification and establish the clutter background recognition classification set. According to the output result of each classification, the best CFAR detector is selected from four CA/SO/GO/OS‐CFAR detectors to detect the target. The simulation results show the detection performance of the proposed method in a uniform environment, a multi‐target environment, and a clutter edge environment. The results show that the environment adaptability is strong for different clutter backgrounds, which further improves the control ability of false alarms under a non‐uniform background.

With the development of the Internet of Things (IoT) , a large-scale, heterogeneous, and dynamic distributed network has been formed among IoT devices. There is an extreme need to establish a trust mechanism between devices, and blockchain can provide a zero-trust security framework for IoT. However, the efficiency of the blockchain is far from meeting the application requirements of the IoT, which has become the biggest resistance to the application of the blockchain in the IoT. Therefore, this paper combines sharding to build an effective Blockchain-based IoT data storage scheme (S-BDS) . Sharding can solve the problem of blockchain capacity and scalability. While the blockchain provides data immutability and traceability for the IoT, it also brings huge demands for data credibility verification. The communication delay in the IoT system seriously affects the security of the system, while the Merkle proof of traditional blockchain occupies a lot of communication resources. This paper constructs Insertable Vector Commitment (IVC) in the bilinear group and replaces the Merkle tree with IVC to store IoT data in the blockchain. The construct has small-sized proof. It also has the ability to record the number of updates, which can prevent replay-attacks. Experiments show that each block processes 1,000 transactions, the proof size of a single data piece is 30% of the original scheme, and proofs from different shards can be aggregated. IVC can effectively reduce communication congestion and improve the stability and security of the IoT system.

Abstract Face Recognition is a challenging task for recognizing and detecting the identity of an individual. Although, plethora of work has already been done in the field of pattern recognition still there has been lot which has not been addressed in any of the literature. In the current research, we have presented a comparative analysis using three popularly known techniques for face recognition namely, Principal Components Analysis (PCA) using Eigen Faces, Hidden Markov Model (HMM) using Singular Value Decomposition, and Artificial Neural Network (ANN) using Gabor filters. These techniques are implemented and evaluated using various measuring metrics such as false acceptance, false recognition rate, and so on. We used ORL and Yale Face dataset to test the robustness of implemented algorithms. Results show that ANN model for face recognition outperforms the other two techniques by achieving more accurate results and shows the highest recognition rate of 97.49% on ORL database. Moreover, it is also observed that ANN model shows the minimum error count of about 2.502% on ORL database while it is 3.5% on Yale Face dataset. To evaluate further, the implemented techniques are compared with best known techniques in class implemented by various researchers.

Summary Due to the growth of applications and the integration of new customers into the world of computing systems, computing needs to be changed and to become more powerful and flexible than before. Meanwhile, cloud computing is presented as a model beyond a system that is currently capable of answering most request needs. Flexible infrastructure for cloud computing and virtualization technology provide new features to support business activities. Clouds are a very important topic that used secure management tools for storage, security and securing data centers in a flexible manner. One of the important matters in cloud technologies is virtual machine migration (VMM). There are different ways to implement the VMM, but because of the limitation of resources' energy, energy management is also very important and challenging. Due to the NP‐hard nature of this problem, this paper presents an energy‐aware VMM engine for cloud computing using the discrete bacterial foraging algorithm as a new collective behavior‐based metaheuristics algorithm. The CloudSim simulator is employed to investigate the efficiency of this method. The obtained results have shown that the proposed method improves the energy consumption and the migration count.

The car-following model describes the microscopic behavior of the vehicle. However, the existing car-following models set the drivers’ reaction time to a fixed value without considering its dynamics. In order to improve the accuracy of car-following model, this paper proposes Deep Feature Learning-based Car-Following Model (DeepCF), a car-following model based on fatigue driving and Generative Adversarial Networks (GAN). The model is composed of the drivers’ reaction time model and the car-following decision algorithm. First, we regard driving fatigue as the starting point to study the influence of driving time and the acceleration of the preceding vehicle on the drivers’ reaction time, and develop a coarse-grained drivers’ reaction time model. Secondly, considering the impact of fatigue driving on car-following decisions, we utilize GAN to generate a driving decision database based on reaction time and use Euclidean distance as a decision search indicator. Finally, we conduct experiments on a real data set, and the results indicate that our DeepCF model is superior to baseline models.

The transmission speed of big data in multimedia, social networking, and web services, can be enhanced by image compression technology. Fuzzy vector quantization (VQ) image compression is a significant tool for achieving a codebook to illuminate lineaments of big data. A functionality combination of PSO and GSA algorithms, with parallel running, have been used to design a fuzzy-VQ image compression system. The improvement of the compressed image quality has been executed by carrying out suitable parameters selection using the proposed algorithm. Comparative study between sophisticated learning schemes and Linde-Buzo-Gray (LBG) based VQ learning process has been introduced. The proposed algorithms provide an achievement in the behavior of pure image compression.