Next-generation industrial networks incorporate diverse devices and technologies, including cloud- and fog-based systems and large-scale Industrial Internet of Things (IIoT) applications. These heterogeneous environments comprise thousands of sensors, actuators, controllers, and geographically distributed zones, posing significant challenges for authentication mechanisms that must scale while remaining secure and practical under real industrial conditions. Current blockchain-based decentralized identity-management systems are characterized by high transaction overhead, reliance on computationally intensive cryptographic operations, and partial dependence on trusted entities, all of which limit their scalability and practical deployment. To address these issues, in this paper, we propose a lightweight, blockchain-based identity-management solution that integrates epoch- and batch-based registration with sparse Merkle trees (SMTs). Device registrations are grouped into fixed epochs, and cryptographic costs are amortized across multiple devices to substantially reduce per-device overhead. The SMT structure generates verifiable, reusable proofs that preserve privacy while enabling efficient verification; sensitive device information is never revealed during authentication. Fog nodes act as decentralized verifiers, supporting distributed trust without continuous interaction with the blockchain. Experimental results show that the system can be practically deployed on Raspberry Pi and ESP32 devices, reliably perform identification with around 1000 total messages, and operate at the tested upper bound of 7000 devices distributed over 20 fog nodes, all while keeping the blockchain storage overhead under 5 MB.

Internet of Audio Things (IoAuT) is an emerging paradigm that integrates intelligent audio processing, ubiquitous connectivity, and edge–cloud computing resources to enable a network of devices capable of sensing, analyzing, and exchanging sound-based information seamlessly across distributed environments. This technology supports a wide range of applications, ranging from interactive musical performances to intelligent environmental monitoring, that play a significant role in everyday life. Although this technology holds great potential and could be highly useful across many domains in the future, its reliance on real-time audio streaming and distributed sensing introduces several communication and Quality of Service (QoS) challenges, such as latency, bandwidth limitations, packet loss during audio transmission, resource management, and energy-efficient networking, all of which directly affect its usability and scalability. In this paper, we provide the first holistic analysis of IoAuT applications from a QoS and communication perspective. We systematically review artistic, functional, and industrial use cases to explore how performance and sustainability trade-offs shape system design. Unlike prior reviews, our study introduces a unified architecture taxonomy and sustainability-enabled QoS metrics to evaluate network efficiency, interoperability, and energy use. We further examine the roles of edge computing, adaptive streaming, and dynamic resource allocation in achieving reliable and low-latency audio transmission. Finally, the paper highlights open challenges and suggests future research directions to help build IoAuT applications that can scale, work well with other systems, and reduce their impact on the environment.

Cooperative robotic healthcare systems (CRHS) are advanced technologies that enhance medical services by allowing robots to collaborate with healthcare professionals, making clinical practices safer and more efficient. However, for these systems to work efficiently, they need fast and reliable communication and computation, all while managing the limited resources and energy available in robot-embedded sensors. Therefore, this survey focuses on clarifying how various networking and computing decisions impact different aspects of this technology, such as latency, reliability, Quality of Service (QoS), and scalability, etc. We evaluated the recent research on resource allocation, as well as orchestration in edge, fog, and cloud computing, to have a holistic overview of what has been done so far in this field. Moreover, we analyzed communication technologies such as 5G, Ultra-Reliable Low-Latency Communication (URLLC), Time-Sensitive Networking (TSN), Software-Defined Networking (SDN), Network Function Virtualization (NFV), and network slicing to understand their role in RHCS QoS metrics. Our synthesis finds that (i) placing perception/control close to the edge consistently decreases end-to-end delay, (ii) SDN/NFV and time-sensitive networking improve predictable and real-time operation in multi-robot hospital environments; and (iii) learning-based scheduling and offloading often outperform static heuristics in variable workloads. Despite these advancements, we have identified several challenges in the literature, such as limited interoperability between different vendors and a lack of standardized benchmarks for Quality of Service (QoS), etc. Therefore, we conducted a comparative analysis to understand how specific design choices influence the QoS metrics of this technology. In addition, we have proposed potential research directions that address the open challenges to ensure the real deployment of this technology.

The expansion of 6G-enabled IoT networks demands integrated solutions for optimizing computational, communication, and energy resources. Existing methods often treat power efficiency and resource allocation independently, limiting overall performance gains. This study introduces FLARE, a federated learning–based framework for adaptive resource management and optimization in 6G-IoT. FLARE integrates an adaptive learning rate scheduler ηt= η0/(1+αt), gradient sparsification (Top-k) and uniform quantization to minimize communication overhead, and model pruning with weight quantization for energy reduction. A novel dynamic route optimization algorithm jointly minimizes delay and power consumption via continuous flow-based updates, supported by a virtualization-aware server activation model. An economic optimization module applies dynamic pricingPdynamic(t) and incentive mechanisms to balance demand and cost. NS-3 simulations on Edge-IIoTset, N-BaIoT, and IoT-23 show that FLARE improves resource utilization by up to 5%, power efficiency by 4%, and network performance by 3–5% compared to leading baselines under varied demand scenarios.

Camera-based facial-emotion recognition (FER) suffers from poor lighting, occlusion, and privacy exposure, whereas mmWave-only solutions lack the spatial detail required for fine-grained affect analysis. To close this gap, we present SwinLSTM-EmoRec (Shifted Window Transformer + Long Short-Term Memory Emotion Recognition). This non-contact dual-modal framework fuses micro-Doppler signatures captured by a TI IWR1443 mmWave radar with RGB imagery while treating radar as the primary, identity-obscured source and adaptively limiting reliance on RGB. Privacy is preserved because the cross-attention gate down-weights or bypasses RGB when illumination is poor or when potential identity exposure is detected, leaving decisions dominated by illumination-invariant radar dynamics. A shifted-window Swin Transformer extracts spatial facial cues, an LSTM models temporal radar dynamics, and a lightweight cross-attention layer aligns the two streams, boosting F1 by up to 4% over early, late, and self-attention baselines. On a 50-participant interactive-gaming dataset recorded under varied lighting and distances of 0.5–2 m, the system achieves 98.5% accuracy (F1 ≈ 0.98). It maintains 33.9 ms end-to-end latency on a 15 W Jetson Xavier NX edge device. Performance remains > 92% at 2 m, demonstrating robust, privacy-preserving FER robust, privacy-aware emotion sensing suitable for smart-home, tele-health, and e-sports IoT applications.

The deployment of battery charging stations (BCS) has emerged as a critical bottleneck in robotic mobile fulfillment systems (RMFS), directly governing operational continuity and system-level throughput. Optimizing BCS deployment under multi-visit charging demands remains a significant challenge, particularly in dynamic environments with fluctuating workloads and spatiotemporal constraints.We propose CLAP-IGA (Integrated Genetic Algorithm Considering Charging Logic and Allocation Path), a constraint-embedded evolutionary framework that jointly optimizes BCS layout, robot routing, and multi-visit task scheduling. CLAP-IGA incorporates a rolling-horizon replanning mechanism and embeds real-time battery state tracking, time-varying order arrival dynamics, and spatiotemporal path conflict management directly into the evolutionary process. Two deployment strategies—internal and external BCS configurations—are systematically compared to characterize their trade-offs across varying operational scales. Experiments across three operational scales demonstrate that CLAP-IGA reduces total completion time by up to 30% and improves charging station utilization by 18% relative to standard GA, greedy, and DQN baselines. The findings further reveal a counterintuitive capacity-threshold interaction: naively expanding BCS capacity can paradoxically degrade throughput, underscoring the necessity of co-designing charging policy and infrastructure rather than optimizing either in isolation.

Radio frequency identification (RFID) systems are an indispensable part of many critical Internet of Things (IoT) applications, including supply chain management and access control. Ensuring strong security in these systems is critical to safeguarding sensitive information and protecting user privacy. In recent years, in order to meet the diversified security needs of RFID systems, authentication and key protocols based on physical unclonable functions (PUFs) have received wide attention. Nevertheless, existing protocols typically require RFID tags to pre-store an excessive number of secret credentials and impose considerable computational and communication overheads, which prove challenging for resource-constrained RFID tag. Additionally, certain lightweight protocols fall short of achieving their intended security and functional objectives, exhibiting insufficient anonymity and untraceability, and vulnerability to desynchronization attacks. To address these critical challenges, this paper first proposes a lightweight anonymous authentication and key agreement protocol designed for an ideal PUF environment. The proposed protocol integrates the arbiter PUF with cryptographic hash functions, providing robust resistance to potential attacks while minimizing system overhead. Subsequently, an enhanced protocol specifically tailored for noisy PUF scenarios is presented. This protocol employs a fuzzy extractor to reliably derive stable keys from noisy PUF responses, thereby mitigating the instability caused by inherent noise. Through comprehensive security analysis and formal verification, as well as performance evaluations compared with existing state-of-the-art protocols, both protocols are demonstrated to overcome the limitations of prior protocols and provide efficient and practically feasible solutions well suited for resource-constrained RFID environments.

Mobile crowd sensing (MCS) has gained much attention as an important component of next-generation Internet in recruiting workers to collect data to build and deliver services to users for profit. However, recruiting reliable workers with low cost remains a critical challenge under the Information Elicitation Without Verification (IEWV) problem, where worker data quality cannot be directly observed. A large number of studies have been conducted assuming known data quality of workers and using incentives to motivate them to actively participate in data collection. Such assumptions limit their applicability in real-world MCS systems, as platforms cannot directly observe data quality after collection. Therefore, recruiting credible workers to maximize platform profit remains a challenging issue that has not been adequately addressed. In this paper, an LSTM-assisted Trust Worker Recruitment (LTWR) scheme is proposed to maximize platform profit in MCS. First, we propose a trust evaluation mechanism that utilizes the credible data to estimate the quality of workers, effectively addressing the IEWV problem. Second, an LSTM-based data completion method is employed to reduce sensing cost while preserving data quality. Third, we model platform revenue as a function of data quality and design a worker recruitment strategy to maximize profit. Finally, extensive experiments validate the effectiveness of the proposed LTWR scheme in terms of data quality, cost, and profit.

In resource-constrained Internet of Things (IoT) environments, lightweight deep learning is crucial for Radio Frequency Fingerprint Identification (RFFI). However, existing lightweight designs primarily rely on group convolution with a “hard split" topology, which strictly isolates channels and blocks inter-group information sharing, impairing feature extraction. To address this, we propose overlap convolution, a novel operator employing a “soft coverage" mechanism to facilitate inter-group interaction. By enabling tunable channel overlapping, this method unifies and generalizes standard and group convolutions, restoring inter-group interaction without requiring additional mixing. We then analyze the approach by introducing structural constraint entropy as an interpretive framework to investigate the information flow capacity. Theoretical analysis demonstrates that overlap convolution offers expanded structural flexibility to achieve enhanced learning ability. Furthermore, we establish the OverlapConv framework for systematic integration of the operator into networks. Within this framework, we develop an automatic parameter acquisition strategy based on a differentiable-to-discrete transition mechanism to efficiently narrow down the search space for optimal settings. Extensive evaluations across diverse datasets (LoRa and UAV RFFI) and multiple backbones (MobileNet, ShuffleNet, and EfficientNet) confirm the method’s effectiveness as a universal plug-and-play module. Notably, our approach achieves an accuracy gain of 29.22% on the Fire block, and improves the accuracy (averaging 2.1% and 2.47% on the two datasets) of FasterNet, EfficientNet, and LMSCNet with reduced FLOPs.

In Intent-based Vehicular Edge Computing (VEC) networking, escalating demands for computational offloading and resource management in dynamic urban environments necessitate innovative solutions. This paper proposes a novel RIS-assisted NOMA-VEC framework that empowers vehicle users (VUs) to offload arbitrary task portions to multiple edge servers via any available subcarrier. This approach overcomes limitations posed by heterogeneous local computing capabilities and stringent latency constraints. By leveraging Reconfigurable Intelligent Surfaces (RIS) to enhance channel conditions through both direct and reflected links, our framework significantly improves communication reliability and offloading efficiency. To minimize the average weighted energy consumption of VUs under time-varying channels and traffic dynamics, we formulate a joint optimization problem integrating offloading decisions, power allocation, and transmission time scheduling. Addressing the problem’s inherent complexity, characterized by multi-variable coupling and non-convex constraints, we develop a two-stage decomposition strategy: Offloading decisions are dynamically adapted to environmental fluctuations using a Proximal Policy Optimization (PPO)-based algorithm, while resource allocation is resolved through a hybrid Genetic Algorithm (GA) and Sequential Least Squares Programming(SLSQP) approach, efficiently navigating combinatorial and non-convex landscapes. Extensive simulations demonstrate that our framework reduces VU energy consumption by 11.12% compared to baseline methods, validating its superior efficiency in RIS-enhanced VEC systems.

Deep learning-based image classification models have been widely applied in the Industrial Internet of Things (IIoT), but studies have shown that adversarial attacks can cause misclassification of these models, affecting the reliability and security of IIoT systems. Aiming at the low transferability of adversarial examples caused by non-targeted perturbation locations in existing black-box adversarial attack methods for image classification models, this paper presents an adversarial example generation method with multi-layer mapping of heatmaps based on encoder-decoder structure. The encoder is constructed based on local mappings, where the gradient-based class activation mapping method generates a feature heatmap of the original image. This heatmap guides the target label mapping to the salient regions, producing a local label feature map, which is then embedded into different encoding layers. The heatmap is integrated to optimize the decoder, restricting the perturbations generated by the output layer to remain within the salient regions, thereby enhancing the location specificity of the perturbations. An adversarial transformation module is designed to apply random affine transformations to the adversarial examples generated by the encoder-decoder, increasing the diversity of the adversarial examples while enhancing their transferability and robustness. The proposed method is evaluated through experiments conducted on ImageNet and MSCOCO datasets. Compared with mainstream methods, the results show that the proposed method improves the average attack success rates on six different black-box models for eight target categories by 6.5% and 7.5%, respectively. Additionally, the perturbation regions of the generated adversarial examples are reduced by 30% and 18%, and the overall perturbation magnitudes are decreased by 18% and 11%, respectively. The proposed method effectively enhances the transferability of adversarial examples while reducing the perturbation regions.

The rapid growth of sensing-enabled mobile devices has accelerated mobile crowdsensing (MCS), enabling large-scale data collection. However, because tasks are location-dependent, protecting geographic privacy and ensuring data reliability remain critical challenges. In practice, honest-but-curious platforms and malicious workers submitting falsified reports may lead to incorrect recruitment and degraded system performance. Therefore, achieving bilateral location privacy and mitigating falsified data are essential problems. A Trust-aware and bilateral Location-Private Worker Recruitment scheme, named TLPWR, is proposed to address these challenges. TLPWR safeguards both worker and task locations during the recruitment process and enables continuous updates of worker trustworthiness, which have not been simultaneously achieved in prior studies. The scheme employs additive secret sharing combined with a distributed comparison method to securely compute spatial proximity without revealing precise locations. A Multi-Armed Bandit strategy based on the upper-confidence-bound algorithm is further incorporated to recruit high-quality workers, while a trust-oriented evaluation mechanism dynamically assesses and updates worker reliability. Extensive experiments on real-world datasets demonstrate that TLPWR effectively preserves bilateral location privacy and significantly reduces data errors compared with baseline methods, thereby improving data quality while maintaining strong privacy guarantees in MCS systems.

Integrated Sensing and Communications (ISAC) systems face challenges in balancing sparse human resource allocation with high-quality data acquisition, especially in scenarios involving highly mobile workers and uneven task coverage. While Sparse Mobile CrowdSensing (SMCS) offers a resource-efficient solution by inferring unobserved data from partial sensing tasks, two critical issues remain unaddressed: (1) existing data inference methods often assume collected data is trustworthy, neglecting the detrimental impact of malicious workers on data quality, and (2) the lack of privacy-preserving mechanisms in sparse ISAC may deter worker participation, compromising service quality. To address these, we propose a Quality-driven Privacy-preserving Truth Discovery scheme (QPTD), which optimizes sparse sensing and data quality with privacy constraints in ISAC systems. QPTD integrates a trust-aware Deep Reinforcement Learning (DRL) algorithm for dynamic cell selection, prioritizing cells with high-trust workers and minimizing recruitment costs, while a matrix encryption protocol ensures end-to-end privacy protection for both workers and tasks. By leveraging Matrix Factorization (MF) on encrypted sparse data aggregated from high-trust workers, QPTD infers unobserved tasks and safeguards sensitive information. Theoretical analysis proves the robustness of our privacy guarantees against adversarial attacks. Evaluations on real-world datasets demonstrate that QPTD improves ISAC system performance by increasing total data quality by 1.73%–142.07%, reducing estimation errors by 24.82%–75.32%, and cutting recruitment costs by up to 62.03% while ensuring service performance.

The literature repeatedly reports that the unique nature of individual brainwave patterns makes them suitable for identification and authentication, because they are difficult to replicate or forge. Therefore, many researchers have utilized brainwaves for authentication by training traditional deep learning and machine learning models. However, the internal decision processes of these black-box models have not been evaluated in terms of biases, overfitting, large training data requirements, and handling complex data structures, which keep them in a fuzzy state. To address these limitations, a smart system is needed to be develop that could be capable of making the authentication process user-friendly, robust, and reliable. In this paper, we present a deep reinforcement learning-based biometric authentication framework known as "BrainAuth" for personal identification using the gamma ($\gamma$) and beta ($\beta$) brainwaves. This approach improves the accuracy of authentication by using the (i) Dyna framework and a dual estimation technique. Both these technique helps to maintain the integrity of brainwave patterns, which are needed for authentication and understanding of spoofing activities. (ii) We also introduce a layered structure architecture in the proposed model to reduce the time needed for exploration using two deep neural networks. These networks work together to handle the complex data while making decisions in delay sensitive environment. (iii) We evaluate the model on seen and unseen data to verify its robustness. During analysis, the model achieved an equal error rate (EER) of $\approx$ 0.07% for seen data and $\approx$ 0.15% for unseen data, respectively. Furthermore, the analysis metrics such as true positive (TP), false positive (FP), true negative (TN), and false negative (FN) followed by false acceptance rate (FAR), false rejection rate (FRR), true acceptance rate (TAR) revealed significant improvements compared to existing schemes.

Mobile Crowdsensing (MCS) has emerged as a promising paradigm for large-scale, real-time data collection by leveraging the sensing capabilities of widely distributed mobile workers. However, its practical adoption is challenged by privacy risks and unsustainable incentive structures that inadequately compensate for workers' inherent participation costs, leading to diminished motivation, sparse task coverage, and reduced data availability. Existing approaches either provide limited and utility-degrading privacy protection or design incentive mechanisms that incur substantial costs, even under the Nash equilibrium. To bridge this gap, we propose C-PRISM (Compre hensive Privacy-preserving and Behavioral-Incentive Sustainable crowdsensing Mechanism), an integrated framework that seamlessly combines privacy-preserving techniques with behavioral economic incentive design. Specifically, C-PRISM employs ran domized matrix perturbation for fine-grained location protection and a two-phase proxy re-encryption protocol to secure task details and sensing data across evaluation, recruitment, and transmission. Building upon this secure foundation, behavioral economic incentives grounded in prospect theory, the Aronson effect, and a dual reference-point model are introduced to promote sustained worker participation at sub-Nash-equilibrium costs. Rigorous theoretical analysis validates C-PRISM's security and individual rationality. Extensive experiments on real-world datasets demonstrate that C-PRISM increases data collection efficiency by 7.42%-193.75%, improves worker retention by 2.53%-69.78%, and effectively maintains overall system utility.

Spatial Crowdsourcing (SC) has become a popular paradigm for executing spatiotemporal tasks, such as urban sensing, ride-hailing, and last-mile delivery. In many of these service applications, it is common for workers to undertake multiple tasks simultaneously, as this allows them to efficiently complete tasks with a convenient route. In multi-task SC, location privacy, movement cost, and service quality constitute the three most critical factors. However, the inherent contradictions among these factors pose significant challenges in simultaneously ensuring them all, and to date, no existing work has addressed all three factors concurrently. To fill the research gap, in this paper, we propose a Privacy-Preserving, Cost-Aware, and Quality-Enabled Service Planning (PCQ-SP) scheme for multi-task SC. First, we introduce a secure distance acquisition method with additive homomorphic encryption and garbled circuits, which enables the calculation of distances between workers and tasks while preserving both their location privacy. Second, we design a Thompson Sampling-based quality evaluation method to assess each worker's service quality by balancing exploration and exploitation. Third, we propose a minimum-cost flow-driven service planning method to derive the optimized service plans, which aims for high quality and low cost while preserving location privacy. To our knowledge, our PCQ-SP scheme is the first to simultaneously consider location privacy, movement cost, and service quality in SC systems. Extensive evaluations demonstrate the effectiveness of our PCQ-SP scheme in enhancing service quality, reducing movement costs, and ensuring location privacy.

In Sparse Mobile Crowdsensing, many data inference schemes have been proposed without considering the sensing dataset with different trust levels which contains unrecognizable false data, resulting in high inference error and poor practical utility. To address this challenge, we propose a Transformer based Truthful Data Inference (TTDI) scheme that can tolerate false data interference to enhance data inference quality. Firstly, the trust-weighted loss function (TLF) is proposed to reduce the false interference for data inference model training. The TLF implements a differential model parameter updating strategy for data with different trust levels, which can enhance and weaken the effect of high-trust and low-trust data on the trained data inference model, respectively. Secondly, the transformer based truthful spatiotemporal feature extraction (T-TSFE) algorithm is proposed to mitigate the mutual interference between biased spatiotemporal features. The T-TSFE extracts data features through temporal and spatial multi-head attention respectively, which reduces the propagation of false information across the spatiotemporal dimensions. Finally, we conduct extensive experiments with four real-world air quality datasets to verify the effectiveness of the TTDI in data inference quality.

Clustered federated learning (CFL) is a promising solution to address the performance degradation posed by inconsistent data distribution, enhancing model collaborative training through homogeneous data clustering. However, the dynamic nature of user mobility in mobile edge computing (MEC) scenarios results in highly inconsistent data distributions, further complicating collaborative training. So far, existing CFL schemes rely on a central server for clustering, while the prominent clustering algorithms require a predetermined number of clusters. This inevitably leads to a relatively fixed cluster structure, which fails to adapt to user mobility, thereby hurting the model's performance. To capture the inconsistent data distribution in the dynamic user mobility scenario, this paper proposes a decentralized clustered federated learning framework (FedCVM) based on the voter model, which comprises two modules: an iterative adaptive clustering algorithm (ACAS) and a transition probability-based neighbor aggregation algorithm (TPNA). Specifically, ACAS is designed based on the greedy strategy of minimum spanning forest, which achieves optimal cluster division without predetermining the number of clusters, to realize user dynamic clustering. To further improve the model accuracy, the proposed TPNA sets the optimal exchange weights based on the neighbor interaction principle of the voter model to implement precise updates of similar user parameters. We have conducted the theoretical analysis to demonstrate that FedCVM can select the optimal cluster head and ensure model convergence. Extensive experiments on MNIST, CIFAR-10, DermaMNIST, and HARBox demonstrate that FedCVM consistently achieves the highest accuracy while maintaining a favorable trade-off between accuracy and time cost. Moreover, under simulated mobile scenarios, it yields at least a 5.1% improvement in accuracy and achieves at least a 1.29x reduction in communication overhead compared to SOTA methods. We further validate FedCVM through real-world deployment on a heterogeneous edge testbed, demonstrating its effectiveness in dynamic, decentralized mobile scenarios.

In Mobile Crowd Sensing (MCS), privacy and quality are two pivotal issues. Specifically, Bilateral Location Privacy Preservation (BLPP) and Bilateral Data Privacy Preservation (BDPP) represent the most critical dual-bilateral privacy in MCS. However, recruiting high-quality workers typically necessitates calculating the spatial proximity of each worker-task pair and the trustworthiness of the reported data, which tends to introduce significant dual-bilateral privacy risks. Additionally, due to the lack of prior knowledge about the workers' trustworthiness at the initial stage, the platform also faces the exploration-exploitation dilemma in worker recruitment. Existing work either overlooks the service quality of worker recruitment or fails to preserve the dual-bilateral privacy, making these two issues have not yet been adequately addressed. To bridge the gaps by addressing the associated challenges, this paper proposes aQuality-awareDual-BilateralPrivacyPreserving (Q-DBPP) scheme, for upholding service quality under both BLPP and BDPP. Specifically, we present two perturbation-based privacy preservation stages to calculate Degree of Proximity (DoP) and Degree of Trust (DoT) while safeguarding BLPP and BDPP, respectively. Meanwhile, to address the exploration-exploitation dilemma, we employ an upper confidence bound-based high-quality worker recruitment stage, which integrates the estimated DoT and DoP into the reverse auction to comprehensively optimize service quality. To the best of our knowledge, our Q-DBPP scheme is the first to simultaneously achieve high service quality and dual-bilateral privacy in MCS. Theoretical analyses and extensive experiments validate the superior performance of our Q-DBPP scheme in terms of privacy, incentive, efficiency, and quality.

Intelligent adaptive bitrate (ABR) schemes have been widely recognized for their excellent learning strategies. However, existing intelligent ABR methods have limitations, i.e., the lack of logical reasoning capability for video-aware symbolic representations leads to low sampling efficiency and fails to achieve the optimal performance of Bitrate Adaptation. We introduce NeuroBA, a learning-based approach to realize ABR using neuro-symbolic deep reinforcement learning. NeuroBA trains a neuro-symbolic deep network model without making any assumptions about the edge video scene and without relying on a predefined model. Instead, it enables bitrate decision-making under uncertainty and partial observability by knowledge-driven video quality perception in symbolic first-order logic. To enhance wireless signals, we have introduced Intelligent Reflecting Surface (IRS) technology to address this issue. By dynamically adjusting the phase shift of IRS, the throughput performance of wireless networks is significantly improved. Based on trace-driven and real-world experiments covering a variety of edge video scenarios, and network performance metrics, NeuroBA is compared with state-of-the-art ABR schemes, and NeuroBA exhibits superior performance, with an average QoE improvement of 16.58% (BOLA)-25.34% (Fugu). In particular, it outperforms existing baseline approaches even without pre-programmed models and network scenarios assumed for the edge network.

Numerous mobile devices have enabled Mobile Crowdsensing (MCS) to recruit a massive number of workers to sense and collect data for data requestors, thereby supporting a wide range of data-driven services. However, due to its crowdsourcing nature, MCS services face two critical challenges: ensuring privacy preservation and maintaining high-quality data collection. To address these issues, this paper proposes a privacy preserving and data quality optimization framework, named DQO-P5PI, which integrates the Preservation of Five Privacy Information (P5PI) framework with a Data Quality Optimization (DQO) scheme based on Fuzzy sets. Specifically, the P5PI frame work focuses on protecting five types of sensitive information for workers, namely the sensing data, the Reputation, the Identity, the Bid, and the Location (collectively referred to as DRIBL). In the P5PI framework, homomorphic encryption is employed to ensure DRIBL information privacy; the pseudonym mechanism is adopted to protect worker identities during interactions; Credit Verification Methods (CVM) verify the validity of workers' reputation scores, thereby protecting reputation privacy; and a Homomorphic Encryption Comparison Method (HECM) enables the platform to assess worker eligibility and select the top-k workers with the highest comprehensive scores. The DQO scheme further incorporates a Worker Reputation Update Method (WRUM) to dynamically update workers' reputation parameters, and a Credit Rating Method (CRM) that leverages Fuzzy sets to integrate bid values with historical reputation information for more reliable task allocation. We theoretically prove the correctness of the P5PI framework and DQO scheme, and extensive experimental results demonstrate that the proposed framework effectively improves sensing quality while keeping costs low.

Video streaming has emerged as a widely used Internet service, in which adaptive bitrate (ABR) algorithms play a critical role in delivering high quality of experience (QoE). However, existing learning-based ABR methods often suffer from limited generalization in unseen and dynamically changing network conditions. Although some meta-reinforcement learning techniques have been proposed to mitigate this issue, they generally depend on additional online training or fine-tuning. To overcome these limitations, this paper introduces EAStream, an environment-aware ABR algorithm based on meta-reinforcement learning for reliable video streaming services. The method employs a variational autoencoder to extract a latent representation of the current network environment from historical interaction data. This latent variable, along with the current system state, is fed into a policy network that perceives network conditions in real time and adapts bitrate decisions accordingly, without requiring further online training. A comprehensive evaluation is conducted using diverse real-world network traces. Experimental results show that EAStream not only achieves leading performance on in-distribution test sets compared to state-of-the-art ABR algorithms, but also demonstrates superior generalization capability on out-of-distribution test scenarios.

The Internet of Medical Things (IoMT) has attracted the attention of many scholars because of its revolutionary impact on healthcare services. However, the sensitivity of medical data and the complexity of IoMT architectures pose critical challenges to privacy-preserving service design. This survey offers a comprehensive and novel investigation and classification of privacy and security issues in IoMT. We explore the issue from four distinct angles: privacy protection technologies, IoMT architecture, data type variations, and emerging technologies, with each section delving into more detailed analysis. Specifically, privacy protection technologies include cryptography method, Federated Learning (FL), blockchain technology and Differential Privacy (DP). We provide a more detailed classification for popular technologies such as blockchain. Regarding the IoMT architecture, we dissect the attack and defense mechanisms at the perception layer, the transmission and cloud layer, and the application layer. In terms of data types, we discuss the research on privacy protection based on the structural differences of various medical data. In the aspect of emerging technologies, we investigated the impact of four emerging technologies on privacy protection of IoMT. Our survey highlights the comprehensive and novel perspective of the characteristics of IoMT privacy protection, which will promote possible booming research in the future.

Mobile Crowd Sensing (MCS) has emerged a promising paradigm for large-scale data collection, which recruits truthful workers to enhance data quality, thereby maximizing the platform's profit. However, most existing work has the assumption that workers' trust remains stable once verified and truthful workers can consistently provide high-quality data. However, in real-world scenarios, there exist workers whose trust may change dynamically over time, which makes it difficult to ensure high-quality data collection due to the current lack of sustainable trust verification research, thereby damaging the platform's profit. To tackle the above challenge, we propose a novel Truthful Workers Recruitment with Sustainable Trust Verification (TWR-STV) scheme. The proposed TWR-STV scheme identifies the dynamic trust of workers and assesses the data value based on the quality of collected data, thereby selecting high-value workers to maximize the profit. First, we establish a dynamic trust model to capture the change in workers' trust. Second, we propose a stable and trustworthy worker identification method based on a two-layer sustainable data verification to identify workers' trust and the stability of workers' trust. Third, a multi-armed bandit-based stable and trustworthy worker recruitment scheme is proposed, in which high-value workers are recruited to increase platform profit and trustworthy workers who can be effectively verified are prioritized for recruitment to maintain sustainable trust verification. Furthermore, the proposed TWR-STV scheme takes into account the decay effect of time and trust on data value, which is more practical. Finally, we theoretically analyze the regret bound of TWR-STV and conduct experiments on a real dataset. Experimental results show that our proposed solution is superior to existing solutions in terms of total profit.

Temporal Graph Neural Networks (TGNNs) have become indispensable for analyzing dynamic graphs in critical applications such as social networks, communication systems, and financial networks. However, the robustness of TGNNs against adversarial attacks, particularly sophisticated attacks that exploit the temporal dimension, remains a significant challenge. Existing attack methods for Spatio-Temporal Dynamic Graphs (STDGs) often rely on simplistic, easily detectable perturbations (e.g., random edge additions/deletions) and fail to strategically target the most influential nodes and edges for maximum impact. We introduce the High Impact Attack (HIA), a novel restricted black-box attack framework specifically designed to overcome these limitations and expose critical vulnerabilities in TGNNs. HIA leverages a data-driven surrogate model to identify structurally important nodes (central to network connectivity) and dynamically important nodes (critical for the graph's temporal evolution). It then employs a hybrid perturbation strategy, combining strategic edge injection (to create misleading connections) and targeted edge deletion (to disrupt essential pathways), maximizing TGNN performance degradation. Importantly, HIA minimizes the number of perturbations to enhance stealth, making it more challenging to detect. Comprehensive experiments on five real-world datasets and four representative TGNN architectures (TGN, JODIE, DySAT, and TGAT) demonstrate that HIA significantly reduces TGNN accuracy on the link prediction task, achieving up to a 35.55% decrease in Mean Reciprocal Rank (MRR) - a substantial improvement over state-of-the-art baselines. These results highlight fundamental vulnerabilities in current STDG models and underscore the urgent need for robust defenses that account for both structural and temporal dynamics. Code and Data are available at https://github.com/ryandhjeon/hia.

In conventional paradigms of machine learning (ML) and deep learning (DL), models are trained as ’black boxes’ on task-specific datasets prior to deployment. This poses various challenges to the application of AI for spectral adaptation. First, we cannot ensure the reliability of the model, since we do not know how they correlate signal features with their recognition target. We need a method to explain the final stage of decision-making. To address the first challenge, we propose a zero-bias neural network where we replace the penultimate layer of a specific DNN or CNN model with an extended Cosine Similarity Matching layer, called the zero-bias dense layer. This adaptation has been proven not to affect the learning and inferential capabilities of AI models. In this research study, we used the zero-bias neural network (ZBNN) for spectral sensing on the RADIOML 2016.10A dataset, which consists of 11 different modulation types, consisting of both analog and digital signals having varying Signal-to-noise ratio (SNR). To compare the ZBNN accuracy, we used the CNN model having identical hyperparameters. The proposed ZBNN for spectral sensing has the potential to become the fundamental building block for explainable AI, especially in remote spectral sensing.

Neurosymbolic AI combines neural network adaptability with symbolic reasoning, promising an approach to address the complex regulatory, operational, and safety challenges in Advanced Air Mobility (AAM). This survey reviews its applications across key AAM domains such as demand forecasting, aircraft design, and real-time air traffic management. Our analysis reveals a fragmented research landscape where methodologies, including Neurosymbolic Reinforcement Learning, have shown potential for dynamic optimization but still face hurdles in scalability, robustness, and compliance with aviation standards. We classify current advancements, present relevant case studies, and outline future research directions aimed at integrating these approaches into reliable, transparent AAM systems. By linking advanced AI techniques with AAM’s operational demands, this work provides a concise roadmap for researchers and practitioners developing next-generation air mobility solutions.

Accurate travel demand prediction is essential for effective transportation planning, infrastructure development, and resource optimization. Traditional models often lack both interpretability and the ability to capture complex, nonlinear relationships across geospatial and socioeconomic variables. To address this, we propose a Neurosymbolic AI framework that integrates decision tree (DT) based symbolic reasoning with neural network (NN) learning for travel demand forecasting. The model leverages multisource data including geospatial, economic, and mobility datasets and embeds interpretable if-then rules extracted from DTs as additional features in the NN. Our experiments demonstrate that this hybrid approach improves predictive accuracy across key metrics, achieving up to 24.19% reduction in Mean Absolute Error (MAE), 1.79% improvement in R2, and 6.49% in Common Part of Commuters (CPC) compared to NN models. Rules extracted at finer variance thresholds (e.g., 0.0001) capture nuanced patterns and enhance model alignment with observed commuter behavior. By combining symbolic interpretability with neural generalization, the proposed method advances both the transparency and performance of travel demand modeling. The data and code can be accessed on GitHub.1

Network slicing has emerged as a transformative enabler for meeting the diverse requirements of 5G and beyond networks, including 6G. However, network slices’ dynamic and virtualized nature introduces significant security challenges, particularly against evolving cyber threats. We propose a Deep Reinforcement Learning (DRL)–based Moving Target Defense (MTD) strategy tailored for secure network slicing to address these challenges. Our approach utilizes a Q-Learning framework to manage MTD actions dynamically, optimizing security while maintaining service quality. Extensive simulations demonstrate the effectiveness of our framework in minimizing attack success rates and ensuring operational stability, significantly outperforming baseline methods such as random decision-making.

In this paper, we show a technique which can be employed in simulating GPS spoofing attacks through the use of Ardupilot, which is an open-source software allowing for the simulation of UAV (Unmanned Aerial Vehicles). As concerns about GPS spoofing are raised, the harm it poses to navigation reliant systems and applications become an increasingly pressing issue. In order to resolve this problem, it is a necessity to understand how a GPS spoof works in practice and learn how to combat it through testing. Our simulated framework will greatly assist in this endeavor. In our methodology, we utilize the Ardupilot simulation system and the MAVProxy system to artificially send GPS data to the UAV’s navigation system, essentially simulating a GPS spoof. We created a Python script to perform this GPS injection and make the UAV fly in a circular motion. The aim of the proposed system is to be able to operate in a virtual environment, thus reducing required hardware, and minimizing the risks that come with testing actual UAV systems. Throughout the progress of the project, a GPS spoofing script was created, incorporated into the ArduPilot simulation system, and tested on how effectively it is able to affect the movement of the UAV. This simulation will help in creating a framework to help detect a GPS spoof attack as well as allow us to understand the effects of a GPS spoof on a UAV. In addition to this, we evaluate the extent to which a remote attacker is able to exert control over a UAV through GPS spoofing.

This paper addresses the challenge of device localization within a Wi-Fi network. It introduces a novel localization technique, using a pre-constructed map with predefined positions around an Access Point (AP). The Wi-Fi client devices (STAs), are positioned on the map utilizing the capabilities of the Fine Time Measurement (FTM) protocol (IEEE 802.11mc), and tracked using the Pedestrian Dead Reckoning (PDR) method, which leverages the device’s Micro-Electro-Mechanical Systems (MEMS). It also harnesses the capabilities of Neighbor Awareness Networking (NAN) technology, to discover the distances between Wi-Fi Aware certified devices. This method requires no additional equipment or prior setup, since one AP is sufficient to determine the STA’s position. The findings suggest that this approach provides a scalable and efficient solution for IoT applications.

Current Retrieval-Augmented Generation systems use uniform processing, causing inefficiency as simple queries consume resources similar to complex multi-hop tasks. We present SymRAG, a framework that introduces adaptive query routing via real-time complexity and load assessment to select symbolic, neural, or hybrid pathways. SymRAG’s neuro-symbolic approach adjusts computational pathways based on both query characteristics and system load, enabling efficient resource allocation across diverse query types. By combining linguistic and structural query properties with system load metrics, SymRAG allocates resources proportional to reasoning requirements. Evaluated on 2,000 queries across HotpotQA (multi-hop reasoning) and DROP (discrete reasoning) using Llama-3.2-3B and Mistral-7B models, SymRAG achieves competitive accuracy (97.6–100.0% exact match) with efficient resource utilization (3.6–6.2% CPU utilization, 0.985–3.165s processing). Disabling adaptive routing increases processing time by 169–1151%, showing its significance for complex models. These results suggest adaptive computation strategies are more sustainable and scalable for hybrid AI systems that use dynamic routing and neuro-symbolic frameworks.

In recent years, quantum computing has made significant contributions to many emerging technologies. However, it also poses serious security challenges to these technologies, and one of them is Healthcare Internet of Things (HC-IoT) applications. The devices used in HC-IoT often have limited power, memory, and computational resources, making them especially vulnerable to various cyberattacks. Even a small security breach could cause serious problems, from general system failures to risks that directly affect patients’ diagnoses and treatment. To address this important issue, we review research from 2017 to 2025, examining both the strengths and weaknesses of the technology across various subdomains of the healthcare system. We begin by presenting a taxonomy of healthcare, along with a breakdown of different domains where this technology has been applied or holds potential for future use. This foundation helps establish the motivation and context for the study. Next, we discuss various security threats, considering both the pre-quantum and post-quantum computing eras. Then, we explore existing studies to see what progress has been made and what is still needed. Finally, we point out key security challenges that need more attention from the research community. Lastly, we provide a comparative analysis with existing review articles to address the question of why this article is needed in the presence of published reviews.

The complexity of next-generation industrial cyber-physical systems (NG-ICPSs) is increasing due to the integration of machine-embedded sensors, cyber-infrastructure, and physical processes, which calls for the new intelligent operation mechanisms to achieve system-level objectives. Although NG-ICPS has proliferated in many applications, such as advanced manufacturing, intelligent transportation, smart homes, etc., and achieved remarkable results. But these applications are susceptible to many problems and new security threats are some of them that goes beyond the scope of traditional communication and network security, due to the tight integration of cybers and physical systems. For redressal of this, several traditional authentication and data privacy schemes have been used in the recent past, but somehow, they did not satisfy the need for this emerging technology, due to their complex verification and validation processes. Recently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) enabled authentication and data preservation techniques had shown remarkable results to address the security problems of this technology at the system/client side and server side cost-effectively. Given that, in this article, we present a comprehensive survey of the current literature on NC-ICPS technology security threats and their countermeasures, with a focus on AI, ML, and DL-enabled techniques. We evaluate these techniques by identifying their advantages and disadvantages compared to traditional authentication and data preservation methods. In addition, we discussed the review articles published on this topic to acknowledge their contributions and limitations, because most of them cover a specific part of security concerns of this technology, and unable to present the true picture of all problems under one shallow. Building on this, we addressed the gaps in the literature by highlighting the open security challenges of NG-ICPS technology and suggesting potential future research directions, considering the capabilities of AI, ML, and DL-enabled algorithms. Finally, we compared this article sectionwise with rival review articles to claim its novelty followed by the question of reviewers, editors, students, and readers why this article is needed in the presence of these articles and what are its distinctive factor that makes this article different from them.

In 5G and Beyond-based Internet of Things (IoT) sensor networks, the end-to-end (E2E) route traverses via multiple heterogeneous network domains, necessitating interdomain interaction to guarantee and confirm Quality-of-Service (QoS) for low-power IoT devices applications. Moreover, in heterogeneous IoT sensor networks, the E2E path often encompasses domains with diverse QoS parameters or classes. The unique E2E requirements for delay, packet loss ratio (PLR), and other factors present further challenges. However, existing legacy network architectures and typical software-defined networking (SDN) models lack effective strategies for QoS provisioning tailored to the service requests of IoT low-power sensor devices. To address these issues, this study proposes a novel multiobjective SDN-based framework for IoT sensors, ensuring E2E QoS across multiple domains with heterogeneous traffic service classes (TSC). A two-layer SDN framework is presented to provision QoS for IoT sensors based on their specific service demands at the E2E network level. Central to the framework is the deployment of an optimal additive weighting module (OAWM), facilitating TSC ranking according to their weights and incorporating a priority mechanism for specific service parameters, such as delay, PLR, and jitter. Additionally, the global controller statistics enable the provisioning of E2E QoS by mapping the service requests from IoT sensors. Experimental evaluations are conducted to compare the proposed approach with existing schemes. The results validate the effectiveness of our proposed method, demonstrating improved E2E QoS provisioning and meeting the specific requirements of IoT sensors in precision agriculture with low-power IoT devices.

Internet of Vehicles (IoV) is growing rapidly with the potential to revolutionize transportation systems. Considering the promising future and potential contributions of IoV’s technology, it has attracted the attention of researchers, industry stakeholders, and potential intruders. However, the IoV’s network topological infrastructure faces several connectivity and communication challenges, along with security issues that are beyond the scope of current literature. Although each aspect and challenge has its own consequences, this work focuses on Physical Layer Security (PhyLaySec) threats, which are the most devastating because they undermine the trust of all stakeholders associated with this technology. In the literature, this topic is bearly focused, which demonstrates that the existing PhyLaySec countermeasures would not be able to counter future security challenges in IoV in terms of vehicle-to-vehicle (V2V) authentication, vehicle-to-infrastructure (V2I) authentication, vehicles-to-everything (V2X) authentication, etc., due to factors such as high vehicle mobility, dynamic network topologies, limited bandwidth, and ultra-fast communication. Therefore, this paper aims to provide a systematic review of state-of-the-art PhyLaySec techniques from 2017 to 2025, with a focus on their strengths and weaknesses. Through our review, we identify key open research questions that require further investigation to enhance the security of IoV’s technologies. Moreover, we highlight potential future research directions that aim to ensure the foolproof security of IoV technology with respect to underlined challenges. Finally, we acknowledge that this is the first paper to comprehensively address the topic of PhyLaySec of IoV technology, which makes it a valuable resource for researchers and professionals working in this field.

The ever-evolving Internet of Things (IoT) has ushered in a new era of intelligent manufacturing across multiple industries. However, the security and privacy of real-time data transmitted over the public channel of the Industrial IoT (IIoT) remain formidable challenges. Existing lightweight protocols often omit one or more critical security features, such as anonymity and untraceability, and are susceptible to threats like desynchronization attacks. Additionally, they struggle to achieve an optimal balance between robust security and performance efficiency. To bridge these gaps, we introduce a new lightweight key agreement security scheme that guarantees secure access to the IIoT-enabled flexible manufacturing system (FMS). The strength of our scheme lies in its utilization of the authenticated encryption with associative data (AEAD) primitive, AEGIS, along with hash functions and physical unclonable functions, which secure the IIoT ecosystem. Additionally, our scheme offers flexibility in the form of the addition of new machines, password updates, and revocation in cases of theft or loss. A comprehensive security analysis demonstrates the efficacy of the proposed scheme in thwarting various attacks. The formal analysis, based on the Real-or-Random (RoR) model, ensures session key indistinguishability, while the informal analysis highlights its resilience against known attacks. The comparative assessment demonstrates that the proposed scheme consistently outperforms the benchmark schemes across multiple dimensions, including security and functionality features, computational and communication overheads, and runtime efficiency. Specifically, the proposed scheme achieves peak performance enhancements of 77.55%, 44.73%, and 69.6% in computational overhead, runtime overhead, and communication overhead, respectively, underscoring its substantial performance advantages.

In the Internet of Vehicles (IoV), privacy preservation is a major challenge due to the mobility of vehicles and their resource-constrained nature. The limited resources of on-board units (OBUs) and embedded sensors lure the adversaries to launch various types of attacks. Thus, lightweight but reliable authentication schemes need to be designed to combat these attacks. Another major challenge is the scarcity of available bandwidth and excessive delay experienced by vehicles while they communicate with the servers located at the cloud. These servers execute various machine learning (ML) and deep learning (DL) algorithms to extract useful features from upstream traffic of IoV. To addresses these challenges, we propose an ML-based authentication scheme that trains and classifies the vehicles at the edge servers in a distributed manner, preserves the privacy of communicating entities and minimizes the bandwidth consumption and delay experienced by the vehicles. The ML-based approach extends the decision power of vehicles and edge servers to identify adversaries. Our scheme requires that each vehicle participates in an offline phase, where a trusted authority shares a list of MaskIDs and secret keys of legitimate vehicles and edge servers. A timestamp is embedded in the payload of each encrypted message to prune the proposed scheme against well-known adversarial attacks. The simulation results verify the exceptional performance of our scheme in terms of computational overhead, communication overhead, and storage overhead.

Unmanned Aerial Vehicles (UAVs) are increasingly integrated into vehicular systems for applications such as autonomous surveillance, traffic monitoring, and navigation. These applications rely on real-time object tracking, which has been significantly enhanced by deep learning (DL)-based models. However, DL-based trackers remain highly vulnerable to adversarial attacks, where imperceptible perturbations can severely degrade tracking accuracy and reliability. To address these challenges, we propose a pluggable defense solution designed to enhance the robustness of UAV tracking systems without modifying existing tracking architectures. Our approach leverages a dual-level optimization strategy to mitigate adversarial perturbations at both feature and decision levels, ensuring resilient tracking performance. Implemented as a pre-processing stage, our solution can be seamlessly integrated with various UAV tracking systems. We evaluate our approach against multiple adversarial attacks across three widely used UAV tracking benchmarks: UAVTrack112, UAV123, and UAVDT. Experimental results demonstrate that our pluggable solution effectively restores tracking accuracy and improves robustness under various adversarial attacks without sacrificing tracking performance in original (attack-free) scenarios. Real-world tests on a UAV platform validate the efficiency and practicality of our method. Comprehensive results indicate our solution can strengthen UAV tracking in real-world applications and ensure reliable performance in adversarial environments

Underwater federated learning (UFL) is an emerging technology to realize distributed intelligent collaboration in the Internet of Underwater Things (IoUT), but its application faces two challenges: the limited bandwidth of underwater communication leads to low model transmission efficiency, and the data is characterized by low quality and high heterogeneity due to environmental interference. In this paper, an underwater federated learning framework with dual-path collaborative optimization is proposed to solve the above problems systematically through the joint design of knowledge distillation and data quality enhancement. Specifically, to optimize the transmission efficiency, a knowledge distillation mechanism is designed, and the complex model is compressed into a simplified model suitable for low-bandwidth transmission by using the collaborative distillation of lightweight teacher-student models. To enhance data quality, a supervised data quality enhancement (S-DQE) method is proposed. The integration of traditional methods with deep learning-based approaches optimizes feature representation through the joint application of contrastive learning and adversarial training, thereby effectively addressing the issue of low-quality underwater data. Finally, numerical results are given to compare the final scheme with the initial federated learning scheme, lightweight model scheme, and lightweight-data quality enhancement scheme, clearly demonstrating its performance gains.

In distributed learning, the End-Edge-Cloud architecture is gaining widespread adoption. However, the cross-layer data interaction in EEC systems significantly increases the risk of privacy breaches. To address this challenge, this paper proposes a novel dynamic privacy-protection framework named MetaDP-HE. The framework is designed to enhance privacy protection while maintaining model performance. It integrates meta-model guided differential privacy (MetaDP) with CKKS homomorphic encryption that supports floating-point operations. A dynamic coordination mechanism is introduced to optimize the parameter configurations between MetaDP and HE. Specifically, clients use the meta-model to predict privacy budgets based on data sensitivity and adjust the noise in differential privacy accordingly. Edge servers then dynamically adjust the encryption parameters of homomorphic encryption based on the noise level, achieving adaptive regulation of encryption strength. The combination of layered encryption and dynamic parameter optimization enables the system to ensure privacy protection and efficient operations when handling data at different levels. Experimental results show that MetaDP-HE outperforms traditional single-privacy methods in both privacy protection and model performance, validating its effectiveness and applicability in practical scenarios.

Centralized federated learning is being widely researched and applied. However, centralized federated learning is prone to problems such as single point of failure and privacy disclosure because it relies too much on the central server. Focusing on decentralized federated learning, this paper innovatively constructs a decentralized federated learning framework based on dynamic selection collaboration and topology optimization. Firstly, we propose a dynamic client selection algorithm based on node training quality. Then, a global network topology for data communication is constructed by us based on the Watts-Strogatz(WS) model. Finally, we design a temporary topology algorithm to realize synchronization and model update in training. In the process of decentralized federated learning, the global network topology based on WS model cooperates with the current network topology constructed by temporary topology algorithm. The two network topologies work together to realize a dynamic client selection algorithm based on node training quality. A large number of experiments verify that DFedMQ can accelerate the model convergence and improve the training effect under the premise of privacy protection.

Metacomputing optimizes distributed computing resources to enhance federated learning systems by enabling efficient resource allocation, improved scheduling, and greater scalability, thereby addressing challenges in large-scale and dynamic environments. This paper proposes an innovative framework integrating Directed Acyclic Graph (DAG) technology with federated learning within a metacomputing environment. The key contributions include a three-layer decentralized federated learning model integrating DAG and metacomputing to enhance resilience and scalability, two advanced tip selection models LazyEval Tip Selector and Precision Tip Selector to optimize node selection and improve data flow, and a Benchmark Improvement Protocol (BIP) for efficient node publishing and role adaptation.The BIP ensures that only high-performing models are published by comparing new models against established benchmarks, which enhances node collaboration and optimizes resource allocation. LazyEval Tip Selector minimizes redundant computations by leveraging a global cache and employing a lazy evaluation strategy, thereby improving computational efficiency. On the other hand, Precision Tip Selector uses a precise scoring mechanism to ensure accurate tip selection, thereby enhancing the robustness and reliability of the entire system. Collectively, these innovations enhance model training efficiency, support real-time updates, and improve the scalability of federated learning systems, making them well-suited for managing complex, dynamic environments.

Mobile crowdsensing is an emerging sensing paradigm that utilizes widely distributed mobile smart devices to rapidly collect data to support multiple applications in the Internet of Things. In mobile crowdsensing, collecting high-quality data at a low cost while protecting participants’ privacy is essential for developing high-quality applications and providing superior services. However, the paradigm faces many challenges, including protecting participants’ private information, improving data quality, and efficiently assigning tasks. To address these challenges, a Reliable Task Allocation Framework (RTAF) is designed to protect participant privacy, enhance data quality, and reduce costs. Specifically, the RTAF consists of three key algorithms: (1) A Bilateral Zero-bias Location Privacy-preserving (BZLP) algorithm is designed to protect the location privacy of tasks, ensuring the confidentiality and security of participants’ privacy. (2) An Unmanned Aerial Vehicle (UAV) Truth-driven Workers’ Trust degree Recognition (UAV-TWTR) algorithm is proposed to evaluate the data truth of tasks and identify trusted workers, reducing the risk of malicious workers attacking the system. (3) A Bi-objective Optimization Allocation of Tasks (BOAT) algorithm is developed to construct an optimal allocation scheme to ensure that the tasks can be assigned to appropriate workers, improving data collection accuracy and reducing the cost. Experimental results demonstrate that the RTAF surpasses the state-of-the-art method, enhancing the F1-score by 24.5%, increasing accuracy by 24.3%, and reducing the movement costs of workers by 19.1%.

The prompt expansion of Internet of Things (IoT) devices necessitates advanced security frameworks to protect data integrity, confidentiality, and availability in resource-constrained environments. Traditional security solutions are often resource-intensive for IoT devices with limited computational power and energy resources. This study addresses these inadequacies by proposing a novel approach formulated to such constraints. This study propose the trust-enhanced lightweight security framework (TELSF), integrating two novel components: 1) the adaptive lightweight encryption algorithm (ALEA) and 2) the trust-aware data protection model (TADPM). ALEA employs dynamic key generation through a lightweight hash function, ensuring unique and regularly updated encryption keys based on device context and behavior. TADPM enhances this framework by continuously assessing device trustworthiness through direct interactions, aggregated feedback from neighboring devices, and contextual parameters, such as location and device capabilities. Performance evaluations demonstrate that TELSF significantly enhances security and operational efficiency, reducing computational overhead by 18%, improving energy efficiency by 20%, and increasing data transmission security by 10% compared to existing solutions.

Ultrasound imaging continues to play a critical role in prenatal diagnostics, but accurate interpretation remains hindered by limited labeled data, inconsistent pseudo label quality, and real-time processing constraints in Internet of Medical Things (IoMT) environments. Existing semi-supervised learning (SSL) frameworks fail to maintain reliable segmentation under these dynamic and resource-constrained conditions. This study proposes ADLIFT, a real-time SSL-based ultrasound processing framework designed to optimize diagnostic accuracy and computational efficiency. The approach integrates an Adaptive Dual-Layer Perception (ADLP) mechanism combining macro-level anatomical recognition with micro-level feature refinement, and a Dynamic Label Generation (DLG) module that iteratively improves pseudolabel reliability using confidence-driven feedback. Efficient Sparse Feature Extraction (ESFE) minimizes computational overhead by isolating high-activation regions, while the Temporal Contextualization Framework (TCF) ensures inter-frame consistency. Blockchain-enhanced edge computing supports secure and scalable IoMT deployment. Evaluations in HC18, FetalPlane18 and Kvasir-Segment datasets demonstrate precision of 93. 7%, decision stability of 92. 8%, interpretability index of 91. 5%, uncertainty handling efficiency of 89. 7%, trust reliability score of 95. 3%, and processing latency of 28.1 ms per frame.

With the advancement of information technology, the Social Internet of Things (SIoT) has fostered the integration of physical devices and social networks, deepening the study of complex interaction patterns. Text attribute graphs (TAGs) capture both topological structures and semantic attributes, enhancing the analysis of complex interactions within the SIoT. However, existing graph learning methods are typically designed for complete attributed graphs, and the common issue of missing attributes in attribute missing graphs (AMGs) increases the difficulty of analysis tasks. To address this, we propose the topology-driven attribute recovery (TDAR) framework, which leverages topological data for AMG learning. TDAR introduces an improved prefilling method for initial attribute recovery using native graph topology. Additionally, it dynamically adjusts propagation weights and incorporates homogeneity strategies within the embedding space to suit AMGs’ unique topological structures, effectively reducing noise during information propagation. Extensive experiments on public datasets demonstrate that TDAR significantly outperforms state-of-the-art methods in attribute reconstruction and downstream tasks, offering a robust solution to the challenges posed by AMGs. The code is available athttps://github.com/limengran98/TDAR.

Assigning tasks to reliable workers to obtain reliable data is a critical issue in Mobile CrowdSensing (MCS). The challenge is compounded by the problem of Information Elicitation Without Verification (IEWV), which renders traditional data quality evaluation methods ineffective. While some studies attempt to address this, they often struggle to assess workers’ dynamic trustworthiness, resulting in unreliable data. To overcome these challenges, we propose the Trust and Time-sharing Task Allocation based Truth Discovery (TTTA-TD) scheme, designed to ensure reliable data collection in MCS. This scheme includes three components: (a) Classification-based Trust Evaluation (CTE) that classifies workers based on behavior and applies tailored penalties—lenient for honest workers and stricter for malicious ones, (b) Trust-based Truth Data Discovery (TTDD), which improves truth data accuracy by integrating trust scores, and (c) Trust and Time-sharing Task Allocation (TTTA) which allocates tasks to ensure data reliability and minimize time-sharing disparities. Experimental results show that the TTTA algorithm reduces average time-sharing by 93.95%. The TTDD algorithm improves truth estimates across all dataset qualities, and the TTTA-TD scheme enhances data reliability by 0.35%, 2.06%, and 7.41% in high, medium, and low-quality datasets respectively.

Deep learning-based object detection has become an important application in industrial IoT. However, studies have shown that adversarial attacks may cause object detection to output incorrect detection results. Such vulnerabilities can threaten the robustness of object detection systems and lead to security problems. To address the issue of low attack effectiveness on target detection from different perspectives using the existing adversarial attack methods, this paper proposes an adversarial attack method with multi-view adaptive weight-balancing. First, a multi-view channel is constructed for training, and the target features under different viewpoints are comprehensively considered to enhance the robustness of the attack method. Then, the model is optimized by combining the model shake drop and patch cut-out algorithms during the training process, so that the attack method no longer relies on a single model, thus enhancing its generalization ability. Finally, by dynamically adjusting the weights of each viewpoint, a weight-balancing strategy is constructed, which adaptively adjusts the preference of different perspectives during the training process to enhance the attack effect of the attack method in each viewpoint. To verify the performance of the method, experiments are conducted on multiple benchmarks, specifically the PKU-Reid dataset. Compared with the mainstream methods, the proposed method improves the attack success rate by 3.78% and 19.26% under white-box and black-box conditions, respectively, while reducing the mean average precision of the object detection model by 2.18% and 11.12%, respectively. The experimental results demonstrate that the proposed method effectively enhances attack performance on targets from different viewpoints and exhibits better viewpoint robustness.

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), Information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches.

The convergence of Web 3.0 and Metaverse technologies with Connected Autonomous Vehicles (CAVs) is catalyzing a new era of intelligent vehicular systems, characterized by decentralization, immersive interaction, and enhanced autonomy. This paradigm is especially valuable in scenarios demanding secure peer-to-peer coordination, trustless automations, and seamless integration of physical and virtual environments under real-time constraints. Nonetheless, realizing such intelligent applications introduces critical challenges, including the development of robust decentralized governance and smart contracts, ensuring ultra-low-latency and high-throughput communications in edge computing contexts, achieving seamless digital–physical synchronization via high-fidelity digital twins or Metaverse representations, and guaranteeing scalability and privacy across distributed vehicle networks. This special issue brings together a collection of pioneering research that tackles these multifaceted challenges, showcasing innovations that advance the state of the art toward more secure, responsive, immersive, and decentralized CAV systems empowered by Web 3.0 and Metaverse technologies.

Crack segmentation receives extensive attention in structure health monitoring for many industrial scenarios, e.g., bridges, highways, and nuclear power plants. The current deep learning-based crack segmentation models enjoy the ability to extract discriminative crack features by training with an extensive labeled crack dataset. However, collecting extensive crack samples with accurate annotations from experts for a new scenario is labor-intensive, thereby limiting the effectiveness of these deep models in practical applications. To address this problem, the existing Fourier-based augmentation adopts a vanilla amplitude fusion process, i.e., the portion of amplitude components is fixed or randomly selected, failing to guarantee augmented samples’ semantics consistency, and diversity concerning the original sample. To fill this, this article proposes a self-adaptive Fourier augmentation framework that efficiently synthesizes diverse crack samples for training crack segmentation models. Our proposed framework advances Fourier transformation in an adversarial learning manner, alternating between self-adaptive Fourier-based data augmentation and teacher–student learning. The former aims to guarantee the diversity and semantics consistency of Fourier-based augmented samples, while the latter progressively updates the student network by observing these augmented samples for extracting discriminative features via a knowledge distillation mechanism. It is worth noting that the proposed method is only applied in the training stage without extra computation and memory during inference. Extensive experiments demonstrate the superiority of our method over the existing methods.

Recent advancements in non-invasive blood glucose detection have seen progress in both photoplethysmogram and multiple near-infrared methods. While the former shows better predictability of baseline glucose levels, it lacks sensitivity to daily fluctuations. Near-infrared methods respond well to short-term changes but face challenges due to individual and environmental factors. To address this, we developed a novel fingertip blood glucose detection system combining both methods. Using multiple light sensors and a lightweight deep learning model, our system achieved promising results in oral glucose tolerance tests. A total of 10 participants were involved in the study, each providing approximately 700 data segments of about 10 seconds each. With a root mean squared error of 0.242 mmol/L and 100% accuracy in zone A of the Parkes error grid, our approach demonstrates the potential of multiple near-infrared sensors for non-invasive glucose detection.

Vehicle platooning enables vehicles to drive cooperatively on highways and motorways. This is to weigh numerous benefits such as lesser fuel consumption, safe drive and better road utilization. The main research focus of vehicle platoons is secure and dynamic platoon formation and ensuring privacy of vehicular data. Platoons are not safe from cyber-attacks and key is to safeguard the platoons from different known/unknown cyber threats. This paper introduces a dynamic and secure platoon formation technique targeting three different vehicle conditions on road. While the private credentials of CAVs is protected using zk-SNARK encryption protocol through permissioned Blockchain. The proposed system has been evaluated for its performance against DDoS and impersonation attack. Platoon formation time has been compared with other benchmarks and shows better results. While the performance against DDoS and impersonation attacks in comparison to the benchmarks is also improved. Since the limitation of zk-SNARK is it takes time to generate a proof and this has been also the focus of this study. The protocol has been fine tuned to adjust its parameters so that it could generate proof in less possible time.

In remote or disaster areas, where terrestrial networks are difficult to cover and Terrestrial Edge Computing (TEC) infrastructures are unavailable, solving the computation computational offloading for Internet of Vehicles (IoV) scenarios is challenging. Current terrestrial networks have high data rates, great connectivity, and low delay, but global coverage is limited. Space–Air–Ground Integrated Networks (SAGIN) can improve the coverage limitations of terrestrial networks and enhance disaster resistance. However, the rising complexity and heterogeneity of networks make it difficult to find a robust and intelligent computational offload strategy. Therefore, joint scheduling of space, air, and ground resources is needed to meet the growing demand for services. In light of this, we propose an integrated network framework for Space-Air Auxiliary Vehicle Computation (SA-AVC) and build a system model to support various IoV services in remote areas. Our model aims to maximize delay and fair utility and increase the utilization of satellites and Autonomous aerial vehicles (AAVs). To this end, we propose a Deep Reinforcement Learning algorithm to achieve real-time computational computational offloading decisions. We utilize the Rank-based Prioritization method in Prioritized Experience Replay (PER) to optimize our algorithm. We designed simulation experiments for validation and the results show that our proposed algorithm reduces the average system delay by 17.84%, 58.09%, and 58.32%, and the average variance of the task completion delay will be reduced by 29.41%, 48.74%, and 49.58% compared to the Deep Q Network (DQN), Q-learning and RandomChoose algorithms.

Mobile Crowdsensing (MCS) has emerged as a promising sensing paradigm for accomplishing large-scale tasks by leveraging ubiquitously distributed mobile workers. Due to the variability in sensory data provided by different workers, identifying truth values from them has garnered wide attention. However, existing truth discovery schemes either offer limited privacy protection or incur high participation costs and lower data aggregation quality due to malicious workers. In this paper, we propose an Efficient and Trusted Bilateral Privacy-preserving Truth Discovery scheme (ETBP-TD) to obtain high-quality truth values while preventing privacy leakage from both workers and the data requester. Specifically, a matrix encryption-based protocol is introduced to the whole truth discovery process, which keeps locations and data related to tasks and workers secret from other entries. Additionally, trust-based worker recruitment and trust update mechanisms are first integrated within a privacy-preserving truth discovery scheme to enhance truth value accuracy and reduce unnecessary participation costs. Our theoretical analyses on the security and regret of ETBP-TD, along with extensive simulations on real-world datasets, demonstrate that ETBP-TD effectively preserves workers’ and tasks’ privacy while reducing the estimated error by up to 84.40% and participation cost by 54.72%.

Enhancing the communication rate and quality has become the primary goal for the development of next-generation mobile communication networks, and traditional techniques such as MIMO and increasing the transmit power of the base station (BS) have not achieved a leapfrog effect. The emergence of Intelligent Reflective Surfaces (IRS) provides more reliable technical support for providing high-energy and high-rate communications. However, IRS-aided joint optimization with communication rate and quality of service constraints is a non-convex non-smooth optimization problem, and the optimal global solution cannot be obtained due to its computational complexity. In this paper, we propose an intelligent Advanced Non-convex Non-smooth Scheme (ANNS) in IRS-aided Next Generation Mobile Communication Networks for making the transmission rate of mission communication and communication quality effective. To ensure that the inequality constraints in the joint optimization problem are not violated and the equation constraints are satisfied, a hybrid deep reinforcement learning and data-experience-driven constraint security layer network is proposed, which maps the constraint violations into the safe feasible domain by mapping the constraint variables into the constraint variables mapping method, and the convergence of the algorithm is theoretically demonstrated. Experimental results show that the proposed ANNS performs superior optimization compared to SAC, DDPG, and A2C for solving non-convex non-smooth problems. The proposed ANNS has the potential to be generalized to other mobile computing applications with non-convex non-smooth characteristics.

Recently, For-Hire Vehicles (FHVs) have emerged as major players in Vehicular CrowdSensing (VCS). However, the heterogeneity of tasks issued by Data Requesters (DRs) and the heterogeneity of sensors equipped on FHVs under different Vehicle Platforms (VPs) bring difficulties to task allocation and execution. It can be concluded that it is important to reasonably analyze the relationship among DRs, VPs, and FHVs, as well as to motivate VPs and FHVs to complete sensing tasks. Therefore, taking advantage of the real-time simulation and intelligent decision-making of Digital Twins (DT), this paper proposes a DT-drivenDouble-layerReverseAuctionMethod (DRAM). In the first layer, the reverse auction is established between each DR and VPs, and in the second layer, the reverse auction is established between each VP and FHVs. Meanwhile, we also introduce a sensing fairness index to ensure the sensing balance of different sub-regions and consider it in the DRAM process. Here, the idea of backward induction is used to solve the above problems, with the goal of minimizing the overhead of winning VP and the average overhead of all DRs. Finally, the effectiveness of the DRAM proposed in this paper is verified based on the real data set. Compared with the baseline method, DRAM can reduce the average overhead of DR by about 4%-25%. Meanwhile, in terms of sensing fairness, it can be improved by up to 55%.

The proliferation of mobile smart devices with ever-improving sensing capacities means that Mobile Crowd Sensing (MCS) can economically provide a large-scale and flexible solution. However, existing MCSs face threats to privacy and fairness when recruiting workers due to information sensitivity, uncertainty about worker behavior, and budget constraints. To address the above issues, we propose an Anonymity, Trust, and Fairness in Privacy Protection (ATFPP) service construction framework to cost-effectively improve the quality of data at MCS. The main innovations are as follows: Firstly, on anonymity, in order to protect the privacy of workers, we propose a Privacy-Preserving (PP) framework based on an anonymous three-party platform, which realizes a full-process privacy-preserving scheme for workers. Second, on trust, we design more efficient Truth Discovery (TD) algorithm and adopt multifactor trust assessment method to identify more trustworthy workers. In addition, in terms of fairness, the fair distribution of compensation is realized through reasonable budget and approximate Shapley method. Finally, the proposed ATFPP scheme is theoretically proven to be correct and effective. Simulations based on real-world datasets illustrate that our ATFPP service construction scheme outperforms the state-of-the-art method in terms of both privacy protection and data quality.

Mobile Crowd Sensing is a prevalent and efficient paradigm for multi-source data collection, where Multi-source Data Fusion (MDF) plays a crucial role in constructing quality data collection services. Current MDF methods often require the majority of participating sensing sources to be credible, or assume that the workers’ credibility is either prior known or easily calculable. However, due to the presence of uncredible environments and the problem of Information Elicitation Without Verification (IEWV), these methods are impractical. It may lead to a vicious cycle where the recruitment of uncredible workers affects the quality of the estimated truth, which can further lead to misjudgments of worker credibility, thereby exacerbating the quality of subsequent recruitment. In this article, a Reliable Multi-source Data Fusion scheme with Cross Validation (RMDF-CV) is proposed to obtain reliable truth for service construction. Specifically, we first introduce the Combinatorial Multi-Armed Bandit (CMAB) model to recruit high-credibility workers by balancing exploration and exploitation. Then, we establish three-stage truth data through three different data sources: Unmanned Aerial Vehicles, credible workers, and Deep Matrix Factorization. Theoretical analyses and extensive simulations confirm the excellent performance of our RMDF-CV scheme.

In the service of Mobile Crowd Sensing (MCS), High-quality Data Collection (HDC), Bilateral Location Privacy Preservation (BLPP), and sensing cost are three pivotal issues. It is widely believed that HDC necessitates the recruitment of workers with high Quality of Service (QoS), which is related to the sensing data capabilities of the recruited workers and the worker-task distances. However, submitting high-quality data demands more resources from the workers, incurring higher costs. Meanwhile, BLPP techniques, aiming to conceal the locations of the workers and tasks, may impede the evaluation of the workers' QoS. Therefore, there is still a lack of a low-cost and BLPP high QoS data collection research. Motivated by this, we propose a Quality-Aware, Low-Cost, and Privacy-Preserving Data Collection Service (QLP-DCS) for MCS. First, we propose a matrix perturbation-based approach to achieve BLPP while preserving the partial order relationship of distances. Subsequently, we employ the Upper Confidence Bound indexes-based reverse auction recruiting workers to balance exploration and exploitation with the low sensing cost. Then, we propose a multi-level truth discovery approach and establish an effective trust verification mechanism. Theoretical analysis and extensive experiments validate the superior performance of our QLP-DCS.

Growth of computing capacity at edge drives edge computing as an efficient computing model, and cloud computing is considered as an indispensable computing model as various cloud services emerge. However, edge computing performs gracelessly in persistent tasks and the tasks requiring dispersed data, whereas cloud computing is often accompanied with high latency. To deal with more complex network scenarios, we fuse the two computing models and propose cloud-edge-seamless system (CESS) which enables all infrastructures to orchestrate data as services in a 5G/6G network, realizing infrastructure as a service (IaaS). In CESS, raw data is processed on intermediate processing devices (IPDs) before reaches the cloud. Thereafter, we proposed OBRI scheme to minimize processing and routing delay in which IPD can orchestrate data as a service if it is busy and handle data roughly if it is idle, thus reducing data volumes and total delay according to IPD congestion. Furthermore, there is still a large number of flows queuing on downstream IPDs, then NOB scheme is proposed to reduce delay further, in which IPD sends notice to upstream IPDs when congestion occurs to restrict them to orchestrate data only. Thus, the flow velocity slows down and more flows are orchestrated as services upstream, resulting greater delay reduction. Extensive experimental results show that the proposed CESS is immensely efficient. OBRI and NOB can effectively reduce delay, balance load, and improve quality of experience (QoE). In addition, NOB provides great flexibility and is extremely suitable to process delay-sensitive data.

In recent years, the rapid pace of urbanization has posed profound challenges globally, exacerbating environmental concerns and escalating traffic congestion in metropolitan areas. To mitigate these issues, Advanced Air Mobility (AAM) has emerged as a promising transportation alternative. However, the effective implementation of AAM requires robust demand modeling. This study delves into the demand dynamics of AAM by analyzing employment based trip data across Tennessee’s census tracts, employing statistical techniques and machine learning models to enhance accuracy in demand forecasting. Drawing on datasets from the Bureau of Transportation Statistics (BTS), the Internal Revenue Service (IRS), the Federal Aviation Administration (FAA), and additional sources, we perform cost, time, and risk assessments to compute the Generalized Cost of Trip (GCT). Our findings indicate that trips are more likely to be viable for AAM if air transportation accounts for over 70% of the GCT and the journey spans more than 250 miles. The study not only refines the understanding of AAM demand but also guides strategic planning and policy formulation for sustainable urban mobility solutions. The data and code can be accessed on GitHub.1

While deep-learning-enabled recommender systems demonstrate strong performance benchmarks, many struggle to adapt effectively in real-world environments due to limited use of user-item relationship data and insufficient transparency in recommendation generation. Traditional collaborative filtering approaches fail to integrate multifaceted item attributes, and although Factorization Machines account for item-specific details, they overlook broader relational patterns. Collaborative knowledge graph-based models have progressed by embedding user-item interactions with item-attribute relationships, offering a holistic perspective on interconnected entities. However, these models frequently aggregate attribute and interaction data in an implicit manner, leaving valuable relational nuances underutilized.This study introduces the Knowledge Graph Attention Network with Information Fusion (KGIF), a specialized framework designed to merge entity and relation embeddings explicitly through a tailored self-attention mechanism. The KGIF framework integrates reparameterization via dynamic projection vectors, enabling embeddings to adaptively represent intricate relationships within knowledge graphs. This explicit fusion enhances the interplay between user-item interactions and item-attribute relationships, providing a nuanced balance between user-centric and item-centric representations. An attentive propagation mechanism further optimizes knowledge graph embeddings, capturing multi-layered interaction patterns. The contributions of this work include an innovative method for explicit information fusion, improved robustness for sparse knowledge graphs, and the ability to generate explainable recommendations through interpretable path visualization. The implementation and datasets for this study are publicly available1.

Accurately predicting flight demand is essential for optimizing air travel operations and resource allocation. In our research, we explore the relationship between temporal patterns and flight demand, leveraging hourly data rather than traditional meteorological factors. Through analysis, we discovered significant correlations between hour of the day and flight demand, prompting the creation of features such as peak hours and time segments (morning, afternoon, evening). By utilizing these temporal features, we develop predictive models employing various machine learning algorithms, including LSTM, linear regression, and gradient boosting models. We aim to identify the most effective approach for accurately forecasting flight demand, with implications extending to the optimization of Advanced Air Mobility (AAM) solutions, where understanding temporal patterns is crucial for efficient resource allocation and urban air transportation network design.

Integrating Unmanned Aerial Vehicles (UAVs) into the emerging sixth-generation and beyond (6G+) cellular networks as aerial base stations represents a significant technological advancement. This integration offers numerous benefits, including widespread accessibility, enhanced navigation, and simplified monitoring and management. A key element of this integration involves the instantaneous distribution of vital information throughout the transportation infrastructure. Characterized by their agility, mobility, and flexibility, UAVs play a crucial role in relieving data traffic loads, thereby offering additional access points. This function is essential for making prompt, precise, and well-informed decisions in Intelligent Transportation Systems (ITS), utilizing data-centric insights. Deploying versatile Road Side Units (RSUs) for secure data collection and dissemination requires a robust framework for safe data transfer. Ensuring data governance in the Internet of Vehicles (IoV) network relies heavily on specific interactions between trusted parties. In response, we introduce an advanced encryption approach to promote secure data exchange in ITS, thus supporting the confidential transfer of information in IoV communications. This innovative encryption method can also perform encryption and decryption of ciphertexts, encompassing confidential data and facilitating secure communication.

This study presents an innovative approach to enhancing the efficiency of Unmanned Aerial Vehicles (UAV) in IoT networks. Employing UAVs as flying relays focuses on their role in data collection and support for terrestrial cellular networks. The central innovation lies in the application of Federated Learning (FL), which processes data while ensuring user privacy and reducing communication overhead. Addressing the challenge of UAVs’ limited battery life, which restricts sustained FL operations, we introduce the Enhanced UAV Network optimization Algorithm with Adaptive Spatial Play (ENUO-ASP). ENUOASP incorporates a modified Particle Swarm optimization (PSO) technique to determine optimal UAV placements, enhancing data collection by focusing on the Signal-to-Interference Ratio (SINR). Additionally, the paper utilizes the Deep Deterministic Policy Gradient (DDPG) method for dynamic resource allocation, optimizing energy consumption and reducing link latency between the UAV network and users. The findings indicate that the ENUO algorithm outperforms existing methods by achieving higher data rates and balanced SINR. Furthermore, the ASP resource allocation strategy improves FL execution, significantly lowering latency and energy use. This research contributes to the UAV-enabled communication field, offering a more efficient and performance-driven solution for advanced IoT applications.

The model’s uncertainty estimation is what adversarial attacks target and exploit. However, the attacker may not always have a clear understanding of how the model estimates uncertainty. Different types of uncertainty can impact these attacks differently because not all sources of uncertainty are equally affected by the attack strategies. We investigate the impact of various noise distributions as well as adversarial attacks on distinct networks. Our objective is to establish a threshold for quantifying network robustness. To achieve this, we analyze three network models, progressively increasing the depth of layers. Our analysis incorporates Shannon entropy and Kullback-Leibler divergence to assess model uncertainty. This directs our attention toward identifying a criterion for measuring uncertainty. Notably, our findings shed light on the manipulation of neural network uncertainty through adversarial attacks, highlighting variations across diverse datasets and models.

The aim of this study is to investigate into $\mu \mathrm{AFL}$, a non-intrusive, feedback-driven fuzzing framework, evaluated on Cortex M4 embedded systems and Unix platforms, focusing on the STM32F407VE Cortex M4 microcontroller. By leveraging the SEGGER J-Trace Pro for trace collection, it demonstrates $\mu$AFL’s utility beyond its traditional scope, showcasing its efficacy in both embedded and general-purpose computing environments. Our analysis, enriched by juxtaposing $\mu$AFL’s capabilities with traditional AFL, emphasizes the adaptability and effectiveness of fuzzing methodologies in firmware security enhancement. Furthermore, the study provides a deep understanding of fuzzing execution on different hardware, presenting an execution strategy for the STM32F407VE that highlights the framework’s potential in identifying vulnerabilities, evidenced by tests on specific firmware programs such as an LED blinking program integrated with semihosting breakpoints and ETM tracing. The use of uninitialized memory sections and strategically placed break-points offers significant insights into the firmware’s execution flow. The results of our comparative analysis clearly show that $\mu \mathrm{AFL}$ excels at uncovering vulnerabilities, reinforcing the need for evolving fuzzing methodologies to build stronger security systems for embedded devices. This contribution underscores the importance of refining fuzzing techniques to meet the intricate security demands of contemporary computing environments.

In this paper, we focus on addressing the challenges of detecting malicious attacks in networks by designing an advanced Explainable Intrusion Detection System (xIDS). The existing machine learning and deep learning approaches have invisible limitations, such as potential biases in predictions, a lack of interpretability, and the risk of overfitting to training data. These issues can create doubt about their usefulness, and transparency, and decrease the trust of involved stakeholders. To overcome these challenges, we propose an ensemble learning technique called the "EnsembleGuard". This approach uses the predicted outputs of multiple models, including tree-based (LightGBM, GBM, Bagging, XGBoost, CatBoost) and deep learning models such as neural network (LSTM (long short-term memory networks) and GRU (gated recurrent unit), to maintain a balance and achieve trustworthy results. Our work is unique because it combines both tree-based and deep learning models to design an interpretable and explainable meta-model through model distillation. By considering the predictions of all individual models, our neta-model effectively addresses key challenges, and ensures both explainable and reliable results. We evaluate our model using well-known datasets, including UNSW-NB15, NSL-KDD, and CIC-IDS-2017, to assess its reliability against various types of attacks. During analysis, we found that our model outperforms both tree-based models and other comparative approaches when it comes to different kinds of attack scenarios.

Quality control of sensing data poses a fundamental challenge in Spatial Crowdsourcing (SC) applications, and various incentive mechanisms have been proposed to effectively motivate workers. However, real-world SC systems involve multiple stakeholders, namely workers, platforms, and requesters, who exhibit bounded rationality. Moreover, prior research has often neglected the practical aspect of platforms needing to verify sensing data against ground truth information, which incurs a cost. Therefore, platforms face the constraint of verification cost and must judiciously select the verification rate. In this paper, a S ustainable Q uality C ontrol S ystem (SQCS) is proposed, which employs a three-party evolutionary game to model the dynamic interaction among workers, platforms, and requesters in practical SC scenarios. Based on this dynamic model: (1) In theory, we derive the minimum verification rate required for effective worker monitoring and the maximum verification cost that platforms can sustainably bear via the analysis of evolutionary stable strategies. These findings serve as guidelines for achieving sustainable quality control within the SQCS framework; (2) In practice, we apply the SQCS framework to the SC scenario of urban air pollution monitoring. The optimal quality control strategy that maximizes overall social benefits is analyzed via numerical methods . Extensive experimental validation is conducted to demonstrate the effectiveness of the proposed theory and practical approach. These findings provide valuable insights for the sustainable development of SC systems.

The dynamic evolution of wireless communication, driven by the Internet of Everything (IoE) and the envisioned 6G networks, presents both challenges and opportunities. IoE expands beyond IoT, encompassing diverse devices, human interactions, and environmental elements. To unlock IoE’s vast potential and harness the capabilities of 6G, we propose a framework named ISRiD, which assimilates three key components: Integrated Sensing and Communication (ISAC), Reconfigurable Intelligent Surfaces (RIS), and Deep Deterministic Policy Gradient (DDPG). This creates an adaptive, data-driven communication framework for IoE and 6G. ISAC enhances situational awareness, RIS optimizes signal paths, and DDPG adds intelligence. This empowers devices to collect environmental data for optimization and intelligent decisions. By leveraging real-time sensor data, ISRiD optimizes communication protocols, significantly improving efficiency and reliability in wireless networks. Despite challenges, this approach equips IoE, including the advancements brought by 6G, to meet evolving demands, as validated by empirical experiments and simulations.

In recent years, the WiFi channel state information (CSI) has been increasingly used for human activity recognition (HAR) during activities of daily living, because of nonintrusiveness and privacy preserving properties. However, most previous works require complex processing of CSI signals, and the large number of classification network parameters significantly increases the recognition time and deployment costs. Accordingly, a WiFi signal-based lightweight deep learning (WiLDAR) network is developed in this study to ensure systematic operation on edge computing devices. We combine the random convolution kernel with deep separable convolution and residual structure, so that WiLDAR can easily extract CSI signal features without filtering and denoising. The parameter number and training time of WiLDAR are, thus, much less than those of previous neural networks. In addition, a tiny HAR system using only Raspberry Pi and router is implemented. Experiments verify that WiLDAR can achieve real-time HAR on Internet of Things devices, which makes HAR deployment more convenient. We test WiLDAR on three different fine-grained action data sets to achieve 99%, 93.5%, and 97.5% recognition accuracy, respectively. The demonstrated learning capability of WiLDAR makes it an excellent option for the remote HAR.

Artificial Intelligence of Medical Things (AIoMT) is a hybrid of the Internet of Medical Things (IoMT) and artificial intelligence to materialize the acquisition of real-time data via the smart wearable devices. Due to a diverse geographical environment of IoMT, secure, and reliable communication among these devices is a challenging task that needs to be resolved on priority basis. For this purpose, numerous device-focused authentication approaches have been proposed in the literature, however, the problem still persists. This article introduces an advanced, secured, and efficient solution for the IoMT by leveraging a lightweight mutual authentication scheme as well as facilitating AI-enabled Big Data analytics and predictive modeling. The proposed approach is specifically designed to establish secured communication between wearable sensing devices and servers within IoMT by exploiting the desirable features of cloud–edge paradigm. In this approach, every device needs to verify whether the requesting wearable device is legitimate or not and this process needs to be carried out prior to the actual communication. Our proposed approach employs a hybrid of Advanced Encryption Standard, i.e., AES-128 bit and medium access control (MAC) for the establishment of secured communication sessions. In addition, the proposed approach utilizes real-time data collection from wearable devices, enabling predictive modeling for the early detection of health anomalies, thereby, enhancing the patient outcomes of a specific disease. This continuously adaptive approach excels in real-time decision making, promptly alerting healthcare professionals of potential risks. Simulation results have verified that the proposed approach serves an ideal solution for the resource-constrained devices by achieving the expected level of authenticity through minimum possible communication and processing overhead. Additionally, this scheme is prune against well-known security attacks in the AIoMT infrastructures.

Blockchain technology has found applications across diverse domains owing to its ability to establish trust in a decentralized manner. Nevertheless, the integration of blockchain into critical infrastructure domains encounters significant challenges posed by the computational demands and storage requirements associated with the proof-of-work puzzle during the mining process. This scenario becomes particularly complex in the context of applications within the Industrial Internet of Things (IIoT), where stringent timeliness constraints are inherent, notably in functions such as intrusion detection and control. This paper presents a novel solution that takes into account the time-sensitive nature of application constraints within the IIoT. Specifically, we focus on online functions involving intrusion detection and control. By doing so, we address the imperative need for timely and secure data delivery, crucial in maintaining the integrity of hard-to-tamper ledger blocks. These blocks encapsulate measurements that are seamlessly utilized by various system functions and components. The proposed approach optimizes the utilization of heterogeneous resources governing blockchain computations. This optimization ensures that the desired properties for logging within the blockchain are met, enabling the prompt delivery of measurements. The novel collaborative mining technique entails the sharing of nonce ranges among miners, which effectively reduces the overall mining time and enhances the efficiency of the process.

Drone participation in truck delivery is a potential booster for the last-mile logistics system, which has been an emerging hot research field. Among that, how to arrange a fleet of drones from the truck and optimize the vehicle routing problem with drones (VRPDs) is a key issue. However, most existing studies fail to derive the feasible solutions due to unordered customer distributions and multivariant drone feature constraints. In this article, we propose a novel self-driven reinforcement learning structure, named constraint-based hybrid pointer network (CH-Ptr-Net) model, which is a hybrid pointer network approach composed of graph neural network (GNN) embedding and attention decoder. We go into developing the simpler embedding version for multiple drones-assisted truck delivery. The CH-Ptr-Net model tends to generate a set of optimal delivery sequence, after constructing the mixed-integer linear program (MILP) formulation. Extensive numerical testing indicates that the proposed method performs better than recent exact and heuristic approaches for collaborative delivery routing optimization with the truck carrying multiple drones.

With the development of Internet of Medical Things(IOMT), a lot of medical data are stored and released for both scientific research and practical applications. Accurate medical data is very valuable, but it also brings a huge risk of privacy leakage. Moreover, improving the privacy of data often leads to the reduction of data validity. Privacy and effectiveness are in conflict, and their balance is a typical multi-objective optimization problem (MOP). In this paper, we try to use differential privacy to disturb medical data to protect personal privacy. We propose the Environment Switching Algorithm (ESA) based on evolutionary learning to solve this MOP. ESA has excellent performance, which can ensure convergence speed and optimization performance at the same time. The result of optimization is a pareto front (PF) of huge scale, which includes solutions with different characteristics. We put forward a method of double clustering to select the appropriate solution from PF. Based on the above, we conclude the whole method as Flexible Differential Privacy Algorithm based on Evolutionary Learning (FDPEL). FDPEL can realize flexible differential privacy for medical data, while ensuring data privacy and data validity. FDPEL is suitable for privacy protection of medical data of different scales, which makes it have a practical applications value.

Recent years have witnessed the increasing popularity and dramatic progress of contactless sensing technologies, which are able to conduct remote signal acquisition without body contact. Both the physical signs and the physiological parameters can be acquired with contactless sensing. This paper introduces popular contactless sensing technologies, explores their application scenarios, and delves into the underlying theoretical principles. It comprehensively reviews the open datasets released in this field, encompassing collection scenarios, sample counts, data formats, and volunteer information. The performance baseline, typical work, and accessible links are also furnished. In addition, it includes discussions on the primary challenges and potential solutions in the context of contactless sensing with open datasets. Finally, suggestions for establishing a high-quality dataset are also given to the community.

The high mobility of Internet of Unmanned Aerial Vehicles (IUAVs) has attracted attention in the field of data collection. With the rapid development of the Internet of Things (IoT), more and more data are generated by IoT networks. IUAV-aided IoT networks can efficiently collect data in specific areas, which is of great significance in disaster relief. In the data collection task, it is necessary to plan the flight trajectory for the data collector—IUAV, so that the IUAV can collect data efficiently. However, existing research basically only considers the efficiency of data collection by IUAVs, but rarely considers the safety of IUAVs during flight. Therefore, this paper proposes an IUAV trajectory planning algorithm that integrates energy efficiency and safety using local search to address the issues mentioned above. At the same time, a Tiny Machine Learning (TinyML) algorithm is designed to assist the IUAV in making real-time decisions during flight. First, we build a general mathematical model that describes the risk in a particular region. Then consider guiding the IUAV to a safer trajectory by introducing virtual nodes in the flight trajectory. Furthermore, we designed a local search algorithm for the three tasks of IUAV access sequence, IoT Networks cluster heads selection and virtual nodes selection, and solved them through iterative optimization. We also consider the unreachable situation of the virtual nodes and use TinyML technology to help the IUAV adjust the position of the virtual nodes in real time in case of an emergency.In the end, an IUAV trajectory is obtained that can efficiently collect IoT networks’ data and fly safely. We have conducted a large number of simulation experiments to demonstrate the efficiency of the proposed algorithm compared to the baseline algorithm.

With the expansion of Internet-of-Things (IoT), security of smart devices is becoming major or primary concern in today’s era. The increasing demand of consumer electronics due its recent evolution, the personal information that is shared is becoming valuable. In addition, the next generation of Industrial Internet of Things (IIoT) devices include features such as low cost, automation, intelligence provision, reduced overhead, efficiency, and remote interactions while communicating or transmitting information among themselves. There are very few authors who have focused on next gen IIoT while improving the efficiency along with providing the security among devices in the network. Therefore, we have proposed a hybrid trusted model by integrating objective model and fuzzy evaluation matrix method to ensure a secure and efficient transmission method among devices in the network. The proposed mechanism is simulated and experimented over various parameters such as detection ratio and network-related performance and functional tests compared to state-of-art solutions.

The rapid advancement of wireless communication and artificial intelligence (AI) has led to a plethora of emerging applications that require exceptional connectivity, minimal latency, and substantial computing resources. The widespread adoption of cloud-edge intelligence is propelling the development of future networks capable of supporting intelligent computing. Mobile edge computing (MEC) technology facilitates the movement of computing resources and storage to the network’s edge, enabling cost-effective offloading of computational tasks for related applications which needs for reduced latency and improved energy efficiency. However, the offloading efficiency is hindered by limitations of wireless transmission capacity. This paper aims to address this issue by integrating reconfigurable intelligent surfaces (RISs) into a cell-free network within an intelligent cloud-edge system. The core idea is to strategically deploy passive RISs around base stations (BSs) to reconstruct the transmission channel and improve the corresponding capacity. Subsequently, we formulate an optimal problem involving joint beamforming for RISs and BSs, which is characterized by non-convexity and complexity. To tackle this challenge, we employ an alternating optimization scheme to ensure the effectiveness of joint beamforming. In particular, deep reinforcement learning (DRL) is leveraged to reduce the computational complexity involved in optimizing task offloading. Additionally, Lyapunov optimization is utilized to model the latency queue and improve the learning efficiency of the offloading framework. We conduct comprehensive evaluations on the wireless system’s capacity, average latency, and energy consumption, considering the integration of RIS with the DRL offloading framework. Experimental results demonstrate that our proposed scheme achieves superior efficiency and robustness.

Human-centric communication metaverse relies on the convergent integration of multiple existing technologies such as 5G and beyond networks, virtual reality, augmented reality, and digital twins, and thus their security vulnerabilities and vulnerability to interference attacks may also be inherited by the metaverse. In particular, existing security policies may be inefficient for communication interference problems encountered in multi-device collaborative computing in a metaverse 5G environment and lack adaptability to metaverse applications. In this paper, we propose a novel intelligent game anti-interference collaborative computing model that accurately describes the interference relationships among source devices, cooperating devices, and interferers in metaverse collaborative computing. We model the offensive and defensive confrontation between multiple metaverse devices as a Stackelberg game, where the source device is the leader, the collaborative computing device acts as the sub-leader, adjusts its antijamming strategy according to the source device’s strategy to improve the source device’s communication anti-jamming performance, and the jammer acts as the follower. We design an intelligent Stackelberg Game-theoretic Policy-based Learning (SGPL) algorithm for jamming resistance in metaverses over 5G and Beyond Networks, where the leaders (co-computing devices) update their training parameters using the total derivatives of the objective function, while the followers (i.e., jammers) update their training parameters using an independent gradient dynamics strategy. Loops are eased and convergence is accelerated by introducing differential dynamics into the leader training network to reflect the interaction structure of the critic and actor network layers. Finally, numerical results demonstrate the effectiveness of the proposed SGPL algorithm in metaverse anti-jamming countermeasures. The proposed SGPL algorithm has the potential to be generalized to other metaverse applications with multi-user attack and defense characteristics.

Intelligent Transportation Systems (ITS) supported by smart vehicles have revolutionized modern transportation, offering a wide range of applications and services, such as electronic toll collection, collision avoidance alarms, real-time parking management, and traffic planning. However, the open communication channels among various entities, including smart vehicles, roadside infrastructure, and fleet management systems, introduce security and privacy vulnerabilities. To address these concerns, we propose a novel security framework, named blockchain-assisted lightweight authenticated key agreement security framework for smart vehicles-enabled ITS (BASF-ITS), which ensures data protection both during transit and while stored on cloud servers. BASF-ITS employs a combination of efficient cryptographic primitives, including hash functions, XOR operator, ASCON, elliptic curve cryptography, and physical unclonable functions (PUF), to design authenticated key agreement schemes. The inclusion of PUF significantly enhances the system’s resistance to physical attacks, preventing tampering attempts. To ensure data integrity when stored on the cloud, our framework incorporates blockchain technology. By leveraging the immutability and decentralization of the blockchain, BASF-ITS effectively safeguards data at rest, providing an additional layer of security. We rigorously analyze the security of BASF-ITS and demonstrate its strong resistance against potential security ass aults, making it a robust and reliable solution for smart vehicle-enabled ITS. In a comparative analysis with contemporary competing schemes, BASF-ITS emerges as a promising approach, offering superior functionality traits, enhanced security features, and reduced computation, communication, and storage costs. Furthermore, we present a practical implementation of BASF-ITS using blockchain technology, showcasing the computational time versus the “transactions per block” and the “number of mined blocks”, confirming its efficiency and viability in real-world scenarios.Note to Practitioners—This article is motivated by designing an efficient, lightweight, and anonymous blockchain-enabled authenticated security framework that can fix the security and privacy concerns in insecure environments for ITS applications, such as automated road speed enforcement, collision avoidance alarm systems, and traffic planning and management, etc. Authenticated key agreement schemes are extensively used to secure communications in the ITS environment. However, the existing state-of-the-art schemes are not efficient in terms of performance, are not resilient against potential security attacks, and do not support anonymity, untraceability, and unlinkability. Therefore, we propose the authenticated security framework to secure communication among the participating entities in the ITS environment. It utilizes efficient cryptographic primitives, such as hash function, XOR-operator, ASCON, elliptic curve cryptography, and PUF. It is shown that the proposed framework can be deployed as a robust tool to address the ITS security problems efficiently. Moreover, the proposed framework is lightweight and efficient and can be easily deployed in various ITS applications and other resource-constrained environments. However, the participating entities, such as vehicles and roadside units, must be PUF-enabled to deploy the proposed framework.

While Digital Twins (DTs) bring convenience to city managers, they also generate new challenges to city network security. Currently, cyberspace security becomes increasingly complicated. Intrusion detection and Deep Learning (DL) are combined with shunning security threats in service computing systems and improving network defense capabilities. DTs can be applied to network security. People's understanding of cyberspace security can be improved using DTs to digitally define, model, and display the network environment and security status. The intrusion detection data are optimized based on DL technology, and a network intrusion detection algorithm integrated with Deep Neural Network (DNN) model is proposed. In the cloud service system, a trust model based on Keyed-Hashing-based Self-Synchronization (KHSS) is introduced. This model predicts the security state and detects attacks according to existing malicious attacks, ensuring the network security defense system's regular operation. Finally, simulation experiments verify the Deep Belief Networks (DBN) model's feasibility and the cloud trust model. The DBN algorithm proposed improves the correct detection rate of unknown samples by 4.05% compared with the Support Vector Machine (SVM) algorithm. From the 20,100 pieces of data in the test dataset, the number of correct attacks detected by the DBN algorithm exceeds those by the SVM algorithm by 818. DBN algorithm requires a short detection time while ensuring optimal detection accuracy. The KHSS+DBN model predicts cloud security states, and the results are the same as the actual states, with an error of only 1%∼2%.

The detection of optic disc and macula is an essential step for ROP (Retinopathy of prematurity) zone segmentation and disease diagnosis. This paper aims to enhance deep learning-based object detection with domain-specific morphological rules. Based on the fundus morphology, we define five morphological rules, i.e., number restriction (maximum number of optic disc and macula is one), size restriction (e.g., optic disc width: 1.05 +/- 0.13 mm), distance restriction (distance between the optic disc and macula/fovea: 4.4 +/- 0.4 mm), angle/slope restriction (optic disc and macula should roughly be positioned in the same horizontal line), position restriction (In OD, the macula is on the left side of the optic disc; vice versa for OS). A case study on 2953 infant fundus images (with 2935 optic disc instances and 2892 macula instances) proves the effectiveness of the proposed method. Without the morphological rules, naïve object detection accuracies of optic disc and macula are 0.955 and 0.719, respectively. With the proposed method, false-positive ROIs (region of interest) are further ruled out, and the accuracy of the macula is raised to 0.811. The IoU (intersection over union) and RCE (relative center error) metrics are also improved .

Integrated satellite-aerial-terrestrial relay networks (ISATRNs) have been considered as a promising architecture for next-generation networks, where high altitude platform (HAP) is pivotal in these integrated networks. In this paper, we introduce a novel model for HAP-based ISATRNs with mixed FSO/RF transmission mode, which incorporates unmanned aerial vehicles (UAVs) equipped with reconfigurable intelligent surfaces (RISs) to dynamically reconfigure the propagation environment and fulfill the massive access requirements of ground users. Our aim is to maximize the system ergodic rate by joint optimizing the UAV trajectory, RIS phase shift, and active transmit beamforming matrix under the constraint of UAV energy consumption. To solve this intractable problem, a deep reinforcement learning (DRL)-based energy efficient optimization scheme by utilizing an improved long short-term memory (LSTM)-double deep Q-network (DDQN) framework is proposed. Numerical results demonstrate the superiority of our proposed algorithm over the traditional DDQN algorithm, on single-step exploration average reward values and other evaluation metrics.

Opinion spam detection is a challenge for online review systems and social forum operators. Opinion spamming costs businesses and people money since it deceives customers as well as automated opinion mining and sentiment analysis systems by bestowing undeserved positive opinions on target firms and/or bestowing fake negative opinions on others. One popular detection approach is to model a review system as a network of users, products, and reviews, for example using review graph models. In this article, we study the effects of network scale on network-based review spammer detection models, specifically on the trust model and the SpammerRank model. We then evaluate both network models using two large publicly available review datasets, namely: the Amazon dataset (containing 6 million reviews by more than 2 million reviewers) and the UCSD dataset (containing over 82 million reviews by 21 million reviewers). It has been observed thatSpammerRank model provides a better scaling time for applications requiring reviewer indicators and in case of trust model distributions are flattening out indicating variance of reviews with respect to spamming. Detailed observations on the scaling effects of these models are reported in the result section.

As artificial intelligence, the Internet of Things, and information and communication technologies continue to advance, smart healthcare systems are increasingly becoming a cornerstone of modern society. However, this progress brings with it significant concerns regarding the privacy and security of biomedical Big Data. Within smart healthcare systems, biomedical data forms an expansive and intricate web, encompassing a wide array of information from imaging to audio recordings, and various biological signals [1]. Moreover, the widespread adoption of wearable devices, coupled with a heightened public awareness of health, has precipitated a surge in data volume to a Big Data scale. This escalation presents unprecedented challenges to ensuring the confidentiality and integrity of data, demanding robust protection measures for privacy and security [2].

The technology of Unmanned Aerial Vehicles (UAVs) has sparked a revolution in numerous Internet of Things (IoT) applications, such as flood monitoring, wildfire monitoring, coastal area surveillance, intelligent transportation, and classified military operations, etc. This technology offers several advantages when used as a flying base station to enhance the communication metrics of an employed IoT appplication. However, as an integrated technology (UAV-assisted IoT applications), it suffers from many challenges, and security is one of the foremost concerns. Considering that, in this paper, we proposed a hybrid lightweight key exchange authentication model for UAV-assisted IoT applications to resolve the device-to-device (D2D) authentication and data privacy issues in these networks. The proposed model employs five different security parameters named registration, authentication, authorization, accounting, and cache wash and update (R3ACWU) in coordination with a hash function. The network architecture consists of UAVs, IoT devices, and micro base stations, followed by base stations, authentication servers, and service providers (SP). In this framework, we introduce a concept known as ‘dead time’, a specific time period after which each device’s cache memory is cleared and updated. This practice not only enhances the security of the devices in use but also reduces computational and memory overhead by eliminating the records of devices that haven’t participated in the communication process within the specified time frame. Results statistics of our lightweight R3ACWU authentication scheme exhibit notable improvement corresponded to the present authentication schemes in terms of comparative parameters.

According to research, the vast majority of road accidents (90%) are the result of human error, with only a small percentage (2%) being caused by malfunctions in the vehicle. Smart vehicles have gained significant attention as potential solutions to address such issues. In the future of transportation, travel comfort and road safety will be ensured while also offering several value-added services. The automotive industry has undergone a significant transformation through the use of emerging technologies and wireless communication channels, resulting in vehicles becoming more interconnected, intelligent, and safe. However, these technologies and communication systems are susceptible to numerous security attacks. The objective of this paper is to present a comprehensive overview of the smart vehicle’s architecture, encompassing emerging technologies and security challenges and solutions associated with smart vehicles. There has been a significant surge in the utilization of machine learning techniques in smart vehicles. We categorically discuss common security measures, including machine learning and deep learning based solutions that have been mentioned in the literature and implemented against security threats on smart vehicles. This paper has also been titled a tutorial due to its layout, which begins with covering preliminary knowledge, terminologies, and encompassing technologies required to comprehend smart vehicles. Following this, the paper addresses the overall challenges associated with smart vehicles and then focuses on security issues. In terms of solutions, the paper discusses overall solutions to security issues in smart vehicles before delving into a specific solution based on machine learning and deep learning.

Information-centric networking (ICN) allows data to be cached at each node in the network. It is vital in vehicular networks (VNs) to improve caching performance and reduce content delay in high-traffic scenarios. In cooperative VNs, the requested content can be cached in the base station or nearby nodes without fetching the requested content from the server. The existing content popularity approaches face challenges in predicting popular content due to a time-varying environment, resulting in popularity being changed frequently. It is hard to predict such content in highly dynamic vehicular traffic. Therefore, the current approaches are less practical in a realistic scenario. This paper proposes an intelligent caching method for massive traffic in VNs to address these issues based on deep transfer learning (DTL). The primary purpose of this study is to reduce the system cost and content delay by increasing the cache hit rate based on popular data in dynamic traffic. The proposed solution uses a collaborative cache with social interaction among clusters to share the most popular content (MPC). Furthermore, it designs a time-varying mechanism to predict content popularity in a highly dynamic environment and share the widespread knowledge with other target nodes based on DTL. In addition, a content update method is developed to address the content replacement in a cooperative cache environment. Based on thorough analysis and evaluation, similar and dissimilar contents on base stations are classified among source and target clusters. The extensive simulation and experimentation confirm that the developed work achieved better than baseline studies.

Security assurance is the capacity of any teleinformatic system to demonstrate that the system is secure. It is provided by testing the system by an independent laboratory. Such an evaluation gives to the customer certainty that the system or product is secure enough for the intended use. In this paper we discuss the security assurance that the automotive sector requires to the 5G network for the secure communication of the Intelligent Transport System applications. In this case, the automotive sector takes the role of the customer of the network operator that provides connectivity of cars, trucks, bicycles, pedestrians and traffic infrastructure for creating vehicle to everything platform. Concretely, in this paper we (1) show new methodologies for evaluating network security, (2) provide a new strategy for the customer (automotive company) to select the underlying system based on network assurance levels, (3) survey security functionalities of a network devoted to automotive applications and demonstrate how automotive applications, 5G network and physical infrastructure, cooperate for enhancing security of the end-to-end system, and (4) provide example security evaluation results at different assurance levels. The results show the necessity of providing different levels of network assurance during the certification process in order that the automotive customer will be able to select the best products and sub-systems that may demonstrate (assure) enough security to the complete system.

Crack detection has attracted extensive attention in an intelligent transportation system (ITS). Despite the substantial progress of deep learning technology on crack recognition tasks, due to the various limitations in traffic, equipment, and time, it is hard to collect copious samples for training deep models. Considering this, implicitly semantic data augmentation (ISDA) tries to augment the training set in the feature space. However, when applying it to crack recognition tasks, our empirical studies reveal that those poor-classified augmented samples have little semantic relevance to the crack class, resulting in a non-negligible negative effect on training deep models. Since the augmented features follow the multivariate normal distribution, it is computationally inefficient to explicitly sample those features and filter out the hard-classified augmented features. To this end, we propose the implicit gradient-modulated semantic data augmentation (IGMSDA) for addressing the above problems. Concretely, this paper first proposes gradient-modulated (GM) loss to dynamically modulate the gradient of those poor-classified augmented samples by reshaping the standard cross-entropy loss. And then, in the feature space, we derive an upper bound of the expected GM loss on the augmented training set to avoid the costly explicit sampling process. Experiments show that IGMSDA improves the generalization performance of the existing deep models on crack recognition datasets.

Strategic investment(s) in vehicle automation technologies led to the rapid development of technology that revolutionised transport services and reduced fatalities on a scale never seen before. Technological advancements and their integration in Connected Autonomous Vehicles (CAVs) increased uptake and adoption and pushed firmly for the development of highly supportive legal and regulatory and testing environments. However, systemic threats to the security and privacy of technologies and lack of data transparency have created a dynamic threat landscape within which the establishment and verification of security and privacy requirements proved to be an arduous task. In CAVs security and privacy issues can affect the resilience of these systems and hinder the safety of the passengers. Existing research efforts have been placed to investigate the security issues in CAVs and propose solutions across the whole spectrum of cyber resilience. This paper examines the state-of-the-art in security and privacy solutions for CAVs. It investigates their integration challenges, drawbacks and efficiencies when coupled with distributed technologies such as Blockchain. It has also listed different cyber-attacks being investigated while designing security and privacy mechanism for CAVs.

The continuous evolution of cellular networks has resulted in the rapid increase in both mobile applications and devices in the Internet of Vehicles. The introduction of the multi-access edge computing method makes it possible for vehicles in remote areas to offload their computational tasks, which can effectively relieve the computing pressure of local devices and reduce the computational delay as well. Tasks offloading for multi-user is a resource competition problem, especially in dynamic environments, which is difficult to be solved by traditional algorithms. In this article, we propose a two-layer hybrid system with local and edge computing, providing convenient computing and offloading services for vehicle users in dual dynamic scenarios of task generation and vehicle mobility. The delay and queuing situations are considered comprehensively in the formulated optimization problem, which can be solved by the proposed deep deterministic policy gradient-based computation offloading algorithm. The offloading process of the vehicle tasks in dynamic scenarios is transformed into a Markov decision process to obtain the offloading strategy. Simulation results demonstrate the performance advantages of two-tier computing architecture. Compared with random offloading, deep Q network-based offloading, and local computing, the algorithm proposed in this article gains the highest average reward of tasks. Besides that, numerical results also prove that our algorithm has the lowest average delay under different computing capabilities of edge servers.

This article investigates the formation control of autonomous underwater vehicles (AUVs) suffering from unknown sea loads, unmoulded structure, limited communication and multiple static and moving obstacles. Given the challenge, a novel adaptive dual-channel event-triggered control scheme is proposed for formation tracking and obstacles avoidance. To economize the communication resources, the dual-channel event-triggered mechanism is designed in the sensor-to-controller and controller-to-actuator channels respectively. By adopting the approximation of fuzzy systems in the form of one-parameter integrated learning, the uncertainties consisted of the unmoulded structure and unknown sea loads are compressed together to be compensated online, which ensures a lower computational cost. To solve the multiple obstacles, the modified artificial potential field approach is employed, and the derived repulsive potential field can ensure that the multi-AUV formation can avoid obstacles smoothly regardless of static or moving obstacles. It is showed by the Lyapunov stability theorem that the tracking errors are guaranteed to be semi-globally uniformly ultimately bounded. Finally, three simulation examples illustrate the effectiveness and superiority of the proposed scheme.

As the Internet of Vehicles (IoV) has become the critical part of Intelligent Vehicular Transportation Systems (IVTS), massive IoV entities (e.g., RSU, OBU, pedestrians’ mobile devices, etc.) get involved into IVTS. At present, one of the biggest challenges with IoV/IVTS is how to maintain a balance between security and privacy. The receivers need to be sure that they are receiving reliable messages from the origin and could trace or link the attacker’s identity, but the tracing or linking may work against the sender’s need for identity privacy. To solve the security and privacy problem, most of current works have proposed authentication solutions to provide anonymous, traceable and unlinkable schemes, which are still vulnerable to either Sybil attacks or quantum attacks. Therefore, we propose the blockchain-based post-quantum anonymous, traceable and linkable authentication scheme by utilizing NIST winner post-quantum algorithms and related post-quantum linkable ring signature. Grounded on the authentication scheme, we also develop key exchange mechanism, which help IoV entities perform efficient message authentication encryption/decryption during P2P communication and broadcast. The security analysis shows that our proposal is resistant to Sybil attack and provides other essential security characteristics including man-in-the-middle-proof and anti-replay. Finally, we perform detailed performance evaluation including each on-chain API execution time, the off-chain communication time and the on-board/on-chain storage requirements. To further evaluate the feasibility of our scheme in the IoV/IVTS environment, we also show the effectiveness of our proposal in a blockchain-based simulation study.

Special Issue on Deep Learning for Intelligent Human Computer InteractionDeep Learning ( DL ) is growing at a fast pace with a plethora of related research being conducted and industrial applications being developed.Robotics, computer graphics, computer vision including areas such as feature extraction/matching, 3D reconstruction, manufacturing, medicine, knowledge acquisition, control theory, planning and scheduling, among others, have uncovered the potential of DL.The reason for such success mainly stems from two aspects.On the one hand, the theoretical advances in related disciplines such as optimization methods, pattern recognition, and hardware (GPUs) have undergone great breakthroughs.On the other hand, the technical applications in industry have addressed many actual problems that have in turn accelerated DL development.Leading technology companies like Microsoft, Apple, Google, Facebook, Nvidia, and Amazon all launched cutting-edge industrial products offering advanced functionalities that were only made possible thanks to the employment of DL algorithms.The effort in establishing new media and network technologies led to the identification of a niche for ML in the field of humancomputer interaction.Human-computer interaction ( HCI ) is a multidisciplinary field of study focusing on the design of computer technology through the analysis and understanding of how humans interact with computers.HCI addresses various areas, such as user interface design or computer-supported cooperative work for data modeling, system adaptation and optimization to fit user needs, complex systems, the development of smart equipment to ease utilization, and the design of smart environments to promote user comfort and safety.Nowadays, DL is largely employed in HCI to model human behavior as they have shown to outperform traditional modeling techniques.Although their potential is undoubtedly broad, there are several important research challenges that remain unaddressed.The explainability and interpretability of the decision-making process of DL approaches is key to assess models' behaviors, especially when unexpected or incorrect outputs occur.The complexity and the dimensionality of the underlying mathematical models are among the principal causes that hinder understanding.Privacy must be guaranteed as sensible private data is typically continually collected, processed, and stored over the Cloud in order to provide improved user-specific experiences.Such continual learning for DL approaches is itself a challenge as models may suffer from catastrophic forgetting, but it is a key ingredient that needs to be taken into consideration to enable scalability.Data annotation done at large scale is costly and hardly sustainable, therefore particular attention must be put on self-supervised or unsupervised learning mechanisms.DL-based user interfaces, such as gesture recognition, must be supported

The current progressive research in the domain of black-box adversarial attack enhances the reliability of deep neural network (DNN)-based video systems. Recent works mainly carry out black-box adversarial attacks on video systems by query-based parameter dimension reduction. However, the additional temporal dimension of video data leads to massive query consumption and low attack success rate. In this article, we embark on our efforts to design an effective adversarial attack on popular video classification systems. We deeply root the observations that the DNN-based systems are sensitive to adversarial perturbations with high frequency and reconstructed shape. Specifically, we propose a systematic attack pipeline Cube-Evo, aiming to reduce the search space dimension and obtain the effective adversarial perturbation via the optimal parameter group updating. We evaluate the proposed attack pipeline on two popular datasets: UCF101 and JESTER. Our attack pipeline reduces query consumption and achieves a high success rate on various DNN-based video classification systems. Compared with the state-of-the-art method Geo-Trap-Att, our pipeline averagely reduces 1.6× query consumption in untargeted attacks and 2.9× in targeted attacks. Besides, Cube-Evo improves 13% attack success rate on average, achieving new state-of-the-art results over diverse video classification systems.

Mobile Crowd Sensing (MCS) has emerged as a novel paradigm in massive data collection, which leverages many individual mobile devices (called workers) to collect data. MCS platform utilizes the collected data to construct various services for service requesters, thus obtaining profit based on the data values contributed by workers. However, untrustworthy data would greatly reduce the data value, leading to a decline in platform profit, so it is crucial for the platform to recruit high-trust workers and collect truthful data, thereby providing high-quality service and obtaining high profit. To address this problem, we propose a Maximize Profit Scheme, called MPS, for MCS platforms, which consider that the data value declines as data trust decreases and discounts over time. MPS scheme is the first work that systematically addresses the impact of untruthful data on the platform profit, which is not well addressed in previous research. First, we utilize historical data of trusted workers as truthful data to identify the truth of data, which is a low-cost method. Then, a trust-discounting and time-discounting value model is proposed, which is more practical than previous methods. Based on the proposed value model, we propose a novel worker recruitment strategy combined with a trust-related and time-dependent reward threshold, which prioritizes workers with high trust and low latency, thereby promoting the data value of workers and maximizing the platform's profit. By comparing the MPS with existing schemes, the experimental results show that our MPS can achieve better performance in terms of total profit.

The performance testing and optimization of cloud applications is challenging, because manual tuning of cloud computing stacks is tedious and automated tuning tools are rare used for cloud services. To address this issue, we introduce KeenTune, an automated tuning tool designed to optimize application performance and facilitate performance testing. KeenTune is a lightweight and flexible tool that can be deployed with to-be-tuned applications with negligible impact on their performance. Specifically, KeenTune uses a surrogate model that can be implemented with machine learning models to filter out less relevant parameters for efficient tuning. Our empirical evaluation shows that KeenTune significantly enhances the throughput performance of Nginx web servers, resulting in performance improvements of up to 90.43% and 117.23% in certain cases. This study highlights the benefits of using KeenTune for achieving efficient and effective performance testing of cloud applications. The video and source code for KeenTune are provided as supplementary materials.

With the swift growth of mobile devices in a wireless Internet of Things (IoT) network, mobility management in IP networks has attracted significant research interest due to certain issues with the frequent motion of mobile nodes (MNs) participating in the handover process. Hierarchical mobile IPv6 (HMIPv6) is one of the protocols proposed to accommodate recurrent mobility of MNs to decrease the signaling amount and improve packet loss issues, by localizing the mobility. HMIPv6 treats micro and macro mobility separately by introducing a new level in mobile IPv6 (MIPv6) with the addition of a node called mobility anchor point (MAP) which significantly improved handover performance specifically in micro-mobility. However, macro mobility yet needed to be improved as it is managed in a way as MIPv6 does. The registration of MN with the new MAP domain is a lengthy process, which leads to longer handover latency with high signaling cost to start proper communication again. Here, we proposed an advance-binding update HMIPv6 (A-BU), a group-based scheme to lessen interdomain handoff latency of IoT MNs. The efficiency of the proposed technique is validated with numerical analysis. The results compared with state-of-the-art mobility protocols illustrate a visible reduction in handover cost, signaling cost and provide seamless packet delivery during group-based interdomain mobility.

The integration of thriving information and communications technology (ICT) and cyber–physical systems (CPSs) has spawned several innovative applications, such as remote healthcare, smart and intelligent transportation, smart logistics, smart grids, and public safety. An emerging software-defined networks (SDNs) technology further enabled to optimize the communication among Industrial IoT (IIoT) and CPS entities. Nonetheless, the communication on public channel among different IIoT entities in an SDN-enabled environment may be exposed to various security threats due to wireless and insecure communication channels. To counter these security challenges in the way of wider CPS or IIoT adoption, we propose a novel three-factor authenticated key exchange mechanism (SUSIC) for SDN-enabled IIoT ecosystem. The SUSIC enables a registered user to access real-time data from physical IIoT environment directly after having mutual authentication performed through SDN-enabled controller node. The scheme is proved to be secure under rigorous formal and informal security analysis. Moreover, the simulation results and performance evaluation signifies toward achieving a better tradeoff between security functionalities and computational overheads comparatively.

Unmanned aerial vehicles (UAVs) will be a vital part of the massive Industrial Internet of Things (IIoT) in the 5G and 6G paradigms. The UAVs are required to collaborate with each other to deal with some computation-intensive IIoT applications in an autonomous UAV system. However, due to the limited processing capacity of UAVs, they are occasionally unable to handle certain tasks adequately (e.g., crowdsensing). Therefore, it is an important issue to realize efficient offloading of these computation-intensive IIoT applications. In this article, we first partition the computation-intensive IIoT application into a directed acyclic graph with multiple collaborative tasks. Then, we establish a joint optimization problem based on the models of the processor resources and energy consumption for the task offloading scheme. Third, we propose a cooperative resource allocation approach to optimize the joint optimization problem under the constraints of resource and communication latency, and then can migrate more computation-intensive tasks to the edge clouds. Finally, we build an aerial computing simulation system and make a comparative evaluation and analysis of our proposed cooperative resource allocation approach in terms of effectiveness and performance. The experimental results show that our proposed approach performs better than other related approaches.

With the rapid development of the Internet of Things (IoT), massive terminals and emerging applications bring a large amount of data. As an essential prerequisite for mining potential value from these data, data sharing is crucial and getting more and more attention. However, security and privacy concerns, and the lack of effective incentive mechanisms in the decentralized environment hinder the data owners from sharing their data. In this article, we propose a federated learning (FL) and blockchain-based privacy-preserving data sharing system, which solve the privacy leakage problem of FL and the scalability problem of blockchain by their technical complementarity. Considering the resource shortage and the heterogeneity of terminals, we first design a cross-layer architecture by exploiting the cloud–edge–terminal collaboration, and innovatively propose differential data sharing by dividing the providers into origin data providers and model providers. Then, we design a targeted incentive mechanism to maximize the utility of requesters and two types of data providers by formulating it into a two-stage Stackelberg game. Finally, a gradient-based algorithm is designed to get the optimal solution. Theoretical analysis and numerical simulations show that the proposed data-sharing system could eliminate privacy concerns and motivate more participants to share data. With 500 nodes, the differentiated data sharing scheme (DASC) is 1.72 s faster than the shared model scheme (SMC) and 2.59 s faster than SDC. In terms of utility of the requester, DASC is$2.0 \times {10^{3}}$higher than SDC, and 9.27 higher than SMC in average utility of providers.

Internet of Things (IoT) devices are widely being used in various smart applications and being equipped with cognitive radio (CR) capabilities for dynamic spectrum allocation. Our objectives in this work are to achieve higher data rates and minimize end-to-end routing delays in CR-enabled IoT communication in order to maximize throughput. We propose a reinforcement learning (RL)-based routing approach in the cognitive radio network (CRN)-based IoT environment. The idea is to add the channel selection decision capability to the network layer in order to minimize packet collisions as well as end-to-end delay (EED). We perform a comprehensive performance evaluation of the proposed RL-IoT routing mechanism by simulating the cognitive radio-enabled Internet of Things (CR-IoT) communication environment in the cognitive radio cognitive network (CRCN) simulator and comparing the network performance achieved by our proposed mechanism with that of the recent AODV-based routing mechanism for IoT (AODV-IoT), ELD-CRN, and SpEED-IoT routing approaches. Our evaluation results show that the RL-IoT model performs better than existing approaches in terms of average data rate, throughput, packet collision, and EED.

Autonomous underwater vehicle (AUV) shows great potential in the Internet of Underwater Things (IoUT) system, in which the path planning algorithm plays a fundamental role. However, the complex underwater environment brings greater challenges to AUV path planning, especially the ocean current, which has a profound impact on time and energy consumption. This article focuses on the complex ocean current condition and proposes an underwater path planning method based on proximal policy optimization (UP4O). In this novel method, a deep reinforcement network is constructed to serve as a decision control to plan the moving direction of AUV. An information encoding module is developed to extract the features of the local obstacles. Furthermore, UP4O integrates the obstacle features with the current state information, including relative position, ocean current, and velocity, enabling the AUV to focus on the global direction and local obstacles at the same time. Additionally, to further adapt to the ocean current and shorten the time cost, UP4O expands the action space of AUV, realizing a fine and flexible action adjustment. The wide applicability of UP4O has been proved by numerous experiments. The proposed algorithm can always plan the time-saving and collision-free paths in complex underwater environments with various terrains and ocean current.

Cyber–physical systems (CPSs) are new types of intelligent systems that integrate computing, control, and communication technologies, bridging the cyberspace and physical world. These systems enhance the capabilities of our critical infrastructure and are widely used in a variety of safety-critical systems. CPSs are susceptible to cyber attacks due to their vulnerabilities such that their security has become a critical issue. Therefore, it is important to classify and comprehensively investigate this issue. Most of the existing surveys on it are conducted from a single perspective. In this article, we present a comprehensive view of the security of CPSs from three perspectives: 1) the physical domain; 2) the cyber domain; and 3) the cyber–physical domain. In the physical domain, we review some attacks that directly damage the physical components of CPSs such as sensors and discuss corresponding defenses. We also review the attacks that CPSs in the cyber domain may face and study methods to detect and defend against them. In addition, we survey the intelligent attacks faced by CPSs and the corresponding defensive means. In the cyber–physical domain, we provide an overview of attacks that come from the cyber domain and eventually damage the physical parts, and discuss the corresponding detection and defense methods. Finally, we present the challenges and future research directions. Through this in-depth review, we attempt to summarize the current security threats to CPSs and the state-of-the-art security means to provide researchers with a comprehensive overview.

With the continuous improvement of hardware computing power, edge computing of industrial data has been gradually applied. In the past decade, the promotion of edge computing has also greatly improved the efficiency of industrial production. Compared with the conventional cloud computing, it not only saves the bandwidth consumption of data transmission, but also ensures the terminal data security to a certain extent. However, the continuous update of attack types also put forward new requirements for the privacy protection of industrial edge computing. So it should fundamentally solve the risk of industrial data leakage in the process of deep model training in edge terminal. In this article, we propose a new federated edge learning framework based on hybrid differential privacy and adaptive compression for industrial data processing. Specifically, it first completes the adaptive gradient compression preparation, then constructs the industrial federated learning model, and finally makes use of adaptive differential privacy model to optimize, so as to complete the privacy protection towards the transmission of gradient parameters in industrial environment. By optimizing the hybrid differential privacy and adaptive compression, we can better prevent the terminal data privacy against inference attacks. The experimental results show that this method is very effective in the industrial edge computing situation, and it also opens up a new direction for the effect of differential privacy in federated learning.

For emerging industrial Internet of Things (IIoT), intelligent anomaly detection is a key step to build smart industry. Especially, explosive time-series data pose enormous challenges to the information mining and processing for modern industry. How to identify and detect the multidimensional industrial time-series anomaly is an important issue. However, most of the existing studies fail to handle with large amounts of unlabeled data, thus generating the undesirable results. In this article, we propose a novel integrated deep generative model, which is built by generative adversarial networks based on bidirectional long short-term memory and attention mechanism (AMBi-GAN). The structure for the generator and the discriminator is the bidirectional long short-term memory with attention mechanism, which can capture time-series dependence. Reconstruction loss and generation loss test the input of sample training space and random latent space. Experimental results show that the detection performance of our proposed AMBi-GAN has the potential to improve the detection accuracy of industrial multidimensional time-series anomaly toward IIoT in the era of artificial intelligence.

Technological advances in the Internet of Things (IoTs) and the evolution of manufacturing models applied in industrial environments increase the use of concepts, such as plug-and-play in intelligent manufacturing systems for customized products. However, plug-and-play manufacturing modes are subjected to dynamic changes in both products and resources during the runtime phase. Hence, traditional model-based formal verification methods cannot guarantee system dependability before and after the reconfiguration. In addition, customized manufacturing is a product-driven process, which requires product- and equipment-based modeling and analysis to be considered during the product life-cycle management. In this article, a model-data-driven (DD) framework for the formal analysis of production resources is proposed. An IoT-based DD technology is combined with a model-driven approach to ensure dependability verification of formal models in the operational production line phase through real-time resource status feedback. The proposed method is verified experimentally, using a smart factory experimental platform. The results show that the proposed method reduces the time-cost of the formal modeling phase by at least 75%.

Intelligent robots are a current application in Industrial Internet of Things (IIoT), with their trustworthiness being a topic of considerable research interest. Vulnerabilities in robot software may affect the trustworthiness of robotics. To detect these vulnerabilities in robot software, this study proposes a differential fuzz testing method. The main idea is to continuously execute test cases for different versions of software packages to detect inconsistencies among outputs and eventually discover vulnerabilities. First, test cases are generated, combining seed generation and mutation, after which the measured model of the packages in the robot visualization tool (RVIZ) is built and the generated seeds are executed. The differences among inconsistent outputs are calculated and the causes of the differences analyzed. Two evaluation metrics for the inconsistencies and seeds are presented. This method is applied to the crucial package in ROS-MoveIt!. The results show that thearm.go()ofmoveit_commanderhas joint angle overflow.

The fifth generation network offers a powerful infrastructure for Industrial internet of things (IoT) in that its support on massive machine-type communications (mMTC). Recently, grant-free access has been recognized as the promising new access approach for mMTC; however, it also introduces the potential risk. To timely discover intrusion, in this article, we propose a learning network by extracting traffic load information from the states (success, collision, and idle) of access resources observed at media access control and physical layers. In particular, our learning network consists of three concatenated function modules, i.e., traffic load estimation, traffic load prediction, and intrusion identification. Moreover, our proposed learning network is able to identify two types of intrusion, i.e., false data dissemination and congestion attack. Simulation results indicate that the proposed scheme can effectively capture the number of active devices, provide reasonable prediction by using history records, and eventually, achieve more accurate detection compared with baseline approaches.

The papers in this special section focus on embedded microprocessors under wireless transmission in medical imaging lesion detection systems and provides researchers in related fields with opportunities for discussions.

Unmanned aerial vehicles (UAVs) as an intermediary can offer an efficient and useful communication paradigm for different Internet of Things (IoT) applications. Following the operational capabilities of IoTs, this emerging technology could be extremely helpful in the area, where human access is not possible. Because IoT devices are employed in an infrastructure-less environment, where they communicate with each other via the wireless medium to share accumulated data in network topological order. However, the unstructured deployment with wireless and dynamic communication make them disclosed to various security threats, which need to be addressed for their efficient results. Therefore, the primary objective of this work is to present a comprehensive survey of the theoretical literature associated with security concerns of this emerging technology from 2015-to-2022. To follow up this, we have overviewed different security threats of UAV-aided IoT applications followed by their countermeasures techniques to identify the current challenges and requirements of this emerging technology paradigm that must be addressed by researchers, enterprise market, and industry stakeholders. In light of underscored constrains, we have highlighted the open security challenges that could be assumed a move forward step toward setting the future research insights. By doing this, we set a preface for the answer to a question, why this paper is needed in the presence of published review articles. For novelty and uniqueness, we have performed a comparative analysis section-wise with rival papers to demonstrate that how this paper is different from them.

In recent years researchers have shown significant interest in vehicular networks to augment road safety by providing real-time messaging services among vehicles. This work aims to provide a detailed analysis of emergency message dissemination techniques for the Internet of Vehicles (IoV). We explored position-based data dissemination techniques for emergency message dissemination, which is considered the best routing method because it does not rely on predestination entries of the route. Position-based schemes encounter some challenges, such as delay and accurate positioning. Existing survey papers of IoV focused on architecture, technologies, and layers. However, this article examines a brief comparison of subtypes of position-based emergency message routing, beacon-oriented and beacon-less techniques. In the end, we presented the basic challenges of emergency message dissemination; moreover, future directions are highlighted to promote the development of new protocols for emergency message dissemination to enhance the efficiency of IoV in a better way.

Since millions of smart vehicles in Internet-of-Vehicles (IoV) produce and relay data to analyze road conditions, creating social networks of vehicles in IoV is an important factor for the future Intelligent Transportation System (ITS). Likewise, the IoV architecture has seen vertical fragmentation of approaches used to meet the needs of different work domains. Therefore, IoV in combination with social networking, called Social IoV (SIoV), was created to address these alleged problems. However, one of the challenges in SIoV is that the social relations between vehicles grow and deplete very fast due to the extremely dynamic and unstable nature of the IoV. Therefore, a clustering-based scheme for SIoV, which is efficient in terms of stability can overcome this problem. We propose SeAC: an SDN-enabled adaptive clustering technique for SIoV. SeAC uses a 3D modeling approach to construct logical clusters that are based on factors such as physical location, social tie, and interest similarity among vehicles. Therefore, SeAC improves the stability of clusters and the efficiency of the underlying SIoV architecture. Additionally, by minimizing the trade-off between social and physical distances, SeAC lowers communication and computation costs. We evaluate SeAC, and the simulation results show that for two different topologies, the adaptive approach using SeAC can produce better results in terms of a stable cluster formation.

Road hazards (RH) have always been the cause of many serious traffic accidents. These have posed a threat to the safety of drivers, passengers, and pedestrians, and have also resulted in significant losses to people and even to the economies of countries. Hence, road hazards detection (RHD) could play an essential role in intelligent transportation systems (hypertarget ITSITS). The cooperative vehicle-infrastructure systems (CVIS) coordinate the communication between vehicles and roadside infrastructures. Onboard computing devices (OCD), then, make fast analyses and decisions based on road conditions. In this study, an RHD solution based on CVIS is proposed. Firstly, a high-performance heavy action detection model is selected. Using a meta-learning paradigm, critical features are generalized from a few-shot RH data. Secondly, we designed a lightweight RHD model to ensure its smooth inference on an OCD. Thirdly, we use a knowledge distillation (KD) framework to progressively distill the features of the complex model and the privileged information of the data into the lightweight one. Experimental results demonstrate that the model can effectively detect RH and obtain an accuracy of 90.2% with an inference time of 14.7ms.

In this paper, we discuss the co-optimized performance of multi-reconfigurable intelligent surface (RIS)-assisted integrated satellite-unmanned aerial vehicle-terrestrial network (IS-UAV-TN), where the multiple vehicle users are applied to the network under consideration. The performance optimization of IS-UAV-TNs faces two major challenges: one is the obstacles in the transmission path and the other is the highly dynamic communication environment caused by the UAV movement for the multiple ground vehicle users. To tackle these above issues efficiently, we will install RIS on the UAV for the purpose of reshaping the wireless transmission path. In addition, non-orthogonal multiple access (NOMA) protocols are considered as a new paradigm to address spectrum shortage and enhance connection quality. Considering the UAV energy consumption, the satellite transmission beamforming matrix and RIS phase shift configuration, a multi-objective optimization problem is proposed to maximize the system achievable rate and minimize the UAV energy consumption during a specific mission. On this foundation, to facilitate the online decision problem, the deep reinforcement learning (DRL) algorithm is utilized to achieve real-time interaction with the communication environment. A multi-objective deep deterministic policy gradient (MO-DDPG) algorithm is proposed to search for sub-optimal solutions about the learning problem of multi-objective control policies in IS-UAV-TNs. Experimental results show that the method can simultaneously consider three optimization objectives and effectively adjust the optimal update policy according to the settings of different weight parameters.

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs.

Drone logistics delivery is a potential booster to redefine the logistics system efficiency, which has been a new special hot research field. Among that, how to optimize drone trajectory data and find optimal delivery path is a crucial problem. However, most existing studies fail to derive the feasible trajectory data due to underestimating drone energy consumption and delivery multi-variant constraints for air transportation. In this paper, we propose a novel self-driven learning procedure, named attention-based pointer network (A-Ptr-Net) model, which can solve drone delivery trajectory optimization problem. The generated A-Ptr-Net model coupled with attention mechanism is effective on adapting to the new drone trajectory data automatically, regardless of explicit distance matrix. We go into developing the convex function constraints related to drone nonlinear energy consumption, customer demand and service time windows, which is applied on A-Ptr-Net model for optimizing the drone logistics delivery. Numerical experiments indicate that the proposed method performs significantly better than classical heuristics for drone trajectory analysis and optimization.

The intelligent transportation systems improve the transportation system’s operational efficiency and enhance its safety and reliability by high-tech means such as information technology, control technology, and computer technology. In recent years, sustainable development has become an important topic in intelligent transportation’s development, including new infrastructure and energy distribution, new energy vehicles and new transportation systems, and the development of low-carbon and intelligent transportation equipment. New energy vehicles’ development is a significant part of green transportation, and its automation performance improvement is vital for smart transportation.

The Internet of Things (IoT), as an important part of the new generation of information technology, connects any object to the Internet according to the agreed protocol through radio frequency identification, global positioning system, and other information sensing equipment for information exchange and communication. With the continuous development of the IoT technology, it has injected new power into its further development and improvement. The Internet of Vehicles (IoV) is the development focus of the IoT, and the improvement of its connection capability enables the application of IoV to be upgraded from vehicle entertainment to unmanned driving, fleet arrangement and management, and traffic intelligent service. With the release of the market potential of IoV, the transportation cost will also drop significantly, and more transformation opportunities will emerge in the traditional intelligent transportation industry. As an important part of intelligent transportation, intelligent city, intelligent village, and intelligent park, the IoT intelligent infrastructure plays an important role in providing high-quality public services, reducing costs, and achieving sustainable development. At present, the IoT intelligent infrastructure has a wide range of demand around the world, and has become an important innovation and industrial development force in this field.

Out-of-gas errors occur when smart contract programs are provided with inputs that cause excessive gas consumption and which will be easily exploited to perform Denial-of-Service attacks. Various approaches have been proposed to estimate the gas limit of a function in smart contracts to avoid such error. However, underestimation often occurs when the contract is complex In this work, we propose V-Gas, which automatically generates inputs that maximize the gas cost and reduce underestimation. V-Gas is designed based on static analysis and feedback-directed mutational fuzz testing. First, V-Gas builds the gas weighted control flow graph of functions in smart contracts. Then, V-Gas develops gas consumption guided selection and mutation strategies to generate the input that maximize the gas consumption. For evaluation, we implement V-Gas based on js-evm, a widely used Ethereum virtual machine written in Javascript, and conduct experiments on 736 real-world transactions recorded on Ethereum. A total of 44.02% of the transactions would have out-of-gas errors based on the estimation results given by solc, meaning that the recorded real gas consumption for those transactions is larger than the gas limit estimated by solc. In comparison, V-Gas could reduce the underestimation ratio to 13.86%. To evaluate the performance of feedback-directed engine in V-Gas, we implemented other directed fuzzing engines and compared their performance with that of V-Gas. The results showed that V-Gas generates the same or higher gas estimation value on 97.8% of the transactions with less time, usually within 5 minutes. Furthermore, V-Gas has exposed 25 previously unknown out-of-gas vulnerabilities in widely used smart contracts, 6 of which have been assigned unique CVE identifiers in the U.S. National Vulnerability Database.

The number of affirmed COVID-19 cases showed an enormous increase in the recent past throughout the globe. Keeping in view the catastrophic destruction of this devastating virus, there is a must-need situation to maximize the use of existing healthcare technologies such as the healthcare Internet of Things (H-IoT). In healthcare, patient wearable devices are widely recognized as a dormant technology with enormous capabilities to assess and combat various diseases, e.g., cough, seizure, temperature, heartbeat, and so on. As we know, in the H-IoT, patient-wearable devices are dispersed in an infrastructure-free environment that exposes them to several private and public coercion while accumulating and transmitting high sensitive data over the wireless communication channel. Therefore, security is the main concern of these applications, and thus, the primary focus of this article to outline the limitations and challenges in the present literature from 2019 to 2021, to identify the requirements of H-IoT applications used in the context of COVID-19. Following this, we will move one step ahead to explore the current security techniques adopted in these applications. Consequently, we will identify the network architectural, cryptographic, protocols, and operational security challenges during our study to recommend viable research directions and opportunities, which could be helpful and capable to minimize the network architecture, deployment, and maintenance cost with more productive outcomes.

Deep Learning (DL) is being applied in various domains, especially in safety-critical applications such as autonomous driving. Consequently, it is of great significance to ensure the robustness of these methods and thus counteract uncertain behaviors caused by adversarial attacks. In this paper, we use gradient heatmaps to analyze the response characteristics of the VGG-16 model when the input images are mixed with adversarial noise and statistically similar Gaussian random noise. In particular, we compare the network response layer by layer to determine where errors occurred. Several interesting findings are derived. First, compared to Gaussian random noise, intentionally generated adversarial noise causes severe behavior deviation by distracting the area of concentration in the networks. Second, in many cases, adversarial examples only need to compromise a few intermediate blocks to mislead the final decision. Third, our experiments revealed that specific blocks are more vulnerable and easier to exploit by adversarial examples. Finally, we demonstrate that the layers Block4_conv1 and Block5_ cov1 of the VGG-16 model are more susceptible to adversarial attacks. Our work could potentially provide useful insights into developing more reliable Deep Neural Network (DNN) models.

Despite the recent state-of-the-art performance of Deep Learning (DL), imbalanced graph-structured data remains an open challenge in social science, traffic networks, and biomedical informatics. Recently, a surge in research on Representation Learning has significantly improved the performance of DL algorithms on imbalanced non-graph-structured data. In addition, Graph Neural Networks (GNNs) already in widespread use for representing graph-structured data in DL models with more advanced techniques in neural message-passing and deep graph embedding. However, most existing works are based on assumptions that oversimplify the complexity of real-world problems. In this paper, we propose Reinforced Contrastive GNNs (RCGNN), a novel graph representation learning model for anomaly detection with multi-relational graph-structured data. The proposed model produces a neighbor selection with Reinforcement Learning (RL) based on the similarity of neighborhoods in multi-relational structured graphs. In addition, the graph representation is learned by an adaptive AutoEncoder (AE) with Triplet Loss (TL) in Contrastive Learning. By aggregating the nodes with the highest similarities in their features and the importance of each node, our model is able to construct the multi-relational graphs by keeping the complexity of the graph structure as well as the relation-dependency representations. Experiments on multiple benchmark data sets demonstrate the advantage of RCGNN in learning better representations for multi-relational graphs. Furthermore, compared to other GNN models, our model shows better performance in accuracy, F1, and PR AUC scores.

With the growing use of data intensive systems, more endpoints are available for threat actors to leverage vulnerabilities in order to gain access to remote systems and/or sensitive data. These endpoints can potentially accept data from a numerous amount of sources, opening a variety of attack vectors for threat actors to use. The use of files in varying formats and levels of quality present a challenge when attempting to effectively perform analysis of large structured data sets for indicators-of-compromise (IOC’s) in a timely manner. This paper proposes a solution to this issue through the use of a modular system which incorporates a client-server architecture to monitor specified files for IOC’s. The strategy incorporates a centralized application programming interface (API) that accepts files from a variety of general formats, normalization to a standard format, preprocessing, and automated analysis with the use of pre-trained machine learning models (ML), where each model is trained on a specific attack pattern.

The cognitive radio-enabled$massive$Internet of Things (CR-$m$IoT) is envisioned to shape the future of densely connected IoT devices in the sixth-generation networks to support the hyperconnected society. In conventional CR networks, secondary users (SUs) sense the whole block of spectrum to find idle channels, which is an energy-consuming, delay-inducing, and processing-intensive task. With the large scale of resource-constrained heterogeneous devices in CR-$m$IoT, the sensing process becomes a major hurdle for CR-$m$IoT devices to achieve efficient utilization of the limited device and network resources. Thus, a novel multiparameter-based flexible scheme is proposed for idle channel prediction and channel ranking, which considers priorities as well as heterogeneity of users. The scheme uses a probabilistic approach and employs multiple parameters simultaneously to evaluate the suitability of a channel before selecting it for transmission. In addition, valid channel obsolescence, a major problem inherent with channel prediction and ranking, is countered by the proposed scheme. The scheme is evaluated under the impact of variable primary and SUs’ arrivals and under multiple channel failures rates and variable sensing and frame time duration. The proposed scheme is also compared with its own modified version that disregards channel failures, and with the random channel selection approach followed by IEEE 802.22. The overall evaluation is conducted under realistic spectrum sensing. Simulation results show that for different parameter values, the proposed scheme improves the collision probability by 11%–55%, reduces sensing time and energy by 60% and 65%, respectively, and enhances throughput by 4%–70%, and spectrum utilization efficiency by 11%–40%.

Cyber-Physical Systems (CPS) connected in the form of Internet of Things (IoT) are vulnerable to various security threats, due to the infrastructure-less deployment of IoT devices. Device-to-Device (D2D) authentication of these networks ensures the integrity, authenticity, and confidentiality of information in the deployed area. The literature suggests different approaches to address security issues in CPS technologies. However, they are mostly based on centralized techniques or specific system deployments with higher cost of computation and communication. It is therefore necessary to develop an effective scheme that can resolve the security problems in CPS technologies of IoT devices. In this paper, a lightweight Hash-MAC-DSDV (Hash Media Access Control Destination Sequence Distance Vector) routing scheme is proposed to resolve authentication issues in CPS technologies, connected in the form of IoT networks. For this purpose, a CPS of IoT devices (multi-WSNs) is developed from the local-chain and public chain, respectively. The proposed scheme ensures D2D authentication by the Hash-MAC-DSDV mutual scheme, where the MAC addresses of individual devices are registered in the first phase and advertised in the network in the second phase. The proposed scheme allows legitimate devices to modify their routing table and unicast the one-way hash authentication mechanism to transfer their captured data from source towards the destination. Our evaluation results demonstrate that Hash-MAC-DSDV outweighs the existing schemes in terms of attack detection, energy consumption and communication metrics.

One of the operational challenges in the Internet of Things (IoT) is load balancing, which is the focus of interest of this article. We propose a three-phase enhancedad hocon-demand distance vector (enhanced-AODV) routing protocol for multiwireless sensor networks (multi-WSNs). The three phases are categorized based on traffic priority, namely: 1) high priority; 2) low priority; and 3) ordinary network traffic. The network architecture is divided into chains, i.e., local and public chains, where the cluster heads (CHs) and base stations (BSs) are used, respectively, to manage the network traffic based on priority information with alternative route allocation. Moreover, our three-phase enhanced-AODV protocol provides traffic categorization with alternatives route allocation to minimize energy consumption and prolong the lifetime of participating devices in the network. The proposed model is implemented in the simulation environment to overview results statistics in terms of network lifetime, prioritize traffic, computation and communication costs, latency, and packet lost ratio (PLR). Findings from the simulation suggest that our scheme achieves 15% improvement in network lifetime, 17% latency, 22% PLR, and approximately 10% in the computation and communication costs of the network, in comparison to three other similar protocols.

The Internet of Things (IoT) connects heterogeneous sensors with dynamic networks to monitor smart communication and collect real-time data. Such systems are well adapted to satisfy the needs of smart cities and facilitate remote locations. Many cloud-based solutions for effective routing along with scalable data storage have been presented for constraint IoT systems. However, because of the unpredictable nature of mobile networks and communication links, most of the solutions may not be suitable for realistic applications and usually result in path failure with increasing resource utilization. Hence, data forwarding is only reliable and valuable if the proposed algorithms are trust aware with low overheads and consume balanced energy among nodes. Therefore, this article proposed a fault-tolerant supervised routing (Trust-FTSR) model for trust management in the IoT network, to improve trustworthiness and collaborative communication in smart cities. Each node evaluates the behavior of its neighbors and establishes a direct trust for a reliable and optimized network structure. In addition, using a supervised machine-learning technique, a fault-tolerant relaying system is provided without imposing additional overheads. Moreover, it removes the additional load in determining the optimal decision and training the IoT system to balance the network cost. In the end, a secure algorithm is proposed to ensure the privacy and authentication of the relaying system in the presence of critical attacks with secured keys. The proposed model is tested and its performance has significant improvement as compared to existing work.

Big data analytics and mining have the potential to enable real-time decision making and control in a range of Internet of Things (IoT) application domains, such as the Internet of Vehicles, the Internet of Wings, and the Airport of Things. The prediction toward air mobility, which is essential to the studies of air traffic management, has been a challenging task due to the complex spatial and temporal dependencies in air traffic data with highly nonlinear and variational patterns. Existing works for air traffic prediction only focus on either modeling static traffic patterns of individual flight or temporal correlation, with no or limited addressing of the spatial impact, namely, the propagation of traffic perturbation among airports. In this article, we propose to leverage the concept of graph and model the airports as nodes with time-series features and conduct data mining on graph-structured data. To be specific, first, airline on-time performance (AOTP) data is preprocessed to generate a temporal graph data set, which includes three features: 1) the number; 2) average delay; and 3) average taxiing time of departure and arrival flights. Then, a spatial–temporal graph neural networks model is implemented to forecast the mobility level at each airport over time, where a combination of graph convolution and time-dimensional convolution is used to capture the spatial and temporal correlation simultaneously. Experiments on the data set demonstrate the advantage of the model on spatial–temporal air mobility prediction, together with the impact of different priors on adjacency matrices and the effectiveness of the temporal attention mechanism. Finally, we analyze the prediction performance and discuss the capability of our model. The prediction framework proposed in this work has the potential to be generalized to other spatial–temporal tasks in IoT.

The rapid development and deployment of Internet of Things (IoT) devices in modern networks and Industry 4.0 have attracted substantial interest from cybersecurity researchers. In this study, we propose a software-defined framework that improves network intrusion detection systems by using manufacturer usage description (MUD) to enhance the behavioral monitoring in IoT networks. We aim to explore whether Industrial IoT (IIoT) devices typically serve a common role in cyber–physical systems, and their communications exhibit predictable patterns that can be defined in MUD profile(s) formally and succinctly. We design a framework that utilizes the concept of digital twins and software-defined networking to improve the security of IIoT environments. The MUD data are profiled, and the actions are evaluated on the network digital twin before they are used in the physical network. The behavioral profiling system is updated in real time, thereby improving the overall system security and compliance to policies in the IoT deployment. Evaluation results show that our solution outperforms existing approaches substantially in terms of attack detection accuracy, predicting security incidents, response time, and resource usage.

Internet-of-Things (IoT) technology is increasingly prominent in the current stage of social development. All walks of life have begun to implement the IoT integration technology, so as to strive to promote industrial modernization, intelligence, and digitalization. In this case, how to link high-risk network activities with entities has become the primary issue for promoting industrial development. However, at this stage, the security issues in the development of the IoT technology have contradictions that are difficult to resolve. According to this situation, how to make system defense intelligent and replace manual monitoring has become the future of the development of security architecture. This article combines existing security research to explore the possibility of deep learning (DL) in upgrading the IoT security architecture, discusses how the IoT can identify and respond to cyber attacks, and how to encrypt edge data transmission. Moreover, this article discusses security research in application fields, such as Industrial IoT, Internet of Vehicles, smart grid, smart home, and smart medical. Then, we summarized the areas that can be improved in future technological development, including sharing computing power through the edge network processing unit (NPU) central device and closely combining the environmental simulation model with the actual environment, as well as malicious code detection, intrusion detection, production safety, vulnerability detection, fault diagnosis, and blockchain technology.

The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a variety of emerging services and applications. However, the presence of rogue IoT devices has exposed the IoT to untold risks with severe consequences. The first step in securing the IoT is detecting rogue IoT devices and identifying legitimate ones. Conventional approaches use cryptographic mechanisms to authenticate and verify legitimate devices’ identities. However, cryptographic protocols are not available in many systems. Meanwhile, these methods are less effective when legitimate devices can be exploited or encryption keys are disclosed. Therefore, noncryptographic IoT-device identification and rogue device detection become efficient solutions to secure existing systems and will provide additional protection to systems with cryptographic protocols. Noncryptographic approaches require more effort and are not yet adequately investigated. In this article, we provide a comprehensive survey on machine learning technologies for the identification of IoT devices along with the detection of compromised or falsified ones from the viewpoint of passive surveillance agents or network operators. We classify the IoT-device identification and detection into four categories: 1) device-specific pattern recognition; 2) deep learning-enabled device identification; 3) unsupervised device identification; and 4) abnormal device detection. Meanwhile, we discuss various ML-related enabling technologies for this purpose. These enabling technologies include learning algorithms, feature engineering on network traffic traces and wireless signals, incremental learning, and abnormality detection.

Abnormal event detection with the lowest latency is an indispensable function for safety-critical systems, such as cyber defense systems. However, as systems become increasingly complicated, conventional sequential event detection methods become less effective, especially when we need to define indicator metrics from complicated data manually. Although deep neural networks (DNNs) have been used to handle heterogeneous data, the theoretic assurability and explainability are still insufficient. This article provides a holistic framework for the quickest and sequential detection of abnormalities and time-dependent abnormal events. We explore the latent space characteristics of zero-bias neural networks considering the classification boundaries and abnormalities. We then provide a novel method to convert zero-bias DNN classifiers into performance-assured binary abnormality detectors. Finally, we provide a sequential quickest detection (QD) scheme that provides the theoretically assured lowest abnormal event detection delay under false alarm constraints using the converted abnormality detector. We verify the effectiveness of the framework using real massive signal records in aviation communication systems and simulation. Codes and data are available athttps://github.com/pcwhy/AbnormalityDetectionInZbDNN.

The development of the Industrial Internet of Things (IIoT) and digital twins (DTs) technology brings new opportunities and challenges to all walks of life. The work aims to study the cross-layer optimization of DTs in IIoT. The specific application scenarios of hazardous gas leakage boundary tracking in the industry is explored. The work proposes an industrial hazardous gas tracking algorithm based on a parallel optimization framework, establishes a three-layer network of distributed edge computing based on IIoT, and develops a two-stage industrial hazardous gas tracking algorithm based on a state transition model. The performance of different algorithms is analyzed. The results indicate that the tracking state transition and target wake-up module can effectively track the gas boundary and reduce the network energy consumption. The task success rate of the parallel optimization algorithm exceeds 0.9 in 5 s. When the number of network nodes in the state transition algorithm is N = 600, the energy consumption is only 2.11 J. The minimum tracking error is 0.31, which is at least 1.33 lower than that of the exact conditional tracking algorithm. Therefore, the three-layer network edge computing architecture proposed here has an excellent performance in industrial gas diffusion boundary tracking.

In location-based social networks (LBSNs), extensive user check-in data incorporating user preferences for location is collected through Internet of Things devices, including cell phones and other sensing devices. However, directly acquiring the preferences of spars users remains an open challenge. This article offers a point-of-interest (POI) category recommendation model based on group preferences (PPCM). This model is proposed for three reasons: 1) because data influence the training of a deep learning model, the group influence of users is taken into account. To protect the privacy of users’ check-in records and classify similar users into the same group, locality-sensitive hashing (LSH) is used; 2) a successive POI category recommendation model should capture the long- and short-term dependence ability. The attention mechanism and a temporal sliding window are paired with the long short-term memory (LSTM). This paradigm is useful for efficiently mining users’ long-term dependencies and interests; and 3) although the overall users’ check-in data are vast, check-in location options are also massive for a single user. There is a scarcity of data that may be utilized to mine user interests. Thus, instead of using POI, we leverage the POI category to better mine the user’s interests. On real check-in data sets from New York City and Tokyo, the PPCM is compared to other models. The comparison results indicate that the PPCM has improved recommendation performance.

The path planning of the autonomous underwater vehicle (AUV) has shown great potential in various Internet of Underwater Things (IoUT) applications. Although considerable efforts had been made, prior studies are confronted with some limitations. For one thing, existing work only uses the ocean current simulation model without introducing real ocean information, having not been supported by real data. For another, traditional path planning algorithms have strong environment dependence and lack flexibility: once the environment changes, they need to be remodeled and replanned. To overcome these challenges, this article proposes comprehensive ocean information D3QN (COID), an AUV path planning scheme exploiting comprehensive ocean information and reinforcement learning (RL), which consists of three steps. First, we introduce the comprehensive real ocean data, including weather, temperature, thermohaline, current, etc., and apply them into the regional ocean modeling system to generated reliable ocean current. Next, through well-designed state transition function and reward function, we build a 3-D grid model of ocean environment for RL. Furthermore, based on the framework of the double dueling deep$Q$network (D3QN), COID integrates local ocean current and position features to provide state input and uses priority sampling to accelerate network convergence. The performance of COID has been evaluated and proved by numerical results, which demonstrate efficient path planning and high flexibility for expansion into different ocean environments.

Automatic path planning problem is essential for efficient mission execution by unmanned aerial vehicles (UAVs), which needs to access the optimal path rapidly in the complicated field. To address this problem, a novel hybrid particle swarm optimization (PSO) algorithm, namely, SDPSO, is proposed in this article. The proposed algorithm improves the update strategy of the global optimal solution in the PSO algorithm by merging the simulated annealing algorithm, which enhances the optimization ability and avoids falling into local convergence; each particle integrates the beneficial information of the optimal solution according to the dimensional learning strategy, which reduces the phenomenon of particles oscillation during the evolution process and increases the convergence speed of the SDPSO algorithm. The simulation results show that compared with PSO, dynamic-group-based cooperative optimization (DGBCO), gray wolf optimizer (GWO), RPSO, and two-swarm learning PSO (TSLPSO), the SDPSO algorithm can quickly plan higher quality paths for UAVs and has better robustness in complex 3-D environments.

With the widespread application of the Industrial Internet of Things (IIoT), industrial control systems (ICSs) greatly improve industrial productivity, efficiency, and product quality. However, IIoT protocols as the bridge of different parts of ICSs are vulnerable to be attacked due to their vulnerabilities. To reduce cyberattack threats, we need to find the vulnerabilities of IIoT protocols by using efficient vulnerability mining methods, such as fuzzing. Fuzzing is often used to mine vulnerabilities for IIoT protocols. However, the traditional fuzzing methods for IIoT protocols have a low passing rate and low code coverage. To solve these problems, we propose a generative adversarial network (GAN), here referred to as coverage-guided GANs (CovGAN), which aims to generate test cases with a high passing rate and code coverage by learning IIoT protocol specifications. Based on the CovGAN, we construct a fuzzing framework (CGFuzzer) for IIoT protocols. Finally, we design a protocol simulator to verify the CovGAN performance. Experimental results show that the proposed methodology outperforms approximately 5%, 7%, and 39% of the passing rate of GANFuzz, SeqFuzzer, and Peach, respectively. In addition, CGFuzzer has a significant improvement in code coverage, which is about 17%, 24%, and 31% higher than GANFuzz, SeqFuzzer, and Peach, respectively.

The Internet of Things advancements has enabled smart city scenarios worldwide. A tight nit communication, on the one hand, is required for device management and monitoring, but on the other hand, it has raised risks in information exchange in cross-domain scenarios. Keeping in view the above issues, this article considered well-known cross-domain access control protocols, i.e., Shibboleth, xDAuth, and OAuth to ensure user’s security. And, the existing work focused specifically on their implementations without verifying if they are effective for the security scenarios. We aim to verify their claim of fulfilling the claimed security requirements or not. We did a Cyber Attack Analysis to highlight the possible security attacks in smart city scenarios. Then, the protocols are verified against those issues to critically analyze if they are the best fit for the said scenario. The Z3-solver and Satisfiability Modulo Theories Library have been in use for protocol verification purposes. Results reveal that the protocol models are functioning correctly in terms of confidentiality, availability, and integrity. Verifying these protocols in terms of other security properties is part of our future work.

Compared with traditional images, multispectral images (MSIs) contain more spectral bands and higher data dimensions. The existing MSI classification model has high computational complexity and consumes a lot of computing resources. In this letter, we propose a lightweight multispectral classification method named CABNN based on binary neural networks (BNNs) to effectively have a trade-off between model performance and computational cost. First, we modify and binarize the MobileNetV1 network and add almost computation-free shortcuts to enhance the expressive capability. Secondly, since the BNN is sensitive to the distribution of activation functions, we introduce RPReLU with learnable coefficients to automatically adjust activation distribution at almost no extra cost. Lastly, considering that MSIs have multiple channels, we utilize an efficient channel attention (ECA) module to assign different weights to each channel to concentrate on crucial features and suppress insignificant features. We conduct experiments on four public MSI datasets, including NaSC-TG2, EuroSAT, GID Fine land-cover classification, and UC Merced Land Use. Extensive experiments demonstrate that the proposed CABNN has higher efficiency and better comprehensive performance than the state-of-the-art methods across the board.

Hyperspectral image (HSI) registers hundreds of spectral bands, whose intraclass variability and interclass similarity are resourceful information to be mined. Intraclass variability reflects the nonuniform and redundancy of the spatial and semantic features extracted from HSI. Interclass similarity represents the inherent relationship between adjacent features and snapshots. Existing models extract the superficial correlation representation for HSI to tackle the classification task but fail to embed the interclass and intraclass correlations due to these models’ intrinsic bottlenecks. Confronting the challenges of capturing interrelation for complex data in practice, we propose a Context-Aware Attentional Graph U-Net (CAGU) to improve these two modes of representation, which is more flexible in feature enhancement. In this method, attentional Graph U-Net is capable of extracting the intraclass embeddings within a non-Euclidean space by combining similar distributing feature vertices. The gated recurrent unit (GRU) is another critical component of our model to capture the context-aware dynamic interclass embeddings. Extensive experiments demonstrate that our model can efficiently outperform state-of-the-art methods across-the-board on five wide-adopted public data sets, namely, Pavia University, Indian Pines, Salinas Scene-show, Houston 2013, and Houston 2018, on par with the same scale of model parameters.

Hyperspectral images (HSIs) have significant applications in various domains, since they register numerous semantic and spatial information in the spectral band with spatial variability of spectral signatures. Two critical challenges in identifying pixels of the HSI are, respectively, representing the correlated information among the local and global, as well as the abundant parameters of the model. To tackle this challenge, we propose a multi-scale U-shape multi-layer perceptron (MUMLP) a model consisting of the designed multi-scale channel (MSC) block and the U-shape multi-layer perceptron (UMLP) structure. MSC transforms the channel dimension and mixes spectral band feature to embed the deep-level representation adequately. UMLP is designed by the encoder–decoder structure with multi-layer perceptron layers, which is capable of compressing the large-scale parameters. Extensive experiments are conducted to demonstrate that our model can outperform state-of-the-art methods across the board on three wide-adopted public datasets, namely Pavia University (PaviaU), Houston 2013, and Houston 2018.

With Cloud/Fog Computing being a paradigm combination of IoT context and Edge Computing extended with Cloud/Fog, business process in it involves dataflows among multi-layers and multi-nodes, possibly provided by multi-organizations. Achieving end-to-end trustworthiness over the whole dataflow in such a Cloud/Fog Computing context is a challenging issue, nonetheless a necessary pre-condition for a successful business process on intra-/inter- organizational level. This paper investigates technical conundrums related to this target and proposes a policy-based approach for trustworthiness governance. An architectural layout is proposed with according modules, by carrying out two methodologies. One resides in tracing data derivation and maintaining security-level over the whole dataflow, handling data aggregation with several protocols. The other is to express data owner trustworthiness requirements with an enhanced attribute-based access control policy model and to evaluate data accessing nodes’ trustworthiness-related properties. Experiments show that processing time per attribute pair drops as the scales of policies increase, suggesting good scaling property of the system.

Rumors can spread very rapidly through online social networks (OSNs), leading to huge negative impact on human society. Hence, there is an urgent need to develop models that can minimize the spread of rumors. In this article, we propose a novel framework to improve the cost and efficiency of rumor propagation control. First, to reduce the impact of rumor controlling mechanism on users’ normal activities, we introduce a soft dynamic quarantine strategy into rumor propagation control and develop a new propagation model named susceptible-infected-removed-quarantined ignorants-quarantined spreaders (SIRQU) to model and block the rumor propagation in the network. Second, to further improve the control efficiency, we propose an influential node selection algorithm based on discrete particle swarm optimization with an evolutionary search strategy, and the controlling mechanism is only applied on the most influential nodes. Finally, we conduct a series of simulations and experiments on several public datasets and the dataset collected from Sina Weibo to validate the proposed method, and the results show that the proposed method outperfoms the related baseline algorithms.

This article proposes a scheme for multifactor authentication based on electroencephalography (EEG) signal analysis. A solution for EEG signal acquisition and recording of acquisition results has been implemented, a machine learning model has been developed and trained, then a classifier that determines the user's login procedure familiarity has been built, and a solution to carry out the described experiments has been implemented, in the form of a mobile application. Besides, a multifactor authentication system, based on the EEG signal combined with user image verification, using the brain–computer interface system with a single EEG electrode based on the NeuroSky MindWave device was proposed. Based on the defined scenarios, experiments were conducted, followed by a survey on the research group, and the obtained results were analyzed. In the case of an experiment related to login simulation, a high classification accuracy rate was obtained, both for the classifier itself (83.33%) and the proposed user authentication system (77.78%). Analyzing the results of the EEG signal recording used in the classification, it seems that the proposed solution is promising not only due to the high accuracy and a low false rejections rate but also through confirmed associations in the analysis of brain wave signal, corresponding to the results of research in the literature. A proposed multi-factor authentication system based on image selection and EEG analysis can be implemented in many areas as a modern solution in securing IT systems.

In this article, we present a robust channel categorization scheme to fix data privacy and preservation problems in an Industrial Healthcare Internet of Things (IHC-IoT) network. The proposed model categorizes the transmission bandwidth into four independent channels for each device by defining triggering rules with respect to time for reception and transmission of data. Besides, our developed prototype, which is known as high optimal path channel triggering protocol (HOPCTP) ensures data privacy and preservation utilizing minimal network resources in an IHC-IoT network. Furthermore, the HOPCTP prototype enables client-side devices to transmit and receive data with four different independent communication channels following the triggering mechanism to avoid adversary device anticipation in the network. The categorized channels are triggered with a defined time period to change their transmission and reception functionality, which triggers the transmitted data between different channels. Same data transmission via four different channels ensures the confidentiality and integrity of data because if an attacker captures one channel of data, he will not be able to understand the full message. The convalescent communication infrastructure is developed among patient wearable devices followed by cluster heads, micro base station, and macro base station to ensure data privacy and preservation with better communication metrics. In addition, the objective of the HOPCTP prototype is to resolve the data privacy issues in delay-sensitive applications, i.e., IHC-IoT networks. To achieve this, the HOPCTP prototype promotes data preservation and communication in terms of authenticity, congestion, throughput, communication cost, and packet loss ratio. The result statistics of the proposed scheme demonstrate remarkable improvement over the existing schemes for aforementioned comparative metrics.

Augmetned reality is a key technology that will facilitate a major paradigm shift in the way users interact with data and has only just recently been recognized as a viable solution for solving many critical needs. Enter augmented reality (AR) technology, which can be used to visualize data from hundreds of sensors simultaneously, overlaying relevant and actionable information over your environment through a headset. With the rapid development of 5G, which makes AR technology much faster, with much more data flow. With easier and more accessible use, for a variety of different functions (besides video gaming), widespread adoption seems likely.

The papers in this special section focus on artificial intelligence-drive applications for synthetic biology that promote human well being. Synthetic biology is an important branch of biological science, which is different and even completely opposite from traditional research direction on biology. It is obvious that synthetic biology will promote the next biotechnology revolution. At present, related researches are not limited in the painstaking splicing of genes, but have begun to construct genetic codes in order to construct new organisms using synthetic genetic factors. Specially, it is estimated that synthetic biology will have excellent application prospects in many fields, including the production of more effective vaccines, new drugs, biology based manufacturing, the production of sustainable energy, the biological treatment of environmental pollution, and biosensors that can detect toxic chemicals. In this way, synthetic biology is expected to make rapid progress in the next few years.

Visual classification algorithms based-on Deep Neural Networks (DNN) have been widely adopted in autonomous vehicle design. However, DNN suffers from adversarial attacks including pixel attacks and patch attacks, and its adoption may introduce new vulnerability into such security-critical scenarios. Existing defense techniques only focus on defending against one category, either pixel attacks or patch attacks, but does not translate to the other. Hence, the design of a practical comprehensive real-time defense algorithm for DNN-based classifiers presents a challenging task in this adversarial context. This paper attempts to address the abovementioned problem by combining Compressive Sensing with Generative neural networks (CSG) to construct an efficient defense framework, in conjunction with the proposal of a classifier-aware adversarial training way. Extensive experiments have been conducted using the LISA road sign dataset to evaluate the performance of CSG. The results show its superiority in comprehensively defending adversarial examples generated using attacks including CW-L2, FGSM and Sticker, compared with other state-of-the-art defense techniques.

Considering the high mobility and uneven distribution of vehicles, an efficient routing protocol should avoid that the sent packets are forwarded within road segments with ultra-low density or serious data congestion in vehicular networks. To this end, in this paper, we propose a Traffic aware and Link Quality sensitive Routing Protocol (TLRP) for urban Internet of Vehicles (IoV). First, we design a novel routing metric, i.e., Link Transmission Quality (LTQ), to account for the impact of the number, quality and relative positions of communication links along a routing path on the network performance. Then, to adapt to the dynamic characteristics of IoV, a road weight evaluation scheme is presented to assess each road segment using the real-time traffic and link information quantified by the LTQ. Next, the path with the lowest aggregated weight is selected as the routing candidate. Extensive simulations demonstrate that our proposed protocol achieves significant performance improvements compared to the state-of-the-art protocol MM-GPSR, the typical junction-based scheme E-GyTAR, and the classic connectivity-based routing iCAR, in terms of packet delivery ratio and average transmission delay.

Traffic flow prediction plays a critical role in reducing traffic congestion in transportation systems. However, accurate traffic flow prediction becomes challenging due to the impact of complex spatio-temporal (ST) correlations and the diversity of ST correlations. When modeling complicated ST correlations, researchers usu did not take the diversity of ST correlations into consideration, resulting in poor prediction accuracy. In this paper, we propose ST-InNet, a deep spatio-temporal Inception network for collectively predicting traffic flow in each city region. Specifically, ST-InNet employs two Inception networks to simultaneously capture various spatial and temporal correlations of traffic data, including temporal closeness, temporal periodicity, nearby spatial dependencies, and distant spatial dependencies. For the diversity of spatial correlations, ST-InNet presents an improved variant of an Inception module to explicitly capture the different contributions of spatial correlations for each region. For the diversity of temporal correlations, ST-InNet designs a fusion component to explicitly model the varying contributions of temporal correlations on prediction. The experiments are conducted on a real-world traffic dataset in Nanjing, demonstrating that ST-InNet outperforms five state-of-the-art baselines in short-term and long-term traffic flow predictions with an average accuracy improvement of 32.09% and 30.97%, respectively.

In the vehicular ad hoc networks (VANETs), minimizing the broadcast storm that arises due to message rebroadcast during emergency message dissemination in extremely mobile environments under sparse or dense networks is a significant challenge. Proper selection of rebroadcasting vehicles guarantees acceptable end-to-end delay, high delivery ratio, and efficient bandwidth utilization. To date, many protocols have been proposed to select an appropriate rebroadcasting vehicles based on vehicle position information only. However, such approaches neglect the fact that both vehicle velocity and position information can be utilized efficiently to alleviate rebroadcast message collisions and control bandwidth consumption. In this work, we present a new broadcast suppression protocol, named, velocity and position-based broadcast suppression for VANETs (VP-CAST), which can work in both sparse and dense network situations. VP-CAST does rely on periodic beacon messages, rather the position and velocity information of broadcasting vehicle are included in a broadcast message. Moreover, the transmission range of broadcasting vehicle is divided into dynamic time slots based on velocity and position information of broadcasting and receiving vehicles.The proposed scheme assigns shorter and dynamic waiting time to the vehicles moving at high velocities and located farther from the sender vehicle that eventually reduces both the message re-transmission delay and the number of rebroadcasting vehicles. The proposed protocol is compared with the DV-CAST in terms of end-to-end delay, message delivery ratio, and message overhead.

In recent years, vehicular named data networking (VNDN) has quickly ascended to the spotlight and gained enormous popularity, which has emerged as a candidate to support various applications of vehicular communications. VNDN has the potential improve the data dissemination efficiency by mitigating the performance degradation from Internet Protocol (IP) addressing, unstable connectivity and diversified service requirements. With the number of connected vehicles increasing rapidly, the traffic burden of the base station (BS) also grows. As an effective edge computing paradigm, in-vehicle caching can significantly relieve the pressure of the BS. However, the design of a fair caching strategy is still challenging due to the selfish nature of individuals. In this paper, to address the above issues, a popularity-incentive caching scheme (PICS) is proposed in VNDN, where the BS will reward vehicles who execute cache offloading and content sharing with others. To balance the conflict of interest between the BS and vehicles, a Stackelberg game is modeled with rational utilities envisioned. Next, we propose the solution of this game model and evaluate the influence of different weight parameters. Finally, simulation results validate the effectiveness of PICS.

The ubiquitous deployment of 5G New Radio (5G NR) stimulates Unmanned Aircraft Systems (UAS) swarm networking to evolve to achieve more imminent progress. The heterogeneous collaboration between UAS swarm enhances the complexity and the efficiency of mission complement that requires robustness, flexibility, and sustainability of throughput in UAS swarm networking. The conventional approaches mainly are based on the hierarchical architectures that are limited to satisfy the challenges of UAS swarm with high dynamics on a large scale. In this paper, we propose an optimal cell wall paradigm to enhance the throughput in heterogeneous UAS swarm networking. With the weight adjustment of each link, we map the optimization into a polyhedron scheduling problem and formula the problem into Max-min Throughput Fair Scheduling (MTFS). Further, we propose a max-min throughput algorithm to optimize the minimum throughput of cell wall paradigm. With the optimal max-min throughput, we optimize the schedule with edge-coloring to achieve global MTFS solving. The normalized MTFS shows our algorithm can achieve over 40% improvement of MTFS globally. In terms of MTFS solving, our algorithms have promising potential to improve the throughput and mitigate the incidents for multiple beams enabling of UAS in cell wall communication. With the throughput enhancement, the advanced aerial mobility of UAS swarm networking can be escalated on a large scale.

The rapid advancement of intelligent vehicles is deemed crucial to the emergence of diverse compute-intensive applications of assisted driving, which consist of automatic driving, speed recognition, hybrid sensing data fusion, etc. Nevertheless, resources-constraint vehicles with high mobility cannot always meet the computing and communication demands when the above applications occur. Additionally, considering the expensive and inflexible deployment of edge servers, offloading application tasks to “Edge” in the vehicular networks is not always working well. To effectively mitigate the above issues, the complicated application tasks are motivated to offload to the vehicle platoon, where the vehicles travel synchronously in a string with small headway. Benefiting from the stable connectivity, adjustable mobility, and reasonable charge, the task vehicle would like to process the task by leveraging the idle resources of each platoon member (PM). To make more effective use of the resources on the mobile edge platoon cloud (MEPC), we investigate the resource allocation strategy based on the task vehicle’s service pricing strategy in this work. We first formulate the interactions between MEPC and task vehicle as a Stackelberg game to study the joint utility maximization of the MEPC and task vehicle. Then the Stackelberg Equilibrium (SE) for the proposed game is characterized and proved. The proposed algorithm Hook-Jeeves-based Stackelberg game (HJSG) can reach the SE. Finally, we introduce the consortium blockchain to ensure the security and privacy of service transactions. The entire system helps enhance task processing efficiency, protect transaction data, and improve service experience. Experimental results over numerical simulation based on practical scenarios demonstrate that compared with Multi-round Stackelberg Game (MRSG), uniform pricing, and the local computation strategy, the proposed HJSG algorithm can attain less execution time and faster convergence performance.

Mobile edge computing (MEC) provides a novel computing paradigm to satisfy the increasing computation requirements of mobile applications. In MEC-enabled intelligent transportation systems (ITS), the latency-sensitive computing tasks are offloaded to RSUs for execution, reducing the transmission latency compared with the cloud solutions. However, the repetitive executions of the same tasks whose outputs are dependent on the inputs lead to the extra system latency, an alternative is to cache the required services on RSUs in advance. The service requirements of latency-sensitive computing tasks are satisfied by jointly considering computation offloading and service caching. Besides, the digital twin (DT) is utilized to construct the virtual world reflecting the physical world in real-time to efficiently make offloading strategies. In this paper, a computation offloading and service caching method using decision theory in ITS with DT, named CODT, is proposed. Specifically, the computation offloading and service caching in ITS is modeled first with DT. Then, a mixed-integer nonlinear programming (MINLP) problem is formulated to minimize the system latency. Afterward, the decision theory is used to analyze the utilities of offloading strategies in different states of RSUs and make the optimal strategy. Finally, extensive simulations based on the real-world datasets demonstrate that the proposed CODT outperforms other baselines.

Cognitive radio networks (CRNs) can facilitate ultra-reliable communication among IoT devices in the 6G environment by enhancing channel availability (CA) for primary and secondary users. However, CA does not necessarily lead to successful connection establishment unless receiver’s accessibility (RA) is guaranteed. This motivates us to propose the notion of connection availability (CoA) that incorporates RA into CA. We also introduce the idea of service maintainability (SM) that includes the effect of RA in service retainability. Additionally, spectrum utilization efficiency (SUE) is expressed and analyzed with and without considering the impact of RA. For performance evaluation, a channel reservation algorithm with customizable configurations is proposed. Furthermore, an analytical model is used to investigate the network performance for all key performance indicators (KPIs) under multiple channel failures and PU arrival rates and determine valuable tradeoffs among KPIs.

Resource allocation and offloading in green Internet of Things (IoT) relies on the multi-level heterogeneous platforms. The energy expenses of the platform determine the reliability of green IoT based services and applications. This manuscript introduces a decisive energy management scheme for optimal resource allocation and offloading along with energy constraints. This scheme handles both the allocation and energy-cost in a balanced manner through deterministic task offloading. In particular, resource allocation solution for non-delay tolerant green IoT applications is focused by confining the failures of discrete tasks through neural learning. The dropout process augmented with the learning process improves the feasible conditions for resource handling and task offloading among the active IoT service providers. Through extensive simulations the performance of the proposed scheme is analyzed and energy consumption, failure rate, processing, and completion time metrics are used for a comparative study. Further, the optimal utilization and on-demand dissipation of such stored resources help to improve the sustainability of green power and communication technologies in the smart city environment.

Radio frequency (RF) signal classification has significantly been used for detecting and identifying the features of unknown unmanned aerial vehicles (UAVs). This paper proposes a method using empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) on extracting the communication channel characteristics of intruding UAVs. The decomposed intrinsic mode functions (IMFs) except noise components are selected for RF signal pattern recognition based on machine learning (ML). The classification results show that the denoising effects introduced by EMD and EEMD could both fit in improving the detection accuracy with different features of RF communication channel, especially on identifying time-varying RF signal sources.

The ubiquitous of 5G New Radio (5G NR) accelerates the massive implementations in many fields including swarm Unmanned Aircraft System (UAS) networking. The ultra capacities of 5G NR can provide more sufficient networking services for the swarm UAS networking which can enable swarm UAS to deploy in more complex and challenging scenarios to achieve missions. However, the conventional swarm UAS networking are mainly centralized or hierarchical which is vulnerable to the dynamics and the deployment of swarm UAS networking on a large scale. In this paper, we formulate a cell wall communications for the heterogeneous swarm UAS networking with the inspiration of biological cell wall communication. Fueled by reinforcement learning, we resolve the edge-coloring problem of cell wall communication scheduling to achieve the maximum throughput between the heterogeneous swarm UAS networking globally. The evaluation shows our proposed reinforcement learning enabled algorithm can surpass the conventional scheduling algorithms over 90% when the time piece is less than 0.01s and achieve the optimal throughput for the heterogeneous swarm UAS networking.

The Internet of Things (IoT) is reshaping modern society by allowing a decent number of RF devices to connect and share information through RF channels. However, such an open nature also brings obstacles to surveillance. For alleviation, a surveillance oracle, or a cognitive communication entity needs to identify and confirm the appearance of known or unknown signal sources in real-time. In this paper, we provide a deep learning framework for RF signal surveillance. Specifically, we jointly integrate the Deep Neural Networks (DNNs) and Quickest Detection (QD) to form a sequential signal surveillance scheme. We first analyze the latent space characteristic of neural network classification models, and then we leverage the response characteristics of DNN classifiers and propose a novel method to transform existing DNN classifiers into performance-assured binary abnormality detectors. In this way, we seamless integrate the DNNs with parametric quickest detection. Finally, we propose an enhanced Elastic Weight Consolidation (EWC) algorithm with better numerical stability for DNNs in signal surveillance system to evolve incrementally, we demonstrate that the zero-bias DNN is superior than regular DNN models considering incremental learning and decision fairness. We evaluated the proposed framework using real signal datasets and we believe this framework is helpful in developing a trustworthy IoT ecosystem.

With the development of unmanned aerial systems (UAS), the ubiquitous deployment of UAS is becoming a trend. With the digital transceiver, the remote pilot can control the UAS remotely and effectively. With the deployment of 5G technology on a large scale, the convenience of remote control is becoming obvious and stable. However, the convenience of remote control technologies also brings more vulnerabilities to UAS. Software defined radio (SDR) has been explored to play system exploitation and penetration test for the radio system widely. With block programming, an SDR can become a hands-on system exploitation tool for research. With the adjustment of antennas and programmings, we can exploit the vulnerabilities of the UAS system and fixed the problem in advance. In this paper, we introduce an approach to leverage SDR to realize signal spoofing for GPS location and injection for digital communication. Based on SDR, we analyze the security of UAS on the GPS and digital connections. With the adjustment of antennas, we can define the SDR into a GPS spoofing tool and inject the packets in the communication of the digital transceiver. The evaluation shows the approach can delay the GPS searching for tens of minutes and disorder the connections between ground control station to UAS.

With massive data being generated daily and the ever-increasing interconnectivity of the world’s Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and national security. In this paper, we perform a comprehensive study on NSL-KDD, a network traffic dataset, by visualizing patterns and employing different learning-based models to detect cyber attacks. Unlike previous shallow learning and deep learning models that use the single learning model approach for intrusion detection, we adopt a hierarchy strategy, in which the intrusion and normal behavior are classified firstly, and then the specific types of attacks are classified. We demonstrate the advantage of the unsupervised representation learning model in binary intrusion detection tasks. Besides, we alleviate the data imbalance problem with SVM-SMOTE oversampling technique in 4-class classification and further demonstrate the effectiveness and the drawback of the oversampling mechanism with a deep neural network as a base model.

Anomaly detection has been a challenging task given high-dimensional multivariate time series data generated by networked sensors and actuators in Cyber-Physical Systems (CPS). Besides the highly nonlinear, complex, and dynamic nature of such time series, the lack of labeled data impedes data exploitation in a supervised manner and thus prevents an accurate detection of abnormal phenomenons. On the other hand, the collected data at the edge of the network is often privacy sensitive and large in quantity, which may hinder the centralized training at the main server. To tackle these issues, we propose an unsupervised time series anomaly detection framework in a federated fashion to continuously monitor the behaviors of interconnected devices within a network and alert for abnormal incidents so that countermeasures can be taken before undesired consequences occur. To be specific, we leave the training data distributed at the edge to learn a shared Variational Autoencoder (VAE) based on Convolutional Gated Recurrent Unit (ConvGRU) model, which jointly captures feature and temporal dependencies in the multivariate time series data for representation learning and downstream anomaly detection tasks. Experiments on three real-world networked sensor datasets illustrate the advantage of our approach over other state-of-the-art models. We also conduct extensive experiments to demonstrate the effectiveness of our detection framework under non-federated and federated settings in terms of overall performance and detection latency.

The LEO satellite network (LSN) is envisioned to be highly advanced and ubiquitous, as a function complement and enhancement of ground networks. The satellite networking enables low-latency and high-speed data transmission over long distances for global users, especially in remote areas. Since the nature of time-variability, it is not easy to arrange the satellite networking scheme for tasks at each time slot. Specifically, the main problem is how to ensure that the networking scheme always follows the maximum network transmission capacity during the task duration. To address the problem, this paper first constructs a time-varying LSN model to describe the network characteristics. The networking problem is formulated as the maximum network transmission capacity (NTC) problem at each time slot. Next, a two-stage contact optimization scheme is given. The transmission-based depth first search (TDFS) algorithm is first proposed to calculate the optimal networking for each specified time slot. Then a network performance graph (NPG) is constructed to show the NTC performances of different time slot combinations. The dynamic programming is utilized on NPG to find the optimal time slot sequence. Simulation results show that the proposed contact management with dynamic programming (CMDP) scheme achieves better network throughput and service continuity for LSN.

With the ubiquitous deployment of 5G cellular networking in many fields, unmanned aerial vehicle (UAV) networking, as one of the main parts of the Internet of Things (IoT), is playing a pivot role in the extension of smart cities. Different from the conventional approaches, the 5G enabled UAV networking can be more capable of multiple and complex mission executions with high requirements of collaborations and incorporation. In this paper, we leverage reinforcement learning based scheduling to optimize the throughput of heterogeneous UAV networking. To improve the throughput of the heterogeneous UAV networking, we focus on the balance for the inter-and intra-networking with the reduction of collisions occurring in the time slots. With reinforcement learning enabled scheduling, we can achieve the optimum selections on link activation and time allocation. Compared with the edge coloring of Karloff, our approach can achieve a higher enhancement on the throughput. The experimental results show that our approach reaches the global optimization when tsand tgare less than 0.01. Generally, DQN achieves 57.58% improvement on average which exceeds Karloff. The proposed approach can improve the throughput of heterogeneous UAV networking significantly.

In Industrial-Internet-of-Things (IIoT) applications, fog computing (FC) has soared as a means to improve the Quality of Services (QoSs) provided to users through cloud computing, which has become overwhelmed by the massive flow of data. Transmitting all these amounts of data to the cloud and coming back with a response can cause high latency and requires high network bandwidth. The availability of sustainable energy sources for FC servers is one of the difficulties that the service providers can face in IIoT applications. The most important factor contributing to energy consumption on fog servers is task scheduling. In this article, we suggest an energy-aware metaheuristic algorithm based on a Harris Hawks optimization algorithm based on a local search strategy (HHOLS) for task scheduling in FC (TSFC) to improve the QoSs provided to the users in IIoT applications. First, we describe the high virtualized layered FC model taking into account its heterogeneous architecture. The normalization and scaling phase aids the standard Harris hawks algorithm to solve the TSFC, which is discrete. Moreover, the swap mutation ameliorates the quality of the solutions due to its ability to balance the workloads among all virtual machines. For further improvements, a local search strategy is integrated with HHOLS. We compare HHOLS with other metaheuristics using various performance metrics, such as energy consumption, makespan, cost, flow time, and emission rate of carbon dioxide. The proposed algorithm gives superior results in comparison with other algorithms.

Human activity recognition (HAR) has been regarded as an indispensable part of many smart home systems and smart healthcare applications. Specifically, HAR is of great importance in the Internet of Healthcare Things (IoHT), owing to the rapid proliferation of Internet of Things (IoT) technologies embedded in various smart appliances and wearable devices (such as smartphones and smartwatches) that have a pervasive impact on an individual's life. The inertial sensors of smartphones generate massive amounts of multidimensional time-series data, which can be exploited effectively for HAR purposes. Unlike traditional approaches, deep learning techniques are the most suitable choice for such multivariate streams. In this study, we introduce a supervised dual-channel model that comprises long short-term memory (LSTM), followed by an attention mechanism for the temporal fusion of inertial sensor data concurrent with a convolutional residual network for the spatial fusion of sensor data. We also introduce an adaptive channel-squeezing operation to fine-tune convolutional a neural network feature extraction capability by exploiting multichannel dependency. Finally, two widely available and public HAR data sets are used in experiments to evaluate the performance of our model. The results demonstrate that our proposed approach can overcome state-of-the-art methods.

Fog computing (FC) has emerged as a complementary solution to the centralized cloud infrastructure. An FC node is available in closer proximity to users and extends cloud services to the edge of the network in a highly distributed manner. However, with an increase in streaming and delay-sensitive Internet-of-Things (IoT) applications, FC also needs to address the issue of higher latency while forwarding compute-intensive jobs to remote cloud data centers. Hence, there is a need to investigate the use of computational resources at the edge of the network. Volunteer computing (VC) offers a reduction in the cost of maintaining high-performance computing by making use of user-owned underutilized or idle resources, e.g., laptops and desktop computers closer to fog devices. We propose volunteer-supported FC (VSFC), as a computing paradigm, that explores the interplay of these two distributed computing domains to help minimize inherent communication delays of cloud computing, energy consumption, and network usage. To this effect, we have extended the iFogSim toolkit to support VSFC. Extensive simulations show that VSFC outperforms traditional FC-cloud computing by reducing delay by 47.5%, energy by 93%, and network usage by 92% under normal to heavy load conditions.

The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial IoT (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Especially, some common algorithms in IIoT technology, such as deep models, strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this article, we conduct a comprehensive survey on the opportunities, applications, and challenges of differential privacy in IIoT. We first review related papers on IIoT and privacy protection, respectively. Then, we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up research on industrial differential privacy.

The Internet of Things (IoT) is considered a key enabler for a wide range of smart applications. In IoT, a large number of heterogeneous devices form anad hocconnection with each other. Thead hocinfrastructure is considered an integral part of IoT-empowered applications because of its efficient, cost-effective, and dynamic nature. These networks need to ensure the quality of service using their limited resources, particularly in multihop communication. Because multihop communication can be an easy target of attackers, it needs a secure and reliable data transmission scheme. In this article, we propose a secured and reliable continuous transmission scheme for cognitive hybrid automatic repeat request (HARQ)-aided IoT (SRCT-HARQ) capable of maintaining high throughput and lower delay. The SRCT-HARQ scheme is analytically modeled using a probability-based approach. The mathematical formulas are derived for delay and throughput using a probability-based analysis, and the results are verified using the Monte Carlo simulations. The performance results elaborate that the network throughput and delay are improved, mainly due to the proposed authentication scheme. Using our experimental results, we evaluated the optimal time for data transmission to protect the legal rights of primary users that resulted in improved performance.

Deep learning (DL) has been utilized pervasively in the Internet of Things (IoT). One typical application of DL in IoT is device identification from wireless signals, namely, noncryptographic device identification (NDI). However, learning components in NDI systems have to evolve to adapt to operational variations, such a paradigm is termed as incremental learning (IL). Various IL algorithms have been proposed and many of them require dedicated space to store the increasing amount of historical data, and therefore, they are not suitable for IoT or mobile applications. Besides, conventional IL schemes can not provide satisfying performance when historical data are not available. In this article, we address the IL problem in NDI from a new perspective, first, we provide a new metric to measure the degree of topological maturity of DNN models from the degree of conflict of class-specific fingerprints. We discover that an important cause for performance degradation in IL-enabled NDI is owing to the conflict of devices’ fingerprints. Second, we also show that the conventional IL schemes can lead to low topological maturity of DNN models in NDI systems. Thirdly, we propose a new channel separation-enabled IL (CSIL) scheme without using historical data, in which our strategy can automatically separate devices’ fingerprints in different learning stages and avoid potential conflict. Finally, We evaluated the effectiveness of the proposed framework using real data from automatic-dependent surveillance-broadcast (ADS-B), an application of IoT in aviation. The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services. Data and code available at IEEE Dataport (DOI: 10.21227/1bxc-ke87) andhttps://github.com/pcwhy/CSIL.

The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT makes it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary passively listens to the existing radio communications and then mimic the identity of legitimate devices to conduct malicious activities. Existing solutions employ cryptographic signatures to verify the trustworthiness of received information. In prevalent IoT, secret keys for cryptography can potentially be disclosed and disable the verification mechanism. Noncryptographic device verification is needed to ensure trustworthy IoT. In this article, we propose an enhanced deep learning framework for IoT device identification using physical-layer signals. Specifically, we enable our framework to report unseen IoT devices and introduce the zero-bias layer to deep neural networks to increase robustness and interpretability. We have evaluated the effectiveness of the proposed framework using real data from automatic dependent surveillance-broadcast (ADS-B), an application of IoT in aviation. The proposed framework has the potential to be applied to the accurate identification of IoT devices in a variety of IoT applications and services.

In order to protect industrial safety, improve the operation stability of the industrial control system, conduct the response measures for network environment attacked by the external world, and realize simulation in virtual reality environment, in this study, class and sample weighted C-support vector machine (CSWC-SVM) algorithm is first proposed using SVM. Then, the intrusion detection model of industrial control network is built based on the CSWC-SVM algorithm. Finally, KDD CUP 1999 data are introduced to carry out simulation experiments on the algorithm model constructed in this study in the virtual reality simulation environment. The results show when the penalty factor of the polynomial kernel function, radial basis kernel function, and sigmoid kernel function is 104, the average number of support vectors is 45, 46, and 37, respectively; the average training time are about 0.43, 0.45, and 0.47 s, and the average test time is about 9.7, 9.9, and 10.2 s, respectively; the average recognition accuracy is about 85.7%, 86.2%, and 86.7%, and the false positive rate is 3.8%, 2.8%, and 2.3%, respectively; the accuracy of the CSWC-SVM algorithm in different sample sizes (1000-6000) can be kept above 90%. The operation error rate of the CSWC-SVM algorithm is lower than that of C-SVM, C-SVM, and RS-SVM algorithms under different validation data sets. After dimension reduction, the classification accuracy of the CSWC-SVM algorithm is higher than that of C-SVM and WC-SVM algorithms. The weight value increases from 0 to 200, and the number of model errors on 1000, 2000, and 3000 pieces of data decreases significantly. When the weight value is 200, the number of errors drops to 0, and the classification accuracy reaches 100%. In a word, the CSWC-SVM algorithm constructed in this study performs well in response to the attack of the industrial control system in the virtual reality simulation environment, which provides practical significance for the application of virtual reality in industrial monitoring.

The purposes are to enable large-scale Internet of Things (IoT) devices to analyze data more effectively and provide high-efficiency, low-energy, and wide-coverage technical services for terminals. The channel model and energy loss model analyze the devices' access performance, data transmission path delay, energy consumption in the IoT, and large-scale devices' access in the cellular narrowband IoT (NB-IoT) based on big data analysis technology are also discussed. The results show that in the access success rate analysis, the access success rate is the highest with an access time ( T) of 5 s and a preamble resource number ( K) of 25. The restriction factor is inversely proportional to the access success rate. In the node utilization analysis, different transmission node priorities result in different node utilization, and priority 2's node utilization is better than that of priority 1. Moreover, local data makes data analysis and transmission faster. The search time is prolonged, and the corresponding energy consumption is also higher without local data. In the energy consumption analysis, with the 6-generation (6G) technology, different interference thresholds lead to the different energy efficiency of data transmission. The larger the interference threshold, the higher the energy efficiency. Therefore, the 6G-based big data analysis technology can significantly improve large-scale IoT devices' access success rate and enable the system to meet the requirements of low energy consumption and high access success rate, significant for research on more devices' access data analysis.

In order to explore the application value of deep learning denoising autoencoder (DAE) in Internet-of-Things (IoT) fusion security, in this study, a hierarchical intrusion security detection model stacked DAE supporting vector machine (SDAE-SVM) is constructed based on the three-layer neural network of self-encoder. The sample data after dimension reduction are obtained by layer by layer pretraining and fine-tuning. The traditional deep learning algorithms [stacked noise autoencoder (SNAE), stacked autoencoder (SAE), stacked contractive autoencoder (SCAE), stacked sparse autoencoder (SSAE), deep belief network (DBN)] are introduced to carry out the comparative simulation with the model in this study. The results show that when the encoder in the model is a 4-layer network structure, the accuracy rate (Ac) of the model is the highest (97.83%), the false-negative rate (Fn) (1.27%) and the false-positive rate (Fp) (3.21%) are the lowest. When the number of nodes in the first hidden layer is about 110, the model accuracy is about 98%. When comparing the model designed in this study with the common feature dimension reduction methods, the Ac, Fn, and Fp of this model are the best, which are 98.12%, 3.21%, and 1.27%, respectively. When compared with other deep learning algorithms of the same type, the recognition rate, Ac, error rate, and rejection rate show good results. In multiple data sets, the recognition rate, Ac, error rate, and rejection rate of the model in this study are always better than the traditional deep learning algorithms. In conclusion, when deep learning SDAE is applied to IoT convergence-based intrusion security detection, the detection load can be reduced, the detection effect can be improved, and the operation is more secure and stable.

In modern decentralized Internet-of-Things (IoT)-based sensor communications, pseudonoise-diffusion oracles are heavily investigated as random oracles for data exchange among peer nodes. As these oracles are generated through algorithmic processes, they pass the standard random tests for finite and bounded intervals only. This ensures a false sense of privacy and confidentiality in exchange through open protocol IoT-stacks in public channels, i.e., Internet. Recently, blockchain (BC)-envisioned random sequences as input oracles are proposed about financial applications, and windfall games like roulette, poker, and lottery. These random inputs exhibit fairness, and nondeterminism in SC executions termed as probabilistic smart contracts (PSCs). However, the IoT-enabled PSC process might be controlled and forged through humans, machines, and bot-nodes through physical and computational methods. Moreover, dishonest entities like contract owners, players, and miners can co-ordinate together to form collusion attacks during consensus to propagate false updates, which ensures forged block additions by miners in BC. Motivated by these facts, in this article, we propose a BC-envisioned IoT-enabled PSC scheme,SaNkhyA, which is executed in three phases. In the first phase, the scheme eliminates colluding dishonest miners through the proposed miner selection algorithm. Then, in the second phase, the elected miners agree through the proposed consensus protocol to generate a stream of random bits. In the third phase, the generated random bit-stream is split through random splitters and fed as input oracles to the proposed PSC among participating entities. In simulation, the scheme ensures a trust probability of 0.38 even at 85% collusion among miners and has an average block processing delay of 1.3 s compared to serial approaches, where the block processing delay is 5.6 s, thereby exhibiting improved scalability. The overall computation and communication cost is 28.48 ms, and 101 bytes, respectively, that indicates the efficacy of the proposed scheme compared to the traditional schemes.

In the recent years, the blockchain (BC) technology has been used in various applications ranging from financial sector to healthcare sector. Moreover, developing BC-based solutions for these applications has been an area of interest among the academia and industry professionals. Diamond has huge potential to become an investment asset, but there are some issues, which hinder the progress of the diamond industry, such as provenance, supply chain traceability, involvement of third party in the verification process, and reliability of transactions. BC seems to be a promising technology, which bridges the gap between the diamond industry and the burgeoning financial markets. Individuals are always confident that the crystals they bought are legitimate and that stolen property can be handed back to the legitimate owner easily. Motivated from these facts, in this article, we surveyed the adoption of BC in the diamond industry, and also present pros and cons of this integration. Then, we discuss issues of the diamond industry operations and based on the literature review, we suggest their probable countermeasures. Then, we analyze various open research issues and challenges associated with integrating the BC in the diamond industry. Finally, we present a case study on frameworks, such as Everledger and Tracr, which highlights the real-time challenges of integrating BC in the diamond Industry.

In the area of geographic information processing, there are few researches on geographic text classification. However, the application of this task in Chinese is relatively rare. In our work, we intend to implement a method to extract text containing geographical entities from a large number of network texts. The geographic information in these texts is of great practical significance to transportation, urban and rural planning, disaster relief, and other fields. We use the method of graph convolutional neural network with attention mechanism to achieve this function. Graph attention networks (GAT) is an improvement of graph convolutional neural networks (GCN). Compared with GCN, the advantage of GAT is that the attention mechanism is proposed to weight the sum of the characteristics of adjacent vertices. In addition, We construct a Chinese dataset containing geographical classification from multiple datasets of Chinese text classification. The Macro-F Score of the geoGAT we used reached 95% on the new Chinese dataset.

Fuzzing is one of the most effective vulnerability detection techniques, widely used in practice. However, the performance of fuzzers may be limited by their inability to pass complicated checks, inappropriate mutation frequency, arbitrary mutation strategy, or the variability of the environment. In this article, we present DeepFuzzer, an enhanced greybox fuzzer with qualified seed generation, balanced seed selection, and hybrid seed mutation. First, we use symbolic execution in a lightweight approach to generate qualified initial seeds which then guide the fuzzer through complex checks. Second, we apply a statistical seed selection algorithm to balance the mutation frequency between different seeds. Further, we develop a hybrid mutation strategy. The random and restricted mutation strategies are combined to maintain a dynamic balance between global exploration and deep search. We evaluate DeepFuzzer on the widely used benchmark Google fuzzer-test-suite which consists of real-world programs. Compared with AFL, AFLFast, FairFuzz, QSYM, and MOPT in the 24-hour experiment, DeepFuzzer discovers 30, 240, 102, 147, and 257 percent more unique crashes, executes 40, 36, 36, 98, and 15 percent more paths, and covers 37, 34, 34, 101, and 11 percent more branches, respectively. Furthermore, we present the practice of fuzzing a message middleware from Huawei with DeepFuzzer, and nine new vulnerabilities are reported.

The continuous development of industry big data technology requires better computing methods to discover the data value. Information forecast, as an important part of data mining technology, has achieved excellent applications in some industries. However, the existing deviation and redundancy in the data collected by the sensors make it difficult for some methods to accurately predict future information. This article proposes a semisupervised prediction model, which exploits the improved unsupervised clustering algorithm to establish the fuzzy partition function, and then utilize the neural network model to build the information prediction function. The main purpose of this article is to effectively solve the time analysis of massive industry data. In the experimental part, we built a data platform on Spark, and used some marine environmental factor datasets and UCI public datasets as analysis objects. Meanwhile, we analyzed the results of the proposed method compared with other traditional methods, and the running performance on the Spark platform. The results show that the proposed method achieved satisfactory prediction effect.

The rapid development of Internet of Things (IoT) has triggered more security requirements than ever, especially in detecting vulnerabilities in various IoT devices. The widely used clone-based vulnerability search methods are effective on source code; however, their performance is limited in IoT binary search. In this article, we present IoTSeeker, a function semantic learning based vulnerability search approach for cross-platform IoT binary. First, we construct the function semantic graph to capture both the data flow and control flow information and encode lightweight semantic features of each basic block within the semantic graph as numerical vectors. Then, the embedding vector of the whole binary function is generated by feeding the numerical vectors of basic blocks to our customized semantics aware neural network model. Finally, the cosine distance of two embedding vectors is calculated to determine whether a binary function contains a known vulnerability. The experiments show that IoTSeeker outperforms the state-of-the-art approaches for identifying cross-platform IoT binary vulnerabilities. For example, compared to Gemini, IoTSeeker finds 12.68% more vulnerabilities in the top-50 candidates, and improves the value of AUC for 8.23%.

The eight papers in this special section focus on advanced visual perception technologies.

Visual perception refers to the process of organizing, identifying, and interpreting visual information in environmental awareness and understanding. With the rapid progress of multimedia acquisition technology, research on visual perception has been a hot topic in the academical field and industrial applications. Especially after the introduction of artificial intelligence theory, intelligent visual perception has been widely used to promote the development of industrial production towards intelligence. In this article, we review the previous research and application of visual perception in different industrial fields such as product surface defect detection, intelligent agricultural production, intelligent driving, image synthesis, and event reconstruction. The applications basically cover most of the intelligent visual perception processing technologies. Through this survey, it will provide a comprehensive reference for research on this direction. Finally, this article also summarizes the current challenges of visual perception and predicts its future development trends.

In diverse applications of the industrial Internet of Things (IIoT), the six degrees of freedom (6-DoF) information is essential, which determines the attitude and position of a 3-D object. Nevertheless, due to the complexity and variability of the scenarios, higher requirements are imposed on the 6-DoF estimation. Among them, occlusion is one of the knottiest problems, which causes significant performance degradation and needs to be solved urgently. Therefore, in this article, we propose a completely new and universal multibranch network (MBN) for industrial applications. Our method is based on monocular vision system and convolutional neural network (CNN) framework. First and foremost, it reduces occlusion interference by focusing on the physical area characteristics of the image. Compared with the traditional CNN-based method, it owns higher accuracy and lower estimation error under occlusion. Second, we propose five algorithms to process the predictions of the independent branches, further effectively improving performance. Third, we optimize the marker to solve the inequality problem in attitude angle estimation. Furthermore, we design and conduct a series of experiments, and the experimental results sufficiently prove the superiority of MBN.

Due to the limited local computation resource, industrial vehicular computation requires offloading the computation tasks with time-delay sensitive and complex demands to other intelligent devices (IDs) once the data is sensed and collected collaboratively. This article considers offloading partial computation tasks of the industrial vehicles (IVs) to multiple available IDs of the industrial mobile edge computing (MEC), including unmanned aerial vehicles (UAVs), and the fixed-position MEC servers, to optimize the system cost including execution time, energy consumption, and the ID rental price. Moreover, to increase the access probability of IV by the UAVs, the geographical area is divided into small partitions and schedule the UAVs regarding the regional IV density dynamically. A minimum incremental task allocation algorithm is proposed to divide the whole task and assign the divided units for the minimum cost increment each time. Experimental results show the proposed solution can significantly reduce the system cost.

In this paper, a competitive no-reference metric is proposed to assess the perceptive quality of screen content images (SCIs), which uses the human visual edge model and AdaBoosting neural network. Inspired by the existing theory that the edge information which reflects the visual quality of SCI is effectively captured by the human visual difference of the Gaussian (DOG) model, we compute two types of multi-scale edge maps via the DOG operator firstly. Specifically, two types of edge maps contain contour and edge information respectively. Then after locally normalizing edge maps, L -moments distribution estimation is utilized to fit their DOG coefficients, and the fitted L -moments parameters can be regarded as edge features. Finally, to obtain the final perceptive quality score, we use an AdaBoosting back-propagation neural network (ABPNN) to map the quality-aware features to the perceptual quality score of SCIs. The reason why the ABPNN is regarded as the appropriate approach for the visual quality assessment of SCIs is that we abandon the regression network with a shallow structure, try a regression network with a deep architecture, and achieve a good generalization ability. The proposed method delivers highly competitive performance and shows high consistency with the human visual system (HVS) on the public SCI-oriented databases.

Internet of Medical Things (IoMT) platform serves as an interoperable medium for healthcare applications by connecting wearable sensors, end-users, and clinical diagnosis centers. This interoperable medium provides solutions for disease diagnosis; predicting and monitoring end-user health using physiological vital signs sensed wearable sensor data. The communicating and data exchanging internet of things (IoT) platform imposes latency and overloading uncertainties in the heterogeneous environment. This article introduces cognitive data processing for uncertainty analysis (CDP-UA) to improve WS data management's efficiency. CDP-UA addresses uncertainties in two levels namely aggregation and dissemination of WS data. The uncertainties in synchronizing aggregation and dissemination slot mapping are addressed using classification learning. In the dissemination process overloaded intervals are identified and segregated using regression learning and conditional sigmoid function analysis. The joint learning process helps to classify overloaded and latency-centric dissemination and aggregation instances to improve WS data delivery in the clinical/medical analysis center. The experimental analysis shows that the proposed method is reliable in achieving less uncertainty factor, latency, and overloaded intervals for varying disseminations and sensing intervals.

Medical image processing is one of the most important topics in the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical imaging tasks. In this paper, we propose a novel transfer learning framework for medical image classification. Moreover, we apply our method COVID-19 diagnosis with lung Computed Tomography (CT) images. However, well-labeled training data sets cannot be easily accessed due to the disease's novelty and privacy policies. The proposed method has two components: reduced-size Unet Segmentation model and Distant Feature Fusion (DFF) classification model. This study is related to a not well-investigated but important transfer learning problem, termed Distant Domain Transfer Learning (DDTL). In this study, we develop a DDTL model for COVID-19 diagnosis using unlabeled Office-31, Caltech-256, and chest X-ray image data sets as the source data, and a small set of labeled COVID-19 lung CT as the target data. The main contributions of this study are: 1) the proposed method benefits from unlabeled data in distant domains which can be easily accessed, 2) it can effectively handle the distribution shift between the training data and the testing data, 3) it has achieved 96% classification accuracy, which is 13% higher classification accuracy than "non-transfer" algorithms, and 8% higher than existing transfer and distant transfer algorithms.

Internet of Vehicles (IoV) is a large interactive network composed of information such as vehicle location, speed and route. Vehicles can collect their own environment and state information through GPS, RFID, sensors, camera image processing, and other devices. They can transmit their various information to the central processing unit through Internet technology. These large amounts of vehicle information can be analyzed and processed through computer technology to calculate the optimal route for different vehicles, and report road conditions in time and schedule signal light cycles. Internet of Vehicles (IoV) is a large-scale system network for wireless communication and information exchange between vehicles and people, vehicles and roads, vehicles and the Internet, which is based on intra-vehicle network, inter-vehicle network and vehicle mobile Internet, in accordance with the agreed communication protocols and data interaction standards. The system realizes the integrated network of intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control by “filtering and cleaning” massive data and processing data on the platform. Internet of Vehicles (IoV) system utilizes advanced IoT technology, cloud computing, and big data to make the system fully aware of roads and traffic. It enables all vehicles to collect information through their own environment and state, and upload all kinds of information to the Internet big data platform. The central processing unit collects, analyzes, and processes a large amount of uploaded information. The system will control every vehicle involved in the traffic and control every road in real time to provide users with traffic efficiency and safety.

It is necessary to solve the inaccurate data arising from data reliability ignored by most data fusion algorithms drawing upon collaborative filtering and fuzzy network theory. Therefore, a model is constructed based on the collaborative filtering algorithm and fuzzy network theory to calculate the node trust value as the weight of weighted data fusion. First, aFTWDF (Feedback Trust Weighted for Data Fusion)is proposed. Second,EEFA (Efficiency unequal Fuzzy clustering Algorithm) is introduced into FTWDF considering the defects of the clustering structure caused by ignoring the randomness of node energy consumption and cluster head selection in the practical application of the existing data fusion algorithm. Besides, the fuzzy logic is applied to cluster head selection and node clustering. Finally, an FTWDF-EEFA clustering algorithm is constructed for generating candidate cluster head nodes, which is verified by simulation experiments. The comparative analysis reveals that the accuracy of the FTWDF-EEFA clustering algorithm is 4.1% higher than that of theTMDF (Trust Multiple attributes Decision-making-based data Fusion)algorithm, and 8.3% higher than that ofLDTS(Larger Data fusion based on node Trust evaluation in wireless Sensor networks)algorithm. It performs better in accuracy and recommendation results during the processing of ML100M dataset and NF5M dataset. Besides, the new clustering algorithm increases the survival time of nodes when analyzing the number of death nodes to prolong networks’ lifespan. It improves the survival period of nodes, balances the network load, and prolongs networks’ lifespan. Furthermore, the FTWDF-EEFA clustering algorithm can balance nodes’ energy consumption and effectively save nodes’ overall energy through analysis. Therefore, the optimized algorithm can increase the lifespan of network and improve the trust mechanism effectively. The performance of the algorithm has reached the expected effect, providing a reference for the practical application of the trust mechanism in networks.

With the ever-increasing prosperity of web Application Programming Interface (API) sharing platforms, it is becoming an economic and efficient way for software developers to design their interested mashups through web API re-use. Generally, a software developer can browse, evaluate, and select his or her preferred web APIs from the API's sharing platforms to create various mashups with rich functionality. The big volume of candidate APIs places a heavy burden on software developers’ API selection decisions. This, in turn, calls for the support of intelligent API recommender systems. However, existing API recommender systems often face two challenges. First, they focus more on the functional accuracy of APIs while neglecting the APIs’ actual compatibility. This then creates incompatible mashups. Second, they often require software developers to input a set of keywords that can accurately describe the expected functions of the mashup to be developed. This second challenge tests partial developers who have little background knowledge in the fields. To tackle the above-mentioned challenges, in this article we propose a compatibility-aware and text description-driven web API recommendation approach (named WAR text ). WAR text guarantees the compatibility among the recommended APIs by utilizing the APIs’ composition records produced by historical mashup creations. Besides, WAR text entitles a software developer to type a simple text document that describes the expected mashup functions as input. Then through textual description mining, WAR text can precisely capture the developers’ functional requirements and then return a set of APIs with the highest compatibility. Finally, through a real-world mashup dataset ProgrammableWeb, we validate the feasibility of our novel approach.

In this paper, we study a not well-investigated but important transfer learning problem termed Distant Domain Transfer Learning (DDTL). This topic is closely related to negative transfer. Unlike conventional transfer learning problems which assume that the source domain and the target domain are more or less similar to each other, DDTL aims to make efficient transfers even when the domains or the tasks are completely different. As an extreme example in image classification, there are only a sufficient amount of unlabeled images of watches, airplanes, and horses in the source domain, and the target domain only has a small set of labeled human face images. Previously, a few instance-based distant domain transfer algorithms were proposed to deal with this type of binary distant domain image classification problems. Yet most existing algorithms are very task-specific and they are only good at binary classification tasks. In this study, we propose a novel feature-based distant domain transfer learning algorithm, which requires only a tiny set of labeled target data and unlabeled source data from completely different domains. Instead of selecting intermediate instances, we introduced Distant Feature Fusion (DFF), a novel feature selection method, to discover general features cross distant domains and tasks by using convolutional autoencoder with a domain distance measurement as a feature extractor. As the novelty of this study, it can effectively handle both distant domain mutil-class image classification and binary image classification problems. More importantly, it has achieved up to 19% higher classification accuracy than "non-transfer" algorithms, and up to 9% higher than existing distant transfer algorithms.

Semantic segmentation is a fundamental task extensively used in the analysis of high-resolution aerial images. Due to the objects with various scales widely appearing in high-resolution aerial images, the fixed square receptive field in most existing DCNNs cannot work well and usually leads to unexpected predictions. To alleviate this problem, we propose a light-weight scale-aware module (SWD), which is end-to-end differentiable and can be embedded in most existing networks. In our module, we employ the re-sampling scheme to make each element of convolutional patches adjust its position, which explicitly adjusts the receptive field size to fit different scales of target objects. Further, considering re-sampled feature maps as weighted maps implicitly introduces spatial attention mechanism. As a result, with only 0. 2M additional parameters, the network embedded with our module can automatically adjust its receptive field to better recognize objects in various scales. In the experiment, we evaluate our method on the ISPRS Vaihingen Dataset, especially the analysis of buildings category, which usually in different scales. We further compare it to the spatial attention mechanism and mainstream networks that utilize multi-scale information. The experimental results and comprehensive analysis demonstrate the effectiveness and efficiency of our proposed method.

The space-air integrated networks (SAIN) has been a valuable architecture due to its characteristics of wide coverage and high survey accuracy. However, it is not easy to design routing strategy in SAIN, considering complex relative motion of low-earth-orbit (LEO) satellites and unmanned aerial vehicles (UAV). Specifically, the main problem is how to find optimal links to realize stable and efficient UAV data transmission in time-varying SAIN. To address the problem above, this paper first analyzes the motion characteristics of satellites and UAVs to find the optimal accessing control satellites (ACSs) for the UAV. Then, different from traditional routing, a branch-based link planning strategy (BLPS) is proposed to realize efficient and stable inter-satellite link (ISL) deployment between ACSs, which can guarantee timely transmission of UAV data. Simulation results show that the proposed BLPS strategy is feasible and effective.

With the advancement of unmanned aerial vehicle (UAV) technology, UAV swarm has been showing its great security threats towards the ground facility. With current technologies, it is still challenging in unknown UAV swarm tracking and neutralization. In this paper, we propose an analytical method in predicting drone flying behavior based on the machine learning algorithm, which could be integrated into swarm behavior prediction. Radiofrequency (RF) signals emitted from the UAV are captured by software-defined radio (SDR) to form the time series data. By using conventional short-time Fourier transform (STFT), a time-frequency spectrum revealing the RF data energy distribution is obtained for analyzing the signal variance pattern formed by the two different types of UAV flying trajectory. The transformed time-frequency domain matrix would be applied in multiple machine learning classifier for telling the difference of different flying trajectory. The results present the applicability of using machine learning in predicting the flying features and modes of intruding UAV swarm. It shows the potential application of this method in realizing effective UAV swarm negation.

The purpose of this paper is to present Ethereum decentralized application development methodology with focus on security issues and its verification. We introduce key concepts that are related to developing decentralized applications and Crypto Collectibles games. Moreover, the requirements for blockchain projects were presented along with a selection of use case examples. The paper concerns the application design process issues, starting from the methodology used, going through the description of requirements and specification, ending up with the implementation. Finally, an overview of the issues associated with the security of Ethereum decentralized applications is presented. We compared guidelines from Ethereum Smart Contract Best Practices by ConsenSys, Smart Contract Security Verification Standard created by SecuRing, Decentralized Application Security Project introduced by NCC Group, Security Considerations from Solidity documentation, Ethereum Smart Contracts Security Recommendations from Guylando Knowledge Lists, and Smart Contract Weakness Classification and Test Cases. It was discussed which guideline should be followed and when should the verification take place, considering the life cycle of the application. The paper covers different security risks related to blockchain games along with examples of how vulnerabilities can arise, how they can be detected during security verification phase, and countermeasures to address them.

We propose a DQN based reinforcement learning method in swarm communication constraint based formation control with target searching function. A decentralized communication performance indicator is applied in evaluating the UAV's formation control in simulating a more realistic wireless communication environment between each UAV. A target searching model based on communication aware formation control is presented. The simulation results show that the trained model with state observation space and one thrust action space could be applied in the larger swarm system's group formation and target point tracking.

To accommodate the unprecedented increase of commercial airlines over the next ten years, the Next Generation Air Transportation System (NextGen) has been implemented in the USA that records large-scale Air Traffic Management (ATM) data to make air travel safer, more efficient, and more economical. A key role of collaborative decision making for air traffic scheduling and airspace resource management is the accurate prediction of flight delay. There has been a lot of attempts to apply data-driven methods such as machine learning to forecast flight delay situation using air traffic data of departures and arrivals. However, most of them omit en-route spatial information of airlines and temporal correlation between serial flights which results in inaccuracy prediction. In this paper, we present a novel aviation delay prediction system based on stacked Long Short-Term Memory (LSTM) networks for commercial flights. The system learns from historical trajectories from automatic dependent surveillance-broadcast (ADS-B) messages and uses the correlative geolocations to collect indispensable features such as climatic elements, air traffic, airspace, and human factors data along posterior routes. These features are integrated and then are fed into our proposed regression model. The latent spatio-temporal patterns of data are abstracted and learned in the LSTM architecture. Compared with previous schemes, our approach is demonstrated to be more robust and accurate for large hub airports.

The radio frequency identification (RFID) technologies, which have been widely adopted in different Internet of Things (IoT) applications, are the fundamental building block for achieving smart factories, smart logistics, smart stores, etc. Besides knowing the ID of tags, the relative location information of different tags is of great importance since it contains the spatial relationship among different tags, which is essential for many object localization applications beyond the capacity of absolute localization approaches. In this article, we proposeTagSort, an RFID-based sorting system that exploits the physical layer information, i.e., the phase of RFID wireless signals to achieve the relative localization of different tags. Several novel filtering and peak detection algorithms are proposed to achieve accurate and robust detection of the order of tags. Extensive evaluation shows promising results (over 95% accuracy) and makeTagSorta promising system for future RFID sorting systems, thus enabling a variety of IoT applications and services.

In order to investigate security and privacy issues in the mobile Internet of Things (IoT), the access control four states (AC4E) IoT model is constructed, data physical fusion technology is adopted, and simulation experiments are conducted to explore its performance in terms of optimal response path, trust mechanism, and privacy rate. The results show that AC4E has good reactivity, correctness, security, parallel processing, and other characteristics in the analysis of the optimal response path, trust mechanism, and security rate. The AC4E trust model can be used to isolate the influence of “malicious” nodes and reduce the energy loss of the system. Further analysis of its secrecy rate shows that when the number of iterations is 5, nodes at different positions can maximize the secrecy performance of the system according to different power distribution. Therefore, the application of data physical fusion technology in the mobile IoT can effectively improve the security and privacy performance of the network, and provide a certain experimental reference for the improvement of the performance of the mobile IoT in the later stage, which has important research significance.

With the emergence of the Internet of Things (IoT), billions of heterogeneous physical objects are connected through a network for collecting and sharing information, which can improve various aspects of daily lives, including smart living and transportation, and smart ambient environment, including smart city, smart home, smart agriculture, smart water, waste management, etc. The main objective of the IoT devices is to provide seamless services to the users without their intervention. The all-connected paradigm (i.e., connecting people, things, processes, and data in the network) is based on near Internet ubiquity and includes three types of communication: 1) machine-to-machine; 2) person-to-machine; and 3) person-to-person, and consists as the base for reliable services provision to the end devices at the edge. Fog paradigm implements effectively and efficiently the all-data requests to be shared in a reliable manner as fog implementations complement the cloud computing paradigm by extending computing and caching capabilities to the edges of the network, and it facilitates smart localization decisions and rapid responses. The wide range of IoT services calls for a disruptive, highly efficient, scalable, and flexible communication network able to cope with the increasing demands and the number of connected devices, as well as the diverse and stringent application requirements.

The deepening of ocean measurement work requires higher transmission bandwidth and information calculation efficiency, which provides an opportunity for fog computing. Compared with cloud computing, fog computing shows distribution because it concentrates data processing and application on devices at the edge of the network. In this article, the Ocean of Things (OoT) framework is designed for marine environment monitoring based on the Internet of Things technology. The OoT is divided into three layers: 1) data acquisition layer; 2) fog layer; and 3) cloud layer. In the fog layer, in order to complete the quality control of the sensor measurement data, we use the numerical gradient-based method to process the original acquisition data. An improved D-S algorithm is designed for multisensor information fusion, reducing the data capacity and improving data quality. In the cloud layer, we build ocean information change models based on the fog layer data to predict the dynamic ocean environment. The designed fog layer is evaluated based on marine multisensor information. The results have shown that fog-based multisensor data processing shows low time consumption and high reliability. Moreover, this article uses real temperature data sets to evaluate the prediction accuracy of the cloud model. Finally, we tested the performance of the designed OoT framework with multiple data sets. The simulation results show that the framework can improve the efficiency of data utilization at sea and improve the efficiency of information utilization.

This special issue (SI) aims to present recent advances in the design and analysis of communication interfaces for Industry 4.0. The Industry 4.0 paradigm aims to integrate advanced manufacturing techniques with Industrial Internet-of-Things (IIoT) to create an agile digital manufacturing ecosystem. The main goal is to instrument production processes by embedding sensors, actuators and other control devices which autonomously communicate with each other throughout the value-chain[1].

The Internet of Things integrates a large number of distributed nodes to collect or transmit data. When the network scale increases, individuals use multiple sink nodes to construct the network. This increases the complexity of the network and leads to significant challenges in terms of the existing methods with respect to the aspect of data forwarding and collection. In order to address the issue, this paper proposes a Shortcut Addition strategy based on the Particle Swarm algorithm (SAPS) for multisink network. It constructs a network topology with multiple sinks based on a small-world network. In the SAPS, we create a fitness function by combining the average path length and load of the sink node, to evaluate the quality of a particle. Subsequently, crossover and mutation are used to update the particles to determine the optimal solution. The simulation results indicate that the SAPS is superior both to the greedy model with small world and the load-balanced multigateway aware long link addition strategy in terms of the average path length, load balance, and number of added shortcuts.

Driving safety has been a hot topic in recent vehicular research. However, research on active control strategy, by which an accident might be avoided before it really happens, is still lacking, especially those appealing to machine learning methods with real traffic data. In addition, previous works constructed models with only one or a few factors considered, while the impact of multiple factors on a collision probability is overlooked. In this paper, based on machine learning methods with an actual traffic dataset, we propose a multi-level active safety control strategy taking the Multi-source, Multi-parameter, and Multi-purpose (3M) properties of an accident into consideration. First, by analyzing the impact of different conditions on an accident with the AHP (Analytic Hierarchy Process)-Ridge regression and bisecting K-means clustering model, the safety inter-vehicle distance is derived by learning from an actual traffic dataset. Besides, ELM(Extreme Learning Machines) is adopted as a verification scheme for safety distance calculation. Subsequently, we design a three-level active safety control scheme using the LQG (Linear Quadratic Gaussian) optimal-control model based on the obtained safety inter-vehicle distance. Numerical results show that by comparing with some classical braking and car-following models, our strategy can always keep the distance of two followed vehicles at a safety state. To further explore the impact of the time complexity on the rear-end collisions, we also implemented a road-test and verified that our model can timely respond to the risks and keep two cars always in safety.

To study the security performance of the Internet of multimedia things on the privacy protection of user identity, behavior trajectory, and preference under the new information technology industry wave, in this study, aiming at the problems of the sharing of Internet of things perception data and the exposure of users’ privacy information, the Anonymous Batch Authentication Scheme (ABAH) for privacy protection is designed. Hash-based Message Authentication Code is used to cancel the list-checking process and analyze its security performance. Compared with the methods of elliptic curve digital signature algorithm, Bayes least-square method, identity-based bulk verification, anonymous batch authentication and key protocol, conditional privacy authentication scheme, and expert message authentication protocol, the transmission delay, packet loss rate, and computation cost are studied without considering the undo list and during the undo check. The results show that with the increase of information size, the transmission delay and packet loss rate also increase, and the transmission delay of ABAH increases by about 15%, while the correlation between speed and transmission delay is small. In the case of the same amount of validation information, ABAH has the highest validation efficiency, and it still has an efficient validation effect in the case of invalid information. The message packet loss rate for ABAH is always 0 when the undo check validation overhead is considered. It can be found that ABAH can avoid the communication overhead and privacy leakage caused by the revocation list, ensure the integrity of batch verification information, meet the security performance of the vehicular ad hoc network under the Internet of Things, and protect the privacy of users from being disclosed.

Software Defined Networking (SDN) architecture satisfies the flexibility and scalability requirements of Internet of Things (IoT) network. A large amounts of IoT data is transmitted and exchanged through IoT network. However, many of services of IoT are sensitive to latency and bandwidth, so the network traffic model and measurement in IoT are different legacy networks. In this paper, we propose a robust network traffic modeling approach and use it to estimate network traffic in IoT. To obtain the measurement results with low overhead and high accuracy, we model the network traffic as liner function with noise. Then, we collect the statistics of coarse-grained traffic of flows and fine-grained traffic of links, and use the robust network traffic model to forecast the network traffic with the coarse-grained measurement of flows. In order to optimize the estimation results, we propose an optimization function to decrease the estimation errors. Since the optimization function is NP-hard problem, then we use a heuristic algorithm to obtain the optimal solution of the fine-grained measurement. Finally, we conduct some simulations to verify the proposed measurement scheme. Simulation results show that our approach is feasible and effective.

The low earth orbit (LEO) satellite network has been a valuable architecture due to its characteristics of wide coverage and low transmission delay. Utilizing LEO satellites as edge computing nodes to provide real-time services for access terminals will be the indispensable paradigm of integrated space-air-ground network. However, it is not easy to design resource management strategies in edge computing satellite (ECS), considering different accessing planes and resource requirements of terminals. Moreover, a comprehensive analysis of the network topology, relative motion, and available resources is required to establish ECS collaborative networks. To address these problems, the dynamic resource allocation architecture and advanced K-means algorithm (AKA) in ECSs are proposed. Then, the extended graph model and breadth-first-search-based spanning tree (BFST) algorithm are utilized to guide the inter-satellite link (ISL) construction. As a result, the ECS collaborative network is established with fine-grained resource management. Simulation results show that the proposed fine- grained resource management scheme is feasible and effective.

Coverage-guided kernel fuzzing is a widely-used technique that has helped kernel developers and testers discover numerous vulnerabilities. However, due to the high complexity of application and hardware environment, there is little study on deploying fuzzing to the enterprise-level Linux kernel. In this paper, collaborating with the enterprise developers, we present the industry practice to deploy kernel fuzzing on four different enterprise Linux distributions that are responsible for internal business and external services of the company. We have addressed the following outstanding challenges when deploying a popular kernel fuzzer, syzkaller, to these enterprise Linux distributions: coverage support absence, kernel configuration inconsistency, bugs in shallow paths, and continuous fuzzing complexity. This leads to a vulnerability detection of 41 reproducible bugs which are previous unknown in these enterprise Linux kernel and 6 bugs with CVE IDs in U.S. National Vulnerability Database, including flaws that cause general protection fault, deadlock, and use-after-free.

Summary In the era of edge computing, real‐time data preprocessing on the edge node has the potential to improve computational efficiency and data accuracy. However, a significant challenge is private data disclosure, particularly in the case of location‐based services. To address this challenge, in this paper, by leveraging differential privacy, we propose a privacy‐aware framework for mobile edge computing called MEPA to protect the location privacy in which the edge node is regarded as an anonymous central server. The proposed framework can provide computing services without deploying special infrastructure. To be specific, in order to solve the problem of constrained computing resources in the edge nodes, the algorithm of Quadtree Differential Privacy based on Hilbert curve division (QTDP‐H) two‐dimensional spatial data query transmission is proposed. First, a noise quadtree is established and the privacy budget is divided according to the tree level. Then, the constructed quadtree is represented by quanternary, so that the partition based on Hilbert curve can be established and the two‐dimensional data in the area can be converted into one‐dimensional, which can greatly improve the retrieval efficiency. The effectiveness of the proposed algorithm in terms of time complexity and retrieval accuracy has been verified by extensive experimental results. Compared with traditional methods of (D,ε) − LP, the average runtime can be reduced by 15%‐20%, and the average relative error is reduced by 20%.

Aircraft tracking is a significant technology for military reconnaissance, but there is no efficient algorithm to solve this particular problem. Recently, research based on deep learning for object tracking has developed rapidly, and the performance is greatly improved compared to the traditional methods, so the authors refer to relevant work and make an improvement on the previous research to improve the performance on aircraft tracking. They first learn the idea from region‐based fully convolutional networks to perform detection on each frame of video. To avoid the target drift due to the failure of object detection on a certain frame, then they employ Kalman filter (KF) and extended KF together to predict the moving trajectory of the target. Beyond that, this method can confine the valid range based on the size of a target object, which increases the speed of detection. This approach can also correct the bounding box on adjacent frames. The steps are not complicated but have an excellent performance. Through the experiment, it is clear that the proposed method is reasonable and more precise.

Today’s vehicles are no longer stand-alone transportation means, due to the advancements on vehicle-tovehicle (V2V) and vehicle-to-infrastructure (V2I) communications enabled to access the Internet via recent technologies in mobile communications, including WiFi, Bluetooth, 4G, and even 5G networks. The Internet of vehicles was aimed toward sustainable developments in transportation by enhancing safety and efficiency. The sensor-enabled intelligent automation of vehicles’ mechanical operations enhances safety in on-road traveling, and cooperative traffic information sharing in vehicular networks improves traveling efficiency.

Blind or visually impaired people face special difficulties in daily life. With the advances in vision sensors and computer vision, the design of wearable vision assistance system is promising. In order to improve the life quality of the visually impaired group, a wearable system is proposed in this paper. Typically the performance of visual sensors is affected by a variety of complex factors in practice, resulting in a large number of noise and distortion. In this paper, we will creatively leverage image quality evaluation to select the captured images through vision sensors, which can ensure the input quality of scenes for the final identification system. First, we use binocular vision sensors to capture images in a fixed frequency and choose the informative ones based on stereo image quality assessment. Then the captured images will be sent to cloud for further computing. Specially, the detection and automatic result will be done for all the received images. Convolutional neural network based on big data will be used in this step. According to image analysis, the cloud computing can return the requested information for users, which can help them make a more reasonable decision in further action. Simulations and experiments show that the proposed method can solve the problem effectively. In addition, statistical results also demonstrate that wearable vision system can make visually impaired group more satisfied in visual needed situations.

With the development of the Internet-of-Things (IoT) industry, more and more fields are involved such as multimedia data. Currently, users rely on videos and images with high data volume, so it has brought more challenges for wireless communication and transmission. For multimedia data, it is obviously different from traditional communication data. So new method is required to solve the problem of high data volume in communication. The proactive content caching and the unmanned aerial vehicle (UAV) relaying techniques are deployed over IoT network, enabling the maximum throughput for the served IoT devices. Even though these two existing technologies are important to solve the problem of throughput, there are still other challenges for efficiently improving the system throughput. We mainly study the cache-enabled UAV to maximize throughput among IoT devices in the IoT with the placement of content caching and UAV location. Especially, we divide the joint optimization problem into two parts. First, the UAV deployment problem is decomposed into vertical and horizontal dimensions to ensure the optimal deployment height and 2-D position. The enumeration search method is employed to obtain the 2-D position. Then, we also formulate a concave problem for probabilistic caching placement. Experimental results have indicated that the cache-enabled UAV scheme can obtain a better throughput, which can bring new approach for multimedia data throughput maximization in IoT system.

Simulink is widely used for model-driven development (MDD) of cyber-physical systems. Typically, the Simulink-based development starts with Stateflow modeling, followed by simulation, validation, and code generation mapped to physical execution platforms. However, recent trends have raised the demands of rigorous verification on safety-critical applications to prevent intrinsic development faults and improve the system dependability, which is unfortunately challenging. Even though the constructed Stateflow model and the generated code pass the validation of Simulink Design Verifier and Simulink Polyspace, respectively, the system may still fail due to some implicit defects contained in the design model (design defect) and the generated code (implementation defects). In this article, we bridge the Stateflow-based MDD and a well-defined rigorous verification to reduce development faults. First, we develop a self-contained toolkit to translate a Stateflow model into timed automata, where major advanced modeling features in Stateflow are supported. Taking advantage of the strong verification capability of Uppaal, we can not only find bugs in Stateflow models that are missed by Simulink Design Verifier but also check more important temporal properties. Next, we customize a runtime verifier for the generated non-intrusive VHDL and C code of a Stateflow model for monitoring. The major strength of the customization is the flexibility to collect and analyze runtime properties with a pure software monitor, which offers more opportunities for engineers to achieve high reliability of the target system compared with the traditional act that only relies on Simulink Polyspace. In this way, safety-critical properties are both verified at the model level and at the consistent system implementation level with physical execution environment in consideration. We apply our approach to the development of a typical cyber-physical system-train communication controller based on the IEC standard 61375. Experiments show that more ambiguousness in the standard are detected and confirmed and more development faults and those corresponding errors that would lead to system failure have been removed. Furthermore, the verified implementation has been deployed on real trains.

“Smart” has gradually infiltrating all areas of people's daily life and the environments where we lead our life. The term of “Smart Industrial Environment” can be used to refer to each aspect of the industrial environments focused on the future, being smart vehicles, smart systems of transportation, smart devices (wearables and smartphones), smart services (such as just-in-time production pipelines adjusted to the requirements of the supply-chain), smart grids, smart factories and smart plants management utilizing information technology. It includes the inter-connection of all the smart technologies, involving every type of political and technological borders besides being a term that involves all the aspects.

Recently, vehicular ad hoc networks (VANETs) have been attracting significant attention for their potential for guaranteeing road safety and improving traffic comfort. Due to high mobility and frequent link disconnections, it becomes quite challenging to establish a reliable route for delivering packets in VANETs. To deal with these challenges, an artificial spider geographic routing in urban VAENTs (ASGR) is proposed in this paper. First, from the point of bionic view, we construct the spider web based on the network topology to initially select the feasible paths to the destination using artificial spiders. Next, the connection-quality model and transmission-latency model are established to generate the routing selection metric to choose the best route from all the feasible paths. At last, a selective forwarding scheme is presented to effectively forward the packets in the selected route, by taking into account the nodal movement and signal propagation characteristics. Finally, we implement our protocol on NS2 with different complexity maps and simulation parameters. Numerical results demonstrate that, compared with the existing schemes, when the packets generate speed, the number of vehicles and number of connections are varying, our proposed ASGR still performs best in terms of packet delivery ratio and average transmission delay with an up to 15% and 94% improvement, respectively.

This paper uses big data technologies to study base stations' behaviors and activities and their predictability in mobile cellular networks. With new technologies quickly appearing, current cellular networks have become more larger, more heterogeneous, and more complex. This provides network managements and designs with larger challenges. How to use network big data to capture cellular network behavior and activity patterns and perform accurate predictions is recently one of main problems. To the end, firstly we exploit big data platform and technologies to analyze cellular network big data, i.e. Call Detail Records (CDRs). Our CDRs data set, which includes more than 1000 cellular towers, more than million lines of CDRs, and several million users and sustains for more than 100 days, is collected from a national cellular network. Secondly, we propose our methodology to analyze these big data. The data pre-handling and cleaning approach is proposed to obtain the valuable big data sets for our further studies. The feature extraction and call predictability methods are presented to capture base stations' behaviors and dissect their predictability. Thirdly, based on our method, we perform the detailed activity pattern analysis, including call distributions, cross correlation features, call behavior patterns, and daily activities. The detailed analysis approaches are also proposed to dig out base stations' activities. A series of findings are found and observed in the analysis process. Finally, a study case is proposed to validate the predictability of base stations' behaviors and activities. Our studies demonstrates that big data technologies can indeed be utilized to effectively capture network behaviors and predict network activities so that they can help perform highly effective network managements.

Internet of Vehicles (IoV) has drawn more and more attention. However, due to high vehicle movement and frequent topology changing, it is quite challenging to design an efficient routing protocol in the complex urban IoV. In this paper, we propose a Connectivity aware Transmission quality guaranteed Geographic Routing in urban IoV (CTGR). First, we give the connectivity model in case of disconnected link and the transmission quality (TQ) model when the network is connected, respectively. Then, using the two models, with the assistance of road weight evaluation scheme (RWE), each road segment can be assigned with a suitable weight. Based on the weight information, the road segment can be dynamically selected one by one to comprise the optimized routing path, avoiding the local maximum and data congestion. An improved next hop selection strategy is further proposed to forward the packet along the selected road segment, guaranteeing fast and reliable packet transmission. Simulation results show that our proposed protocol achieves higher packet delivery ratio and lower transmission delay compared with the existing protocols.

Mobile cloud computing is being accepted as the model for mobile users to ubiquitously access a shared pool of cloud computing resources, data and services on-demand. In this context, elasticity debt analytics can be harnessed as a measure for efficient scheduling of cloud resources and guarantee of quality of service requirements. This paper proposes a novel green-driven, game theoretic approach to minimizing the elasticity debt on mobile cloud-based service level, investigating the case when a task is offloaded, scheduled and executed on a mobile cloud computing system. The decision to offload a mobile device user's task on cloud affects the level of elasticity debt minimization for the provided services. The research problem is formulated as an elasticity debt quantification game, elaborating on an incentive mechanism to: (a) predict elasticity debt and mitigate the risk of service overutilization, (b) achieve scalability as the number of mobile device user requests for cloud resources increases or decreases accordingly, and (c) optimize cloud resource provisioning, parameterizing the current pool of active users per service. The experimental results prove the effectiveness of the equilibrium model, which allocates the mobile device user requests to high elasticity debt-level services and facilitate elasticity debt minimization for greener mobile cloud computing environments.

We study the problem of energy-efficient networking in cloud services with geographically distributed data centers for multimedia big data applications. This is significantly challenged by the dynamic end-to-end request demands and unbalanced link energy efficiency, the unbalanced and time-varying link utilization, and the bandwidth and delay constraints for service requirements. To solve these issues, we propose a multi-constraint optimization model for energy efficiency optimization in cloud computing services where data centers are geographically distributed and are interconnected by the cloud network. Our model jointly optimizes the energy efficiency in data centers and the cloud network. More specifically, we present an intelligent heuristic algorithm to solve this model for the dynamic request demands between different data centers and between data centers and users. This is implemented by combining the niche genetic algorithm and random depth-first search. Simulation results for energy-efficient networking show that better gains in network energy efficiency can be achieved by our joint optimization. Additionally, the joint optimization between data centers and the cloud network can further improve energy savings and link utilizations for time-varying requests.

Automatic Dependence Surveillance-Broadcast (ADS-B) is transforming all aspects of aviation, including commercial aircraft and unmanned aerial systems (UAS). ADS-B service broadcasts traffic information and flight information to improve aviation safety and efficiency in the air and on runways, reduce costs, and lessen harmful effects on the environment. However, due to its broadcast nature, on one hand, ADS-B is vulnerable to malicious cybersecurity attacks relevant to broadcasting; on the other hand, broadcasting could be leveraged to further improve the safety of aviation. The objective of this paper is to investigate how to enhance aviation security and safety by leveraging the inherent broadcast property of ADS-B. To be specific, we propose two novel schemes for enhancing aviation cybersecurity and safety, respectively. The first scheme, motivated by the fact that fountain codes have been successfully applied in various broadcasting scenarios for ensuring the reliability, we propose to leverage Fountain Code to enhance the security of ADS-B. The second scheme, which is based on information integration, to detect collaboratively the comprehensive weather information to enhance the safety of aviation. The performance evaluation results demonstrate the feasibility of integrating fountain codes with ADS-B for encryption and the feasibility of collaborative detection to bypass turbulent weather conditions. To our knowledge, our work is the first attempt to apply fountain code to enhance aviation security and safety.

With the rapid development of Internet of Things and data interactive processing technologies, the application of Internet of Things in marine field is emerging. In the face of big oceanic data from various sensors, a model of multi-sensor interaction and data processing is proposed in this paper. On the basis of this model, an oceanic data processing algorithm based on back propagation neural network data fitting is proposed, which can be used for various types of oceanic data. In order to make the data more intuitive and improve the efficiency of data processing, we design a basic model of oceanic data visualization concluding the graphical display and processing. In this model, the standard file will be generated finally through the effective data processing. Through the comparison and analysis of experimental results, it's shown that the algorithm achieves better optimization. And the model of data interaction and visualization are suitable and reasonable in practical application.

Two‐way (on‐frequency) relaying using simple amplify‐and‐forward processing at the satellite isan emerging technology that allows doubling of system spectral efficiency forcertain networking applications. However, the satellite channel is non‐linearand compensation for the self‐interfering signal is more difficult than onlinear channels, especially when memory effects are recognised. In this paper, we model two‐user, asynchronous, bidirectional relaying for uncodedtransmission, and proposed a bidirectional satellite communication algorithmunder same frequency transmission. First, we employ a table addressed byside‐information and the symbol to be detected to model the action of thechannel. Then, we employ a linear adaptive canceler followed by a smaller tablelookup process to cancel the self‐interference and to make decisions. In thesimulation part, we focus in the results section is upon 16‐amplitude phaseshift keying (APSK), widely employed in the DVB‐S2 standard forbandwidth‐efficient operation. Compared with the existing bidirectionalsatellite communication algorithm, our proposed algorithm have the far lesscomplexity and training time at the similar performance.

Radio frequency identification (RFID) technology, with its advantages such as battery-free tags, low cost, and scalability, has been playing an important role in many application domains, such as large-scale storage systems, supermarkets, construction sites, etc. Many of those application scenarios also require indoor positioning technologies, for example, warehouse goods positioning, item positioning in production assembly lines, and worker positioning in construction sites. However,indoor positioning using RFID faces accuracy degradation in dynamic environments, especially when tracking randomly moving targets. In this paper, we proposePSOTrack, a continuous RFID-based tracking system for random moving targets in unconstrained indoor environments. InPSOTrack, a data preprocessed, and an optimized particle swarm optimization algorithm is applied to determine the initial position, after that a dynamic correction method for trajectory prediction is proposed for continuous tracking. Results show that the proposed algorithm effectively improves the positioning accuracy and is able to achieve 1 m localization accuracy in dynamic indoor environments, which makes it a promising technology to support future pervasive RFID-based tracking applications.

The Internet of Things (IoT), which is known as one of the key enabling technologies of smart cities, generally refers to the network of smart objects, which are embedded with sensing, computing, networking, and actuating capabilities that all together enable them to collect and exchange data. The IoT devices are usually wirelessly networked, and they serve as a key enabling technology for many critical smart city applications, such as intelligent transportation, smart grid, smart building, and mobile healthcare. However, security has become a key challenge for the wide deployment of IoT: because of the environmental influences, the IoT data are inherently noisy. Moreover, the IoT devices may be compromised by attackers to intentionally generate fake data. Finally, the underlying wireless network can also be subverted. To address the security issue in IoT, we propose a policy-based secure and trustworthy sensing scheme for IoT named RealAlert, in which the trustworthiness of both data and the IoT devices are evaluated based on both the reporting history and the context in which the data are collected using policy rules. Experimental results have shown that the RealAlert scheme can accurately assess the trust of the sensor nodes as well as data in IoT.

Cognitive networks (CNs) are one of the key enablers for the Internet of Things (IoT), where CNs will play an important role in the future Internet in several application scenarios, such as healthcare, agriculture, environment monitoring, and smart metering. However, the current low packet transmission efficiency of IoT faces a problem of the crowded spectrum for the rapidly increasing popularities of various wireless applications. Hence, the IoT that uses the advantages of cognitive technology, namely the cognitive radio-based IoT (CIoT), is a promising solution for IoT applications. A major challenge in CIoT is the packet transmission efficiency using CNs. Therefore, a new Q-learning-based transmission scheduling mechanism using deep learning for the CIoT is proposed to solve the problem of how to achieve the appropriate strategy to transmit packets of different buffers through multiple channels to maximize the system throughput. A Markov decision process-based model is formulated to describe the state transformation of the system. A relay is used to transmit packets to the sink for the other nodes. To maximize the system utility in different system states, the reinforcement learning method, i.e., the Q learning algorithm, is introduced to help the relay to find the optimal strategy. In addition, the stacked auto-encoders deep learning model is used to establish the mapping between the state and the action to accelerate the solution of the problem. Finally, the experimental results demonstrate that the new action selection method can converge after a certain number of iterations. Compared with other algorithms, the proposed method can better transmit packets with less power consumption and packet loss.

The lack of schedulability evaluation of previous charging schemes in wireless rechargeable sensor networks (WRSNs) degrades the charging efficiency, leading to node exhaustion. We propose an Optimal Path Planning Charging scheme, namely OPPC, for the on-demand charging architecture. OPPC evaluates the schedulability of a charging mission, which makes charging scheduling predictable. It provides an optimal charging path which maximizes charging efficiency. When confronted with a non-schedulable charging mission, a node discarding algorithm is developed to enable the schedulability. Experimental simulations demonstrate that OPPC can achieve better performance in successful charging rate as well as charging efficiency.

Robot systems have been widely used in industry and also play an important role in human social life. Safety critical applications usually demand rigorously formal verification to ensure correctness. But for the increasing complexity of dynamic environments and applications, it is not easy to build a comprehensive model for the traditional offline verification. In this paper, we propose RobotRV, the first data-centered real-time verification approach for the robot system. Within this approach, a domain-specific language named RoboticSpec is designed to specify the complex application scenario of the robot system, the data packets transmitted in the robot system, and the safety critical temporal properties. Then, we develop an engine to automatically translate the RoboticSpec model into a real-time verifier. The generated verifier serves as an independent plug-in component for the runtime verification of concerned temporal properties. We applied the proposed approach to a real robot system. As presented in experiment results, our method detected potential failures, and improved the safety of robot system.

In this paper, we propose a distributed interest forwarder selection (DIFS) scheme that mitigates the interest broadcast storm in vehicular named data networks. In DIFS, a vehicle sends an interest packet piggybacking its location, distance to the neighbors, and speed. In this case, the immediate neighbors do not have the requested content and rank themselves to be an eligible interest forwarder by using multiple attributes. Additionally, every intermediate vehicle uses a digital map to be selected as forwarders in both (forward and backward) directions of the consumer. Simulations show that DIFS satisfies more interest packets with less delay as compared with the recent forwarding solutions.

The emerging technologies for connected vehicles have become hot topics. In addition, connected vehicle applications are generally found in heterogeneous wireless networks. In such a context, user terminals face the challenge of access network selection. The method of selecting the appropriate access network is quite important for connected vehicle applications. This paper jointly considers multiple decision factors to facilitate vehicle-to-infrastructure networking, where the energy efficiency of the networks is adopted as an important factor in the network selection process. To effectively characterize users' preference and network performance, we exploit energy efficiency, signal intensity, network cost, delay, and bandwidth to establish utility functions. Then, these utility functions and multi-criteria utility theory are used to construct an energy-efficient network selection approach. We propose design strategies to establish a joint multi-criteria utility function for network selection. Then, we model network selection in connected vehicle applications as a multi-constraint optimization problem. Finally, a multi-criteria access selection algorithm is presented to solve the built model. Simulation results show that the proposed access network selection approach is feasible and effective.

In this paper, we present a formal model-driven design approach to establish a safety-assured implementation of multifunction vehicle bus controller (MVBC), which controls the data transmission among the devices of the vehicle. First, the generic models and safety requirements described in International Electrotechnical Commission Standard 61375 are formalized as time automata and timed computation tree logic formulas, respectively. With model checking tool Uppaal, we verify whether or not the constructed timed automata satisfy the formulas and several logic inconsistencies in the original standard are detected and corrected. Then, we apply the code generation tool Times to generate C code from the verified model, which is later synthesized into a real MVBC chip, with some handwriting glue code. Furthermore, the runtime verification tool RMOR is applied on the integrated code, to verify some safety requirements that cannot be formalized on the timed automata. For evaluation, we compare the proposed approach with existing MVBC design methods, such as BeagleBone, Galsblock, and Simulink. Experiments show that more ambiguousness or bugs in the standard are detected during Uppaal verification, and the generated code of Times outperforms the C code generated by others in terms of the synthesized binary code size. The errors in the standard have been confirmed and the resulting MVBC has been deployed in the real train communication network.

The collaborative charging issue in Wireless Rechargeable Sensor Networks (WRSNs) is a popular research problem. With the help of wireless power transfer technology, electrical energy can be transferred from wireless charging vehicles (WCVs) to sensors, providing a new paradigm to prolong network lifetime. Existing techniques on collaborative charging usually take the periodical and deterministic approach, but neglect influences of non-deterministic factors such as topological changes and node failures, making them unsuitable for large-scale WRSNs. In this paper, we develop a temporal-spatial charging scheduling algorithm, namely TSCA, for the on-demand charging architecture. We aim to minimize the number of dead nodes while maximizing energy efficiency to prolong network lifetime. First, after gathering charging requests, a WCV will compute a feasible movement solution. A basic path planning algorithm is then introduced to adjust the charging order for better efficiency. Furthermore, optimizations are made in a global level. Then, a node deletion algorithm is developed to remove low efficient charging nodes. Lastly, a node insertion algorithm is executed to avoid the death of abandoned nodes. Extensive simulations show that, compared with state-of-the-art charging scheduling algorithms, our scheme can achieve promising performance in charging throughput, charging efficiency, and other performance metrics.

Existing static grid resource scheduling algorithms, which are limited to minimizing the makespan, cannot meet the needs of resource scheduling required by cloud computing. Current cloud infrastructure solutions provide operational support at the level of resource infrastructure only. When hardware resources form the virtual resource pool, virtual machines are deployed for use transparently. Considering the competing characteristics of multi-tenant environments in cloud computing, this paper proposes a cloud resource allocation model based on an imperfect information Stackelberg game (CSAM-IISG) using a hidden Markov model (HMM) in a cloud computing environment. CSAM-IISG was shown to increase the profit of both the resource supplier and the applicant. Firstly, we used the HMM to predict the service provider's current bid using the historical resources based on demand. Through predicting the bid dynamically, an imperfect information Stackelberg game (IISG) was established. The IISG motivates service providers to choose the optimal bidding strategy according to the overall utility, achieving maximum profits. Based on the unit prices of different types of resources, a resource allocation model is proposed to guarantee optimal gains for the infrastructure supplier. The proposed resource allocation model can support synchronous allocation for both multi-service providers and various resources. The simulation results demonstrated that the predicted price was close to the actual transaction price, which was lower than the actual value in the game model. The proposed model was shown to increase the profits of service providers and infrastructure suppliers simultaneously.

The Internet of Things (IoT) is expected to accommodate every object which exists in this world or likely to exist in the near future. The enormous scale of the objects is challenged by big security concerns, especially for common information dissemination via multicast services, where the reliability assurance for multiple multicast users at the cost of increasing redundancy and/or retransmissions also benefits eavesdroppers in successfully decoding the overheard signals. The objective of this work is to address the security challenge present in IoT multicast applications. Specifically, with the presence of the eavesdropper, an adaptive fountain code design is proposed in this paper to enhance the security for multicast in IoT. The main novel features of the proposed scheme include two folds: (i) dynamical encoding scheme which can effectively decrease intercept probability at the eavesdropper; (ii) increasing the transmission efficiency compared with the conventional nondynamical design. The analysis and simulation results show that the proposed scheme can effectively enhance information security while achieving higher transmission efficiency with a little accredited complexity, thus facilitating the secured wireless multicast transmissions over IoT.

During the past decades, we have witnessed significant research and development efforts related to Sensor Networks (SN) with both wired and wireless technologies. Those efforts resulted in the development of benchmark application paradigms for SNs with applications in both safety-critical and non-safety-critical fields, including environmental monitoring, military apps, tactics, smart sensing, underwater sensing, pollution sensing, fire alarms, and other hundreds of applications to improve our lifestyles. Similarly, the Internet of Things (IoT) has been promoting the emergence of various networking technologies with standardization efforts being carried out by the IEEE community. SNs have been proven to be the most investigated topic for enabling IoT applications. For example, sensors installed in a house may update the owner on his/her cell phone about any medical or security threat. However, to support such applications, SNs and IoT come up against several constraints, like low capacity processing capabilities, battery operated devices, limited transmission ranges, and limited data transmission capacity. One of the key issues in IoT and SNs is the efficient data forwarding in terms of energy consumption, multi-hop data retrieval, safety/emergency message dissemination, etc. The goal of this special issue is to present and highlight the novel advances, emerging technologies, and applications targeting the efficient data collection is the internet of things and sensor networks. This special issue contains thirteen regular papers which can be organized into three groups: data forwarding in SNs, energy efficiency in wireless body area networks, secure data forwarding in SNs.

Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT) and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low‐pass component of network traffic that describes the long‐range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low‐pass component. Otherwise, for the remaining high‐pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.

Aphasia is a common adult language disorder acquired after a stroke, head injury, tumor, etc. Accurate diagnosis influences the prognosis of any speech and language disorder including aphasia. Therefore, in this paper we have proposed a semi-automated Aphasia diagnosis and classification framework employing feature extraction and pattern matching techniques of the digital signal processing (DSP). The proposed scheme evaluates the acoustic properties, time consumed, and speech characteristics for each language component i.e. naming, repetition, and comprehension. The naming and repetition tasks utilize DSP techniques. The proposed solution is highly scalable since it determines the diagnosis based on acoustic properties instead of the language characteristics. Thus, it eases extending into multiple languages. The mathematical relationships calculate the corresponding score for each component. The framework then determines the diagnosis according to the obtained scores. Since it occupies computational analysis of the speech signals, it reduces the subjectivity of the manual diagnosis process, meanwhile increasing the efficiency and accuracy by consistent diagnosis decisions. Finally, it distinguishes two sub types of Aphasia i.e. Anomic Aphasia and Wernicke's Aphasia. The results clearly revealed the efficiency improvement achieved by replacing the live auditory model with pre-recorded auditory model.

Wireless mesh network is prevalent for providing a decentralized access for users. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep belief network and a Gaussian model. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then a prediction model is built by learning a deep belief network from the extracted low-pass component. Otherwise, for the rest high-pass component that expresses the gusty and irregular fluctuations of network traffic, a Gaussian model is used to model it. We estimate the parameters of the Gaussian model by the maximum likelihood method. Then we predict the high-pass component by the built model. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.

Inspired by the facts that visual saliency captures more attention and spectral residual (SR) can indicate the saliency of the image, a novel reduced‐reference image quality assessment metric is proposed based on the regularity of the SR. The orientation and frequency components of an image are first extracted in wavelet domain. Then SR is obtained to represent the saliency of the component. Next fractal dimension is adopted to encode SR and concatenated as the image features. Finally, the feature differences between reference image and distorted one are pooled as the quality score. The proposed metric is evaluated on four largest image databases (TID2013, TID2008, CSIQ, and LIVE databases), and experimental results confirm that the proposed metric has a good performance.

With the rapid development of computer science, problems with digital products piracy and copyright dispute become more serious; therefore, it is an urgent task to find solutions for these problems. In this study, the authors’ develop a digital watermarking algorithm based on a fractal encoding method and the discrete cosine transform (DCT). The proposed method combines fractal encoding method and DCT method for double encryptions to improve traditional DCT method. The image is encoded by fractal encoding as the first encryption, and then encoded parameters are used in DCT method as the second encryption. First, the fractal encoding method is adopted to encode a private image with private scales. Encoding parameters are applied as digital watermarking. Then, digital watermarking is added to the original image to reversibly using DCT, which means the authors can extract the private image from the carrier image with private encoding scales. Finally, attacking experiments are carried out on the carrier image by using several attacking methods. Experimental results show that the presented method has higher performance characteristics such as robustness and peak signal to noise ratio than classical methods.

Face identification and resolution technology is crucial to ensure the identity consistency of humans in physical space and cyber space. In the current Internet of Things (IoT) and big data situation, the increase of applications based on face identification and resolution raises the demands of computation, communication, and storage capabilities. Therefore, we have proposed the fog computing-based face identification and resolution framework to improve processing capacity and save the bandwidth. However, there are some security and privacy issues brought by the properties of fog computing-based framework. In this paper, we propose a security and privacy preservation scheme to solve the above issues. We give an outline of the fog computing-based face identification and resolution framework, and summarize the security and privacy issues. Then the authentication and session key agreement scheme, data encryption scheme, and data integrity checking scheme are proposed to solve the issues of confidentiality, integrity, and availability in the processes of face identification and face resolution. Finally, we implement a prototype system to evaluate the influence of security scheme on system performance. Meanwhile, we also evaluate and analyze the security properties of proposed scheme from the viewpoint of logical formal proof and the confidentiality, integrity, and availability (CIA) properties of information security. The results indicate that the proposed scheme can effectively meet the requirements for security and privacy preservation.

In recent years, various researches have been conducted in order to find ways to cover a target or groups of targets with priority-based target coverage and sensor deployment mechanisms taking the front seats. However, with these researches, effective target coverage has been a recurrent issue due to various factors like conflict between sensors and excessive waiting time for targets to be covered. In this paper, we proposed an algorithm based on queuing theory in tandem with mobile crowd sensing to tackle these issues. To do this, first, we develop some models which are based on the birth-and-death mechanism (one of the tools in queuing theory) to determine how long a target has to wait, the mean busy period of sensors and mean idle period of sensors. While developing these models, we consider cases where there exist a single sensor and n-sensors in the system. Based on these models, we develop the required algorithm. The simulation result shows that as the number of sensors increases relative to the number of targets, an average time before a target gets discovered is 0.2 s and sensor utilization decreasing toward zero as the number of sensors increases.

The futuristic trend is toward the merging of cyber world with physical world leading to the development of Internet of Things (IoT) framework. Current research is focused on the scalar data-based IoT applications thus leaving the gap between services and benefits of IoT objects and multimedia objects. Multimedia IoT (IoMT) applications require new protocols to be developed to cope up with heterogeneity among the various communicating objects. In this paper, we have presented a cross-layer protocol for IoMT. In proposed methodology, we have considered the cross communication of physical, data link, and routing layers for multimedia applications. Response time should be less, and communication among the devices must be energy efficient in multimedia applications. IoMT has considered both the issues and the comparative simulations in MATLAB have shown that it outperforms over the traditional protocols and presents the optimized solution for IoMT.

Cyber-physical system (CPS), regarded as the next generation of engineered system, has the capability to interact with the real physical world. Applications of CPS span various fields such as medical monitoring, traffic control, and smart grid. With such widespread applications, privacy assurance is becoming more and more important since what the CPS connects are people and the real world. Any leakage of private information will cause serious consequences. In this paper, we focus on enhancing the secrecy of wireless communications in CPS by use of physical layer security techniques. Specifically, we study an amplify and forward (AF) relay network where all devices are equipped with a single antenna. We propose a privacy-enhanced waveform design approach aided by artificial noise (AN) to enhance the communication secrecy in a wireless environment with multipath receptions. First, we consider the case with perfect eavesdropper's channel state information (CSI). We optimize the AF coefficient for forwarding the informationbearing signal and the AN covariance to maximize the achievable secrecy rate. The optimal solution is obtained by solving a series of semidefinite programs. Then, a more practical scenario with imperfect eavesdropper's CSI is studied. We develop a robust waveform design method and obtain the lower bound of the achievable secrecy rate. Numerical results are presented to show the effectiveness of our proposed algorithms.

A typical cyber-physical system (CPS) refers to a system that features a tight integration of computation, networking, and physical elements for interactions between cyber and physical spaces. The Internet of Things (IoT) is considered to be the networking infrastructure of CPS. Applications of CPS cover numerous smart-world research areas that our daily life depends upon, including smart transportation, smart electrical power grid, smart cities, smart medical systems, smart manufacturing systems, and others. While major research on improving the efficiency and reliability of CPS by using advanced information and communication technologies has been conducted, the risks of cyberspace security and privacy breaches in CPS need to be seriously investigated before a massive deployment of CPS technologies can or should be realized.

Cycling is a typical green traffic mode, and takes a growing part of urban traffic volume. Yet limited cyclist behavior models shed light on cases at unsignalized intersections with heterogeneous traffic, where bicycle behavior is characterized by frequent confrontations with other road users (vehicles, bicycles, and pedestrians). This study developed a microscopic simulation model for cyclist behavior analysis at unsignalized intersection with heterogeneous traffic. The cyclist crossing model applied fuzzy logic and social force theory for this purpose. The parameters are either estimated directly based on empirical data or derived indirectly through maximum likelihood estimation. Finally model performance was confirmed through comparisons between estimations and observations on individual trajectory, minimum distances, and average riding speeds of collision avoidance behaviors with different conflicting road users. Simulation results indicated that the model can represent cyclist crossing behavior at unsignalized intersection with heterogeneous traffic as in the real world.

Wireless medical cyber-physical systems are widely adopted in the daily practices of medicine, where huge amounts of data are sampled by the wireless medical devices and sensors, and is passed to the decision support systems (DSSs). Many text-based guidelines have been encoded for work-flow simulation of DSS to automate health care based on those collected data. But for some complex and life-critical diseases, it is highly desirable to automatically rigorously verify some complex temporal properties encoded in those data, which brings new challenges to current simulation-based DSS with limited support of automatical formal verification and real-time data analysis. In this paper, we conduct the first study on applying runtime verification to cooperate with current DSS based on real-time data. Within the proposed technique, a user-friendly domain specific language, named DRTV, is designed to specify vital real-time data sampled by medical devices and temporal properties originated from clinical guidelines. Some interfaces are developed for data acquisition and communication. Then, for medical practice scenarios described in DRTV model, we will automatically generate event sequences and runtime property verifier automata. If a temporal property violates, real-time warnings will be produced by the formal verifier and passed to medical DSS. We have used DRTV to specify different kinds of medical care scenarios and have applied the proposed technique to assist existing wireless medical cyber-physical system. As presented in experiment results, in terms of warning detection, it outperforms the only use of DSS or human inspection, and improves the quality of clinical health care of hospital.

The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.

The papers in this special section examine recent advancements in the area of network big data analysis.

With the widespread applications of Internet of Things (IoT), the emergency response performance for large-scale network packets is facing serious challenge, especially for renewable distributed energy resources monitoring in a smart grid. Therefore, how to improve the real-time performance of the emergency data packets has been a critical issue. Traditional packet scheduling schemes and topology optimization strategies are not suitable for a large-scale IoT-based smart grid. To address this problem, this paper proposes a new packet scheduling scheme named LOES, which first combines the priority-based packet scheduling scheme with local optimization. We exchange local geographic information to reduce the hop counts and distance between distributed source nodes and sink nodes. Each destination node determines the packet scheduling sequence according to the received emergency information. Finally, we compare LOES with first come first serve, multilevel scheme, and dynamic multilevel priority packet scheduling scheme using packet loss rate, packet waiting time, and average packet end-to-end delay as metrics. The simulation results show that LOES outperforms these previous scheduling schemes.

The ultimate objective of medical cyber-physical systems is to enhance the safety and effectiveness of patient care. To ensure safe and effective care during emergency patient transfer from rural areas to center tertiary hospitals, reliable and real-time communication is essential. Unfortunately, real-time monitoring of patients involves transmission of various clinical multimedia data including videos, medical images, and vital signs, which requires use of mobile network with high-fidelity communication bandwidth. However, the wireless networks along the roads in rural areas range from 4G to 2G to low speed satellite links, which poses a significant challenge to transmit critical patient information. In this paper, we present a bandwidth-compliant criticality-aware system for transmission of massive clinical multimedia data adaptive to varying bandwidths during patient transport. Model-based clinical automata are used to determine the criticality of clinical multimedia data. We borrow concepts from DASH, and propose physiology-aware adaptation techniques to transmit more critical clinical data with higher fidelity in response to changes in disease, clinical states, and bandwidth condition. In collaboration with Carle's ambulance service center, we develop a bandwidth profiler, and use it as proof of concept to support our experiments. Our preliminary evaluation results show that our solutions ensure that most critical patient's clinical data are communicated with higher fidelity.

In order to simultaneously power and cool hundreds of thousands of servers, large-scale data centers usually consume several to tens of megawatts of electricity. This enormous electricity consumption leads to considerable concerns in the electricity cost including both electricity bills and carbon tax. To achieve a sustainable data center, many Internet service providers begin to build their own on-site renewable energy plants to help reduce the electricity cost. However, considering the performance constraint of delay tolerant workloads and the lack of future information about the time-varying electricity price, carbon emission rate, and available on-site renewable energy, it is a fairly challenging problem that how to schedule the delay tolerant workloads to reduce the electricity cost of a sustainable data center. To address this challenging optimization problem, this paper proposes an online workload scheduling algorithm CECM based on the Lyapunov optimization framework, which is able to tradeoff between the electricity cost and the performance of delay tolerant workloads without any future information about the time-varying system states. With extensive simulations based on the real-life traces, we show that CECM is able to reduce the electricity cost by 9.26 percent, while still guaranteeing the performance constraint of delay tolerant workloads.

Today, sepsis syndrome is one of the leading cause of death globally, and is of great clinical importance. In this paper, we present a self-adaptively evolutionary sepsis screening system to shorten the time of syndrome detection and improve the positive effect of treatment, with the screening frequency and content can be automatically adjusted according to the current status of the patient. First, we propose a novel graphical computation model named AdapDBN with a clearly defined syntax for the medical knowledge presentation, especially for the presentation of the pathophysiology model of the disease. Then, the semantics of AdapDBN is formally defined for the evolutionary inference of syndrome onset probability. Finally, we demonstrate how to initialize AdapDBN with sepsis-related epidemiologic statics, published clinical research and physician's knowledge and how to incorporate it into existing sepsis screening and decision support flow. We evaluate its effectiveness and superiority with comparisons to existing computation techniques.

The smart phones are used in many aspects of our life, shopping on the Internet, and creating and distributing many types of files. But these devices have many limitations including: short battery life time and limited storage and processing. Mobile Cloud Computing technology can help to overcome these limitations. Offloading technique reduces the power consumption and saves the mobile storage by executing the huge tasks at the cloud. The mobile devices are connecting to cloud service providers using 3G or LTE technologies, which introduces some challenges including, limited bandwidth, cost, and latency. In this paper, we propose efficient and secure mobile cloud computing model based on the Cloudlet concept were the mobile devices users can connect directly to cloud resources using cheaper technologies such as Wi-Fi. Once needed, and only if the service is not available in the cloudlet, the user will be connected to the enterprise cloud. The proposed cloudlet-based model can be used in many applications where security and efficiency is required. It can be used for health applications to save and analyze patients medical records. The simulation results of our model show that it is more efficient and reliable than the other mobile could computing models that don't use the cloudlet

Coordinated multi‐point (CoMP) techniques can be utilised in several wireless systems, such as LTE‐A and fifth generation. However, inter‐user interferences (IEIs) and intra‐user interferences (IAIs) may degrade the quality of service in joint transmission (JT), CoMP systems. To suppress these interferences, the authors design an interference mitigation scheme via interference alignment (IA) and channel diagonalisation (IMS‐IACD), which is implemented through a local two‐step precoder‐and‐receiver design process. First, IA is employed to force the IEIs of each user into the interfering space. Second, the IAIs of each user are effectively suppressed by channel diagonalisation with a selective method. Performance analyses show that the IMS‐IACD is feasible in general JT scenarios. The IMS‐IACD does not contain any iterative process and superabundant information interaction. The structure of receiver generated by the authors’ channel diagonalisation method is quite simple, so the energy consumption can be saved by using this receiver. Moreover, computational complexities of the IMS‐IACD and traditional schemes are in the same order of magnitude, and simulation results illustrate that the IMS‐IACD improves the bit error rates and data rates for users.

Images obtained under low‐light conditions tend to have the characteristics of low‐grey levels, high‐noise levels, and indistinguishable details. Image degradation not only affects the recognition of images, but also influences the performance of the computer vision system. The low‐light image enhancement algorithm based on the dark channel prior de‐hazing technique can enhance the contrast of images effectively and can highlight the details of images. However, the dark channel prior de‐hazing technique ignores the effects of noise, which leads to significant noise amplification after the enhancement process. In this study, a de‐hazing‐based simultaneous enhancement and noise reduction algorithm of are proposed by analysing the essence of the dark channel prior de‐hazing technique and bilateral filter. First, the authors estimate the values of the initial parameters of the hazy image model by de‐hazing technique. Then, they correct the parameters of the hazy image model alternately with the iterative joint bilateral filter. Experimental results indicate that the proposed algorithm can simultaneously enhance the low‐light images and reduce noise effectively. The proposed algorithm could also perform quite well compared with the current common image enhancement and noise reduction algorithms in terms of the subjective visual effects and objective quality assessments.

Many researches show that the power consumption of network devices of ICT is nearly 10% of total global consumption. While the redundant deployment of network equipment makes the network utilization is relatively low, which leads to a very low energy efficiency of networks. With the dynamic and high quality demands of users, how to improve network energy efficiency becomes a focus under the premise of ensuring network performance and customer service quality. For this reason, we propose an energy consumption model based on link loads, and use the network’s bit energy consumption parameter to measure the network energy efficiency. This paper is to minimize the network’s bit energy consumption parameter, and then we propose the energy-efficient minimum criticality routing algorithm, which includes energy efficiency routing and load balancing. To further improve network energy efficiency, this paper proposes an energy-efficient multi-constraint rerouting (E2MR2) algorithm. E2MR2 uses the energy consumption model to set up the link weight for maximum energy efficiency and exploits rerouting strategy to ensure network QoS and maximum delay constraints. The simulation uses synthetic traffic data in the real network topology to analyze the performance of our method. Simulation results that our approach is feasible and promising.

A network virtual reality (VR) engine for interactive analysis and smart city immersive visualization which integrates several latest information technologies, i.e., Web virtual reality (WebVR), Internet of Things (IoT), and three-dimensional (3-D) geographical information system (3-DGIS) with peer-to-peer (P2P) network, has been proposed in this study. It can be used in big spatial data (e.g., remote sensing data) organization and presentation while achieving the online sharing by hash-based P2P networks. In the P2P network, a map, which contains the real geographic space users and the virtual scene user avatar with prep the virtual network nodes, has been made. Moreover, it can also support the following integrated functions of VRGIS: 3-D spatial analysis function, 3-D visualization for spatial information process, and Web engine for 3-D globe and smart big city. To evaluate WebVRGIS system, a user study had been conducted by comparing it with virtual community system. The performance evaluation results had confirmed the higher usability and user satisfaction of the proposed WebVRGIS system.

Because of the absence of central control and limited capabilities of tiny nodes in mobile ad hoc networks (MANETs), these types of networks are always vulnerable to different kind of threats. Threats can be active and passive. Many cryptographic techniques are suggested for securing MANETs from various threats. This paper introduces a new method using cluster heads with cryptographic keys, for each node, in cluster-based MANETs. Cluster heads are selected according to some definite criteria among the nodes in the clusters. Cluster heads keep the private keys for decryption while each member node is assigned a unique public key for encryption purpose. The approach is very flexible as it supports the nodes' migration to other clusters and transfer of control for one cluster head to another. Different scenarios are explained in the paper to tackle the security mechanism and nodes migration to other clusters. Copyright © 2016 John Wiley & Sons, Ltd.

Vehicular ad hoc networks (VANETs) have the potential to transform the way people travel through the creation of a safe interoperable wireless communications network that includes cars, buses, traffic signals, cell phones, and other devices. However, VANETs are vulnerable to security threats due to increasing reliance on communication, computing, and control technologies. The unique security and privacy challenges posed by VANETs include integrity (data trust), confidentiality, nonrepudiation, access control, real-time operational constraints/demands, availability, and privacy protection. The trustworthiness of VANETs could be improved by addressing holistically both data trust, which is defined as the assessment of whether or not and to what extent the reported traffic data are trustworthy, and node trust, which is defined as how trustworthy the nodes in VANETs are. In this paper, an attack-resistant trust management scheme (ART) is proposed for VANETs that is able to detect and cope with malicious attacks and also evaluate the trustworthiness of both data and mobile nodes in VANETs. Specially, data trust is evaluated based on the data sensed and collected from multiple vehicles; node trust is assessed in two dimensions, i.e., functional trust and recommendation trust, which indicate how likely a node can fulfill its functionality and how trustworthy the recommendations from a node for other nodes will be, respectively. The effectiveness and efficiency of the proposed ART scheme is validated through extensive experiments. The proposed trust management theme is applicable to a wide range of VANET applications to improve traffic safety, mobility, and environmental protection with enhanced trustworthiness.

Supporting massive access of machine-type devices in a short period is a critical challenge in machine-to-machine (M2M) communications. We in this paper propose a traffic-aware Access Class Barring (ACB) scheme to improve the scalability of M2M networks. Unlike traditional ACB scheme, our proposed scheme aim at dynamically regulating the parameter of access probability, called barring factor, based on network load, thus accommodating much more M2M devices as well as lowering the access delay. To achieve this goal, we first develop a Markov-Chain based traffic-load estimation scheme according to the collision status. Then, we propose a spectrum of functions to control the barring factor varying with the estimated traffic load. Also provided is a set of simulations results, demonstrating that our proposed traffic-aware scheme significantly outperforms the traditional ACB scheme in terms of not only access success probability, but also average access delay.

Ubiquitous information exchange has motivated wide research interests on device-to-device (D2D) networks, where device nodes can communicate to each other reusing the cellular network's spectrum in an underlay fashion. We in this paper propose the peer-to-peer (P2P) share enabled routing schemes over multi-hop interference-constrained D2D networks, where multiple D2D subscribers attempt to download the common data from multiple distributed D2D servers. We aim at maximizing the average download rate over subscribers while keeping the interferences to cellular network's spectrum under a tolerable level. We focus on the scenario with two subscribers, two file servers, and two cellular users to avoid massive P2P share drastically increasing the interference temperature and crashing the network. Specifically, we develop a Routing scheme with Direct P2P-Share (R-DPS), which allows subscribers distribute their received data to each other. Moreover, we propose a Routing scheme with Coverage-based P2P-Share (R-CPS). The R-CPS scheme makes use of the broadcast nature of wireless channel to assure that each route can cover all subscribers, unlike passing through them in R-DPS scheme. The R-DPS and R-CPS schemes have the potential to enhance the data download rate compared with the approach without P2P support.

Wireless sensor networks (WSNs) have witnessed rapid advancement in medical applications from real-time telemonitoring and computer-assisted rehabilitation to emergency response systems. In this paper, we present the state-of-the-art research from the ubiquity perspective, and discuss the insights as well as vision of future directions in WSN-based healthcare systems. First, we propose a novel tiered architecture that can be generally applied to WSN-based healthcare systems. Then, we analyze the IEEE 802 series standards in the access layer on their capabilities in setting up WSNs for healthcare. We also explore some of the up-to-date work in the application layer, mostly on the smartphone platforms. Furthermore, in order to develop and integrate effective ubiquitous sensing for healthcare (USH), we highlight four important design goals (i.e., proactiveness, transparency, awareness, and trustworthiness) that should be taken into account in future systems.

In long-haul DWDM or elastic subwavelength modulated systems with periodic dispersion compensation and amplification, all-optical communications give rise to severe physical impairments, due to fiber dispersion and nonlinearity, together with noise due to amplified spontaneous emission (ASE), that adversely affect system performance. Many signal processing techniques, including forward error correction, constrained coding, and predistortion/equalization, have been developed to mitigate these physical impairments and fully exploit the system capacity of long-haul DWDM systems. However, existing equalizers either only mitigate linear impairments or are too complex to be useful, such as backpropagation. This paper proposes a model-centric nonlinear equalizer for coherent systems based on a 2D discrete-time model of physical impairments in long-haul time and wavelength channel systems with periodic dispersion compensation and amplification using inverse Volterra theory. Different from backpropagation, which is hard to implement in hardware, this equalizer can function on the most basic discrete-time signal processing device. The nonlinear equalizer is effective in suppressing linear and nonlinear physical impairments in a long-haul fiber-optic communication system, particularly for high launched power levels where fiber nonlinearity dominates.

In long-haul fiber-optic communication systems, the system performance is affected adversely by both severe physical impairments and amplified spontaneous emission (ASE) noise. Constrained coding, which avoids waveforms in the transmitted signal that are more likely to be detected incorrectly, has been proved to be an effective approach to suppress some physical impairments. However, the constrained coding schemes proposed in the literature are limited to the suppression of only some “resonant” sequences and their performance is evaluated in the absence of ASE noise. Various error correction codes also have been developed to reduce errors due to ASE noise but their performance is very vulnerable to the strong nonlinear impairments. This paper develops a novel concatenation scheme with the inner code being a constrained code based on Total Impairment Extent Rank (TIER) and the outer code being a low-density parity-check (LDPC) code. The TIER code ranks the bit patterns by order of all physical impairments imposed on them and constrains the bit patterns with large physical impairments. It is based on a discrete-time analytical model of physical impairments in long-haul fiber-optic communication systems. Compared with the current constrained codes, the TIER code offers more flexibility and better effectiveness. The TIER-LDPC concatenation scheme combines the strength of the TIER code in correcting errors due to physical impairments and that of the LDPC code in correcting memoryless errors due to ASE noise. Simulation results in the presence of ASE noise show that the TIER-LDPC concatenation scheme performs significantly better than the LDPC code alone in the case of severe impairments.

In wavelength-division multiplexed (WDM) systems, system performance is adversely affected by severe physical impairments due to fiber loss, dispersion and nonlinearity. Fiber modeling is a prerequisite for the development of physical impairment mitigation techniques to improve system performance. The distance between two interacting symbols in time and wavelength, i.e., the range of influence (RoI) of each physical impairment, plays an important role in the development of these mitigation techniques. This paper develops a two-dimensional (time and wavelength) discrete-time input-output model of WDM systems based on the Volterra series transfer function (VSTF) method. This model takes into account multiple channel effects, fiber loss, and frequency chirp, which are ignored in the current literature. Furthermore, with this model we define coefficients that capture intersymbol interference (ISI), self phase modulation (SPM), intrachannel cross phase modulation (IXPM), intrachannel four wave mixing (IFWM), cross phase modulation (XPM) and four wave mixing (FWM) to characterize the impact of these impairments individually on the system output. By investigating these impairment coefficients, the RoIs for different physical impairments are determined.

To mitigate various physical impairments of long-haul dense wavelength division multiplexing (DWDM) systems and exploit their system capacity, there is a need to develop a two-dimensional (time and wavelength) discrete-time input-output model which can become the foundation of signal processing for optical communications. As the first step, this paper develops a model for single channel multipulse multispan systems based on the Volterra series transfer function (VSTF) method. This model is suitable for high-bit-rate time-division multiplexing (TDM) transmission in the pseudo-linear regime and is easily extendable to the multichannel case. We overcome the well-known triple integral problem and reduce it to a simple integral. This model takes into account fiber losses, frequency chirp and photodetection, which are ignored in the literature. Furthermore, with this model we introduce the intersymbol interference (ISI), self phase modulation (SPM), intrachannel cross phase modulation (IXPM) and intrachannel four wave mixing (IFWM) coefficients to characterize the impact of these effects on the system performance. The model is in excellent agreement with SSF (split-step Fourier) simulation. To illustrate its application, we develop a constrained coding scheme based on the system model to suppress the impact of various impairments.