Satellite communications have been considered a key part of global connectivity, effectively supporting diverse applications such as the Internet of Things (IoT) and real-time communication services. However, security-sensitive devices face significant challenges due to the threat of eavesdropping satellites, which compromise data confidentiality. Existing approaches often rely on deterministic models and fail to account for the stochastic nature of eavesdropping threats and the dynamic demands of satellite networks, limiting their applicability in practical scenarios. To address these challenges, this work proposes a novel secure access strategy for satellite mobile edge computing (SatMEC) systems, integrating a stochastic risk assessment model and an evolutionary game-theoretic framework. The proposed solution leverages a probabilistic model to evaluate the spatial distribution of eavesdropping satellites, quantifies the eavesdropping risk via the concept of eavesdropping capacity, and incorporates a dynamic service selection strategy that balances secrecy capacity and queuing delay.Furthermore, a distributed algorithm is developed to enable IoT devices to select service satellites based on real-time utility optimization adaptively. Extensive simulation experiments validate the effectiveness of the proposed strategy, demonstrating its ability to improve system security, balance the network load, and enhance overall performance in large-scale and dynamic satellite network environments. The results highlight the reliability and scalability of the proposed solution, making it a practical approach for secure and efficient access in LEO satellite networks.

In this paper, we propose a novel cooperative relay-based resource and learning optimization (CRRLO) scheme that extends device connectivity, balances learning contributions, and coordinates communication resources to mitigate the negative impact of intermittent participation on FL performance caused by unreliable communication in Industrial Internet of Things (IIoT) environments. After local training, a cooperative aggregation stage is proposed, where fully connected device-to-device (D2D) relaying enables devices with failed device-to-server (D2S) transmissions to still contribute to the global model, while avoiding the transmission burden and relay selection issues associated with single-relay strategies. To further ensure unbiased aggregation, we produce reliability-driven aggregation weights to calibrate each device’s contribution to the global update. We then formulate a joint optimization problem aimed at improving FL convergence rate under communication and resource constraints by co-optimizing blocklength, transmission power, and aggregation weights. An iterative algorithm is designed to determine blocklength bounds, a low-complexity method is developed for power optimization, and a convex relaxation approach is adopted for weight adjustment. These subproblems are alternately solved using a block coordinate descent (BCD) method. Simulation results demonstrate that the proposed CRRLO scheme significantly accelerates convergence and improves test accuracy by up to 24.33% compared to baseline schemes under high transmission error probability.

In this paper, we propose a novel federated learning (FL) scheme, called Device-Cooperative FL (DCFL), to mitigate the negative impact of probabilistic transmission errors on FL performance and improve the convergence rate in Industrial Internet of Things (IIoT) environments with unreliable wireless communication. After local model training, a cooperative local update stage is introduced, utilizing a fully connected device-to-device (D2D) relaying scheme to ensure that model parameters from devices with failed device-to-server (D2S) transmissions are included in the global aggregation, while avoiding the need for real-time relay selection. To address inconsistent participation due to varying communication conditions, we introduce local update weights that reflect each device’s transmission reliability, ensuring balanced contributions to the global model. Simulation results validate that the proposed DCFL scheme can accelerate convergence and improve test accuracy by up to 20.26% compared to baseline schemes under conditions of high transmission error probability.

In this paper, we propose a novel channel access and power control scheme for smart grids in communication networks. The scheme is named digital twin-based memory recall optimization (DMRO), which aims to extract meaningful patterns from noisy network traffic measurements and support more sophisticated decision-making processes for optimizing channel access and power control for smart grids. Specifically, we design a pattern extraction method that minimizes the Frobnius norm between the collected measurements and the expected k-rank approximation of the measurements in order to extract useful information. Then, considering the interference and signal-to-interference-plus-noise ratio (SINR) constraints in the wireless environment, we develop a digital twin-based distributed channel access and power control scheme to improve the latency taming and energy utilization efficiency of the phasor measurements units (PMU). We consider both the real-time traffic prediction and the paired optimization scheme on the digital twin side, and utilize memory recall to enhance local model robustness to optimize from a more diverse set of situations by replaying underrepresented experiences. Simulation results demonstrate that the proposed DMRO scheme can achieve high traffic prediction accuracy and improve the latency taming and energy utilization efficiency even increasing industrial channel interference or the number of PMUs.

In this paper, we propose an intelligent congestion control approach for the QUIC protocol based on digital twin technology, aimed at improving adaptability and transmission efficiency in dynamic network environments. By incorporating actor-critic learning, the proposed approach adaptively adjusts congestion control parameters to meet the demands of different application scenarios, thereby optimizing QUIC’s performance under varying network conditions. Leveraging the capabilities of digital twins, the approach builds a virtual model that accurately replicates the physical network. This digital environment supports rapid data collection, real-time congestion monitoring, and comprehensive analysis of historical transmission patterns and state variations. These features enable proactive parameter tuning and timely strategy updates to maintain optimal control performance. Simulation results validate the effectiveness of the proposed approach, showing improvements in data throughput, reduced latency, faster convergence of the learning algorithm, and enhanced end-to-end reliability, particularly in 5G network settings.

Deep learning plays increasingly important role in future wireless network management and optimization. Existing training methods such as label-based supervised learning and label-free learning have inherent limitations. The performance of supervised learning is limited by labels, while label-free training methods require extensive exploration. To address these limitations, this paper proposes a novel mixture of gradients (MoG) method, which integrates gradients from different sources within the training process in order to improve the convergence performance of neural networks (NNs). Particularly, MoG is a modular, plug-and-play solution requiring no structural modifications to existing NNs. Its implementation necessitates only minor modifications to the loss function, where the label-based supervised loss is combined with a label-free loss through weighted summation. The label-free loss can be either unsupervised loss or reinforcement learning loss. This flexibility allows seamless integration into nearly all NN-based methods, making it applicable to a wide range of wireless optimization problems with minimal implementation cost. Extensive simulations across multiple classic wireless scenarios demonstrate that MoG can significantly enhance the performance of NN decision-making, leading to higher transmission rates.

In this work, we investigate the Age of Information (AoI) in aerial intelligent reflecting surfaces (IRSs) assisted covert data collection in Internet of Things (IoT) networks. Operating with the autonomous aerial vehicle (AAV) and IRS can improve the data transmission covertness, as well as the data freshness by reconstructing the wireless propagation environment. Specifically, we consider a scenario in which ground IoT sensors transmit confidential information to the legitimate receiver (Bob), through IRS assisted AAV relay, at the same time, an eavesdropper passively listens to and intercepts the confidential information sent by sensors. To minimize the average AoI of the system, a joint AAV trajectory and IRS phase shift optimization problem is formulated under the constraints of covertness requirement. The minimum error detection probability and optimal detection threshold are first derived at Willie, which represents the worst case situation for the legitimate transmission. The constructed problem is a mixed integer programming NP-Hard problem, which is more complex using traditional convex optimization methods. Therefore, the online learning, i.e., deep reinforcement learning is leveraged to obtain the near-optimal solution. Details, the deep Q network (DQN) and deep deterministic policy gradient (DDPG) methods are utilized. Numerical results show that deep reinforcement learning can improve the information freshness of the system by reasonably designing AAV trajectory and IRS phase shift under a given covertness constraint.

The widely deployment of infrastructure and wireless interfaces increases industrial IoT (IIoT) vulnerability to network intrusions, highlighting the requirements for robust network intrusion detection systems (NIDSs). Although deep learning (DL) provides a promising solution for NIDSs, it remains susceptible to adversarial attacks as minor input perturbations can lead to major misclassifications. In this paper, we propose a packet-level adversarial traffic generation (PATG) approach for attacking NIDSs in IIoT, which not only aligns with domain constraints but also evades various DL-based NIDSs. Particularly, we introduce a reversible abstract traffic representation to ensure that the original traffic can be effectively modified while preserving its functionality. We propose a packet-level generative adversarial networks to craft adversarial traffic by learning benign data distribution in feature space and simulating evasion behaviors, which escapes the DL-based NIDSs. We further design two defense schemes to enhance system resilience against proposed adversarial attacks. We evaluate PATG on nine state-of-the-art DL-based NIDSs in the Kitsune and CICIoT23 datasets. Experimental results demonstrate that PATG can achieve a maximum evasion increase rate of 99% with cost-effective execution, while the defense methods significantly mitigate the impact of the adversarial attacks.

In this paper, we propose a joint non-line-of-sight (NLoS) predictive beamforming and power allocation (JNPB-PA) scheme to enhance the power efficiency of the road side units in the integrated sensing and communication (ISAC)-assisted vehicular networks. This scheme decouple the spatial and amplitude components in power allocation by exploiting angular domain discretization of a novel modulation technique–spatially-spread orthogonal time frequency space (SS-OTFS). Specifically, we first develop an auxiliary target method to achieve predictive beamforming in NLoS scenarios, which initially determines the power allocation vector’s non-zero positions corresponding to discrete angles of the vehicles. Then, we further refine the power allocation by solving a multi-objective optimization problem (MOP) aimed at minimizing both the age of information (AoI) for communication and the Cramér-Rao bound (CRB) for sensing. A low-complexity algorithm based on the proposed reconstruction-contraction-constraint (RCC) approach is developed to solve the formulated MOP based on its inherent features. Simulation shows that our proposed JNPB-PA scheme can achieve higher power utilization rate, lower AoI, and lower CRB in comparison with benchmark schemes. Besides, RCC solves the formulated MOP more efficiently by avoiding iterative searching of traditional methods.

In this paper, we investigate the sensing and communication (S&C) trade-off in the integrated sensing and communication (ISAC)-assisted vehicular network with spatially spread orthogonal time frequency space (SS-OTFS) modulation technique, where power allocation is the trigger leading to S&C performance shift. We tailor S&C metrics specifically for the vehicular network where information freshness and sensing accuracy are critical due to safety concerns, indicated by age of information (AoI) and Cramér-Rao bound (CRB), respectively. Then we propose an AoI-and-CRB-based power allocation (ACPA) scheme and develop a reconstruction-contraction-constraint (RCC) approach to derive the non-dominated solutions, which delineate the S&C trade-off. Simulation shows that our proposed ACPA scheme can identify the S&C performance frontier of the system, and the RCC approach is more efficient than the traditional non-dominated sorting genetic algorithm II (NSGA-II). In addition, the intrinsic mechanism of how power allocation affects S&C performances in the SS-OTFS-enabled ISAC system is analyzed.

In this paper, we propose a novel intrusion detection scheme within smart grid networks, which is named digital twin-based multi-kernel knowledge replay (DtMKR) scheme. The scheme is designed to improve resilience against the noise and interference, and efficiently address the clustering of diverse and multi-sourced phasor measurement units (PMUs) packets. Specifically, we create channel-vectors to represent the packet features using the power gain and delay spread properties of the channel impulse response derived from the received packets. Then, we investigate the DtMKR scheme to alleviate the impact of noise and interference effects and enhance the precision of the malicious PMU packets detection from benign ones without the need for a comprehensive pre-established database of channel features for all PMUs in the network. In the scheme, the designed multi-kernel enabled Markov decision process (MDP) clustering functions are implemented to map the channel-vectors into a new feature space, thereby the dispersive effects on the channel-vectors are minimized. In addition, we consider both the realtime packet clustering and the paired learning scheme on the digital twin side, and utilize memory recall to mitigate the model overestimation problem and enhance local model robustness to optimize from a more diverse set of situations by replaying underrepresented experiences. Simulation results demonstrate the efficacy of the proposed DtMKR scheme in accurately identifying malicious packets originating from PMUs attackers, distinguishing them from benign traffic, and mitigating the impact of transmission impairments typical in environment.

In this paper, we propose a novel channel access and power control scheme for industrial IoT environments within heterogeneous networks. This scheme, termed Digital Twin-Based Prediction and Optimization (DTPO), extracts significant patterns from noisy network traffic data to enhance decision-making for channel access and power control. Specifically, we develop a pattern extraction method that minimizes the Frobenius norm between the collected traffic measurements and their expected k-rank approximation, thereby isolating useful information. Taking into account the interference and signal-to-interference-plus-noise ratio (SINR) constraints prevalent in wireless settings, we propose a digital twin-enabled distributed channel access and power control scheme to better manage latency and enhance energy efficiency for IoT devices. We consider both the real-time traffic prediction and a coupled optimization process on the digital twin side. Additionally, we employ a memory recall technique to improve the robustness of local models, allowing for optimization across a broader range of scenarios by revisiting underrepresented data. Simulation results demonstrate that the DTPO scheme can achieve high accuracy in traffic prediction and effectively enhances latency management and energy efficiency, even with increased industrial channel interference or a growing number of IoT devices.

The integrated of the fifth generation (5G) and time-sensitive networking (TSN) is a promising approach to meet the requirements of deterministic forwarding with extremely low latency and high flexibility for the Industrial Internet of Things (IIoT). However, due to the large dissimilarity of protocol stacks, the 5G system normally serves as a logical bridge for TSN, performing hold-and-forward operations in Base Stations (BSs) under the current 5G-TSN frameworks. To provide ubiquitous and seamless connectivity for IIoT devices, this paper focuses on the integrated enhancement of 5G and TSN by optimizing the scheduling of time-sensitive flows with end-to-end latency of 5G-TSN transmission taken into consideration. Specifically, we propose a novel architecture named GF-CQF, which combines 5G Grant-free (GF) access with TSN Cyclic Queuing and Forwarding (CQF). Subsequently, the distributed flow scheduling problem based on GF-CQF architecture is established due to the high timeliness demands. To alleviate the impact of the uncertainty inherent in 5G channels on end-to-end deterministic transmission, a feature-aware decentralized real-time scheduling (FDRS) policy based on Multi-agent Reinforcement Learning is proposed. FDRS allows each agent at BS to adaptively allocate TSN injection slots for flows mainly based on dynamic 5G transmission performance and TSN network resource state so that TSN queue overflow can be avoided and flow delay constraints can be guaranteed. Simulations show that FDRS offers superior scheduling capabilities under the limited TSN queue resources.

Artificial Intelligence-Generated Content (AIGC) is a rapidly evolving field that utilizes advanced AI algorithms to generate content. Through integration with mobile edge networks, mobile AIGC networks have gained significant attention, which can provide real-time customized and personalized AIGC services and products. Since blockchains can facilitate decentralized and transparent data management, AIGC products can be securely managed by blockchain to avoid tampering and plagiarization. However, the evolution of blockchain-empowered mobile AIGC is still in its nascent phase, grappling with challenges such as improving information propagation efficiency to enable blockchain-empowered mobile AIGC. In this paper, we design a Graph Attention Network (GAT)-based information propagation optimization framework for blockchain-empowered mobile AIGC. We first innovatively apply age of information as a data-freshness metric to measure information propagation efficiency in public blockchains. Considering that GATs possess the excellent ability to process graph-structured data, we utilize the GAT to obtain the optimal information propagation trajectory. Numerical results demonstrate that the proposed scheme exhibits the most outstanding information propagation efficiency compared with traditional routing mechanisms.

In this paper, we propose a reliable federated learning (FL) scheme for vehicular communication networks. The scheme is named intelligent vehicle selection and resource optimization (IVSRO), which aims to improve the federate learning reliability by reducing the probability of incorrect packet transmission in mobility scenario, and determining the most suitable vehicle for learning based on the incorrect packet probability. Specifically, we introduce a FL model for the vehicular communication network and analyze the probability of incorrect packet transmission caused by dynamic channel changes under this network. In consideration of FL convergence accuracy, an optimization problem is formulated to minimize the incorrect packet transmission rate, which is achieved through selecting the optimal connected vehicles from the training set, allocating transmission power and wireless spectrum resources to the selected vehicles. By employing convergence analysis and determining the optimal power for each selected vehicle, the proposed optimization problem can be handled using a bipartite matching algorithm. Simulation results show that the identification accuracy of the proposed IVSRO scheme is higher than that of existing baseline schemes. The results of this study demonstrate how the proposed IVSRO scheme improve the reliability of the FL scheme in vehicular communication networks while considering the varying channel conditions and proper vehicle selection, making it valuable for FL implementations in the domains of intelligent transportation and road safety management.

Empowered by the large amounts of sensor data in the Industrial Internet of Things, data-driven fault diagnosis has a pivotal role in improving equipment reliability in harsh industrial environments. To enhance diagnostic performance under unknown operating conditions, transfer learning-based cross-domain fault diagnosis has been emerging. However, diagnostic models are prone to overfit to the source domain due to the lack of sample diversity when only a single-source domain is available. Moreover, significant domain shifts between the single-source domain and multiple unknown target domains may degrade the generalization performance on the unknown domains. To address these challenges, we propose a multipseudo domains augmented adversarial domain-invariant learning (MDA-AD) for cross-domain fault diagnosis. First, we design a multipseudo domain generator, where interdomain diversity constraints and manifold-semantic consistency constraints are implemented to avoid overfitting on the source domain by generating diverse and representative pseudo samples. Subsequently, to alleviate the domain shift, we design an adversarial domain-aware classifier that extracts domain-invariant features by introducing an adversarial paradigm between a feature extractor and a domain discriminator. Finally, to further enhance the diversity of the pseudo domains, we implement a diversity-consistency constrained domain-invariant training strategy. The experimental results, obtained through comparative studies, hyperparameter influence analysis, and visualization on two bearing data sets, affirm the superior diagnostic performance of MDA-AD in a single-source domain.

In this paper, we propose a novel age-of-information (AoI)-aware waveform design scheme for the cooperative joint radar-communications (JRC) system, called AoI-aware Online Prediction (A-OnP) scheme. To be specific, we optimize the power allocation of the orthogonal frequency division multiplexing (OFDM) signal. We aim to maximize the radar mutual information (RMI) with considering the communication data rate (CDR) and AoI performance. Specifically, we design a cognitive operating framework for the JRC system, with a particular emphasis on the closed-loop signal processing for online prediction of the radar target scattering coefficient (TSC). Then, considering the obtained TSC prediction result and corresponding communication performance requirement, we optimize the power allocation of the transmit waveform and the signal-to-interference-plus-noise ratio (SINR) threshold of the communication users. Accordingly, we propose a constraints-splitting coordinate descent (CS-CD) method to solve the formulated non-convex problem by strategically splitting the sum-constraints and assign a quota to each channel, where the allocation criteria is automatically decided during iteration. Simulation results demonstrate that, the cooperative radar-centric communication-constrained (RC-CC) waveform outperforms the separately optimized radar-optimal plus communication-optimal (RO-CO) waveform. Additionally, the A-OnP scheme can increase RMI while meeting the communication CDR and AoI requirements.

In this paper, we investigate dense short packet forwarding for clustering-based massive Internet-of-Things (mIoT). The objective is to support the data forwarding with minimal communication overhead while satisfying the differentiated latency constraints from the transport layer perspective. To this end, we propose a dense packet assembling scheme, named ShuttleBus, for forwarding devices in mIoT to achieve effective data merging. The assembling scheme is designed based on the stream multiplexing mechanism of the Quick UDP Internet Connection (QUIC) protocol. With ShuttleBus, the payload data sent from IoT devices are extracted as independent frames belonging to different data streams. The ShuttleBus can bundle data frames from multiple streams into a single packet while ensuring data integrity of these streams. Furthermore, we develop a resilient packing mechanism in packet assembling to merge data received from IoT devices within a cluster. In addition, a latency-oriented scheduling mechanism for backlogged QUIC data is established to guarantee satisfactory delivery of diverse transmission tasks. To accommodate the dynamic network environment, we tailor a learning-based algorithm to determine the optimal packet assembling time adaptively. We evaluate the performance of ShuttleBus under various network load conditions. Both analytical and experimental results demonstrate that the proposed scheme significantly reduces communication overhead and enhances data delivery performance under stringent latency constraints.

In this paper, we propose a novel orthogonal frequency division multiplexing (OFDM) waveform power allocation approach in the spectrum-sharing dual-functional radar-communication (DFRC) systems, with a particular emphasis on improving radar recognition performance while considering the specific communication requirements of individual users. Specifically, the radar mutual information (RMI) is maximized in terms of allocating the radar power to the OFDM subcarrier within the limits of the power constraints and meeting the age of information (AoI) expectation requirements. By considering the impact of radar interference, we measure the AoI performance of individual users using the transmission outage probability. Then, an iterative constraints-splitting (ICS) method is developed to find the optimal radar power allocation results from the formulated non-convex problem by transforming it into an equivalent convex problem using an iteratively determined factor. Simulation results demonstrate that the proposed individual user AoI preference-based approach can improve RMI performance while meeting the AoI communication requirements of individual users. Additionally, higher RMI and more stable AoI performance can be achieved while maintaining total communication performance by allocating more sub-carriers to fewer users.

In this paper, we propose a novel traffic prediction scheme for multiple radio access technology (multi-RAT) heterogeneous network. The scheme is named user-Cybertwin asynchronous learning (UCAL), which aims to extract meaningful patterns from noisy network traffic measurements and mitigate the impact of highly nonstationary measurements for ensuring the prediction accuracy. Specifically, we design a pattern extraction method that minimizes the Frobnius norm between the collected measurements and the expected k-rank approximation of the measurements in order to extract useful information. Then, by transforming the conventional long short term memory (LSTM) model into a nonlinear state space and incorporating Gaussian noise, we develop an online LSTM algorithm to adapt fast to changing environments. As a result, the parameter updating of the new online LSTM model can keep up with data changes while capturing complicated and nonlinear relationships among measurements. We consider both the surrounding environment conditions on the mobile user side and end-to-end link conditions on the Cybertwin side, and iteratively update the model parameters in both Cybertwin and MU. Simulation results demonstrate that the proposed UCAL scheme can achieve high traffic prediction accuracy in comparison to existing schemes. It can also significantly improve the efficiency in maintaining the prediction accuracy even when the dimension of traffic measurements increases.

High-speed train (HST) communications are envisioned to provide diversified broadband services by integrating with 5G while the high mobility induces fast-fading channels and potentially degrades the system performance. To address this issue, we investigate an HST communication network empowered by intelligent reflecting surfaces (IRSs) with the multiple-input–multiple-output (MIMO) technology. Statistical channel state information (CSI) is exploited to mitigate the impact of the fast time-varying fading. The transceiver beamforming vectors and the IRS phase shift matrix are optimized to improve the system performance in terms of the outage probability and the ergodic capacity considering the channel uncertainty. First, we derive the analytical expression of the outage probability with a generalized Marcum$Q $-function. Then, we develop an alternating optimization algorithm to minimize the outage probability by capitalizing on the generalized eigenvalue–eigenvectors. Moreover, the ergodic capacity is deduced with statistical CSI and then optimized by analyzing the upper bound with Jensen’s approximation. Extensive simulations show that simulation results are consistent with the theoretical analysis, and the IRS-assisted system significantly outperforms the system without IRS in terms of the outage probability and the ergodic capacity.

In this paper, we propose a novel channel-aware latency taming scheme, called Optimal Transmission Latency Taming (OTLT), to detect hidden channel state and tame the distribution tail of the packet sojourn time in Industrial Internet of Things (IIoT) devices. Specifically, we design a forward algorithm based on a hidden semi-Markov model to detect the hidden channel state, with a particular emphasis on the state sojourn duration, and to calculate the corresponding channel access probability. Then we develop a time-sensitive model to investigate the minimum sojourn time a packet spends in the IIoT device before leaving successfully. With the obtained channel access probability, the first passage probability of the proposed model is explored to find the maximum probability of a packet being successfully transmitted in a given back-off sojourn duration (BSD). The distribution tail of the packet sojourn time can be tamed by minimizing the cumulative summation of each BSD in consideration of the quadratic penalty latency constraints. Simulation results demonstrate that, in the industrial environment, the OTLT scheme can keep the packet’s sojourn duration within a quantifiable limit and variance. It can also obtain considerably efficient control over packet transmission latency in a time-varying wireless propagation channel even with the increasing number of IIoT devices.

In this paper, we propose a two-level adaptive resource allocation (TARA) framework to support vehicular safety message transmissions. In particular, three types of safety messages are considered in urban vehicular networks, i.e., event-triggered messages for urgent condition warnings, periodic messages for vehicular status notifications, and messages for environmental perception. Roadside units are deployed for network management, and thus messages can be transmitted through either vehicle-to-infrastructure or vehicle-to-vehicle connections. To satisfy the requirements of different message transmissions, TARA framework consists of a group-level resource reservation module and a vehicle-level resource allocation module. Particularly, the resource reservation module is designed to allocate resources to support different types of message transmissions for each vehicle group at the first level. To learn the implicit relationship between the resource demand and message transmission requests, a supervised learning model is devised in the resource reservation module, where to obtain the training data we further propose a sequential resource allocation (SRA) scheme. Based on historical network information, SRA scheme offline optimizes the allocation of sensing resources, i.e., choosing vehicles to provide perception data, and communication resources. With resources reserved for each group, the vehicle-level resource allocation module is then devised to distribute specific resources for each vehicle to satisfy the differential requirements in real-time. Extensive simulation results demonstrate the effectiveness of TARA framework in terms of the high packet delivery ratio and low latency for message transmissions, and the high quality of collective environmental perception.

To realize secure communication and prevent eaves-droppers from detecting the existence of communication activities, covert communication has attracted substantial research interests. In this paper, we propose a dynamic spectrum control (DSC)-assisted scheme to achieve covert and reliable data transmission. Specifically, by constructing time-frequency division channels, the proposed DSC-assisted scheme generates sequences with iterative and orthogonal transformations. Authorized users can orderly occupy different frequency slots in each time slot under the guidance of these sequences, thus achieving simultaneous data transmission without interfering with each other. Then, the covert performance of the proposed transmission scheme is analyzed to provide the closed-form expressions of covert transmission rate and the reliable transmission probability. Simulation results are provided to validate the accuracy of the theoretical analysis and demonstrate that the proposed scheme can achieve better covert and reliable transmission performances when compared with the existing scheme.

Unmanned aerial vehicle (UAV)-assisted wireless powered sensor networks (WPSNs) have emerged as a promising paradigm for charging sensor nodes' batteries in remote areas. However, the sum-throughput of overall sensor nodes can dramatically decrease due to their long-distance transmission to the UAV. In this paper, we propose a joint distributed beamforming and backscatter cooperation (BC) scheme to enhance the sum-throughput of UAV-assisted WPSNs with various types of sensor nodes. In particular, we consider the BC mechanism which leverages other types sensor nodes with constructive multi-path signals to enhance the long-distance transmission of same-type sensor nodes. We maximize the sum-throughput by jointly optimizing the distributed backscattering, distributed beamforming and time allocation. The sum-throughput maximization problem is difficult to be solved directly due to the coupling among optimizing variables. We decompose the problem into a BC subproblem and a time allocation subproblem, and propose a two-step scheme to solve them. First, for the BC subproblem, we derive closed-form low-complexity distributed beamforming solutions and distributed backscattering solutions to maximize the signal-to-noise ratios of the same-type sensor nodes. Second, for the time allocation subproblem, we derive the closed-form solutions according to KKT conditions. Simulation results are provided to demonstrate that the proposed joint distributed beamforming and BC scheme can increase the sum-throughput as compared to conventional distributed beamforming schemes.

Recent developments in industrial wireless sensor networks (IWSNs) have revolutionized industrial automation systems. However, harsh industrial environment poses great challenges to a time-critical and reliable wireless communication. For instance, effects of multipath fading, noise and co-channel interference can have unpredictable and time-varying impacts on the propagation channel, leading to the failure of on-time packet delivery. To address this problem, in this paper we propose a channel-based Optimal Back-off Delay Control (OBDC) scheme which can minimize the total time a packet spends in the sensor node (TSN) by assessing the features of a generic wireless channel. Specifically, we first explore the channel impairments by investigating the probability density function (PDF) of the level crossing rate (LCR) of the received signal in the industrial wireless environment. Then, with the obtained channel assessment results, we develop a phase-type semi-Markov model to investigate the probability distribution of the back-off delay of a packet in the sensor node (SN). The probability distribution of the back-off delay can be further substituted with TSN according to the queuing theory. The proposed OBDC scheme examines the Kullback-Leibler (KL) divergence between the obtained distribution of TSN and the packet arrival rate, and reduces the TSN according to an objective function which is constantly renewed in every transmission round with regard to a delay constraint. The simulation results show that the OBDC scheme can reduce TSN and guarantee to keep the TSN in an acceptable range even though the wireless channel is impaired by interference effects. It also shows that the OBDC scheme can reduce the proportion of packets meeting their deadline to the total packets in transmission when the number of SN and LCR changes

Industrial wireless sensor networks (IWSNs) promote innovations in the industry such as structural status mapping, instrument fault diagnosing, and oriented automation system associating. However, due to the shared nature of the wireless propagation environment, the emerging sensor nodes (SNs) with wireless properties are vulnerable to external malicious attacks. The security threats, especially Sybil attacks, impose great difficulties in fulfilling quality requirements of industrial applications. What is more complicated is that the harsh industrial environment brings about new challenges which can degrade the accuracy of detecting Sybil threats. In this paper, we focus on how to detect the malicious packets transmitted from Sybil attackers without adding extra authentication overhead into the transmission frame. We develop a multi-Kernel-based expectation maximization (MKEM) scheme to detect Sybil attacks in IWSNs. Instead of directly investigating the radio resource of SNs, we produce channel-vectors which are extracted from the power gain and delay spread of the channel impulse response obtained from the received packets to represent each SN. Specifically, a kernel-oriented method is designed to discriminate the malicious packets from benign ones without establishing a pre-defined database of channel features of all SNs. Meanwhile, we allocate different kernel weights to the proposed kernels and combine them to improve the discrimination ability of the scheme. Moreover, a kernel parameter optimization method is developed to regulate each kernel weight and parameter to reduce the effects of transmission impairments in IWSNs. To avoid poor detection accuracy when the number of Sybil attackers increases, we use the gap statistical analysis method to verify and EM method to summarize the detection results. The simulation results show that the proposed MKEM scheme can achieve high accuracy on detecting malicious packets transmitted from Sybil attackers from benign ones, and tolerate the effects of transmission impairments in the industrial environment. Moreover, the MKEM scheme can guarantee the detection accuracy even if the number of Sybil attackers increases.

Industrial Wireless Sensor Networks (IWSNs) improve the transmission precision of control signaling as well as contributing to the real-time data monitoring and instrument fault diagnosing throughout the manufacturing production. However, wireless channel effects, such as multipath attenuations, noise and co- channel interference, may have unpredictable and time-varying impacts on keeping packets transmission delay. To address this issue, we propose a Channel-based Sampling rate and Queuing state Control (CSQC) scheme to minimize the packet transmission delay in IWSNs. Specifically, we explore the rapid fading characteristics of the industrial wireless channel by studying the level crossing rate (LCR). We develop a continuous-time Markov model to evaluate the packet sojourn time and design an expectation-maximization (EM) algorithm to timely calibrate the transition rate in the model. Finally, we optimize the sensor sampling rate and queuing state to minimize the packet queuing delay in IWSNs. Simulation results show that the CSQC scheme has lower delay than IEEE 802.15.4 standard does under varying interference effects.

Industrial Wireless Sensor Networks (IWSNs) integrate various types of sensors to measure and control industrial production. However, the unattended open environment makes IWSNs vulnerable to malicious attacks, such as Sybil attacks, which may degrade the network performance. In addition, multipath distortion, impulse noise and interference effects in the harsh industrial environment may influence the accuracy of attack detection. In this paper, we propose a Sybil detection scheme based on power gain and delay spread analysis by exploiting the spatial variability from their channel responses. Specifically, we utilize channel-vectors to represent the sensor features based on the power gain and delay spread extracted from channel response. Furthermore, we develop a kernel-oriented method to distinguish Sybil attackers from benign sensors by clustering the channel-vectors. In addition, to alleviate the impact of industrial noise and interference effects, we design a multi-kernel based fuzzy c-means method to map the extracted channel-vectors into a new feature space such that the dispersive effects on the channel-vectors can be reduced. We also propose a parameter selection method to optimize the employed kernels. The simulation results show that the proposed multi-kernel scheme can achieve high accuracy in detecting the packets from Sybil attackers, and tolerate the dispersive attenuation and interference effects in the industrial environments.

Localization in Industrial Wireless Sensor Networks (IWSNs) promotes innovations in manufacturing applications, such as structural status mapping, instrument fault diagnosing and oriented automation system associating. However, dispersive distortion, impulse noise and interference effects causing unpredictable and time-variant effects of the signal, decrease the localization accuracy in harsh manufacturing environments. In this paper, we propose a noise reduction localization scheme in IWSNs, called Support Vector Semidefinite (SVSD), based on practical industrial wireless channel measurements. We introduce an e-insensitive error function to evade the effects of impulse noise and interference by applying a new statistical path-loss model obtained from the measurements. We further relieve the noise effects by estimating the boundaries of the sensor locations before addressing the localization. Considering the boundaries, we obtain the sensor locations by utilizing semidefinite programming (SDP) relaxation. Simulation results show that the SVSD scheme provides higher location estimation accuracy than the SDP scheme under varying noise effects.

It has been well recognized that channel state information (CSI) feedback is important for dowlink transmissions of closed-loop wireless networks. In this paper, we investigate the CSI feedback rate control problem in the analytical setting of a game theoretic framework, where a multiple-antenna base station (BS) communicates with a number of co-channel mobile stations (MS) through a minimum mean square error (MMSE) precoder. Specifically, we present a non-cooperative feedback-rate control game with price (NFCP) over orthogonal feedback channels with a total bandwidth constraint. The game utility is defined as the performance gain by CSI feedback minus the price as a linear function of the CSI feedback rate, subject to an overall bandwidth constraint. The existence of the Nash equilibrium of such a game is investigated. Simulation results show that the distributed game approach results in close optimal performance compared with the centralized scheme.

Joint transmitter and receiver design problem for frequency-selective, time-invariant fading channels are studied. The authors first propose a simple approximate maximum likelihood serial decision-feedback equaliser (A-ML-SDFE) through DFE, a Gaussian approximation, a pre-whitening filter and a matched filter. Secondly, assuming full channel knowledge available at the transmitter side, the authors perform pre-equalisation in a downlink scenario by moving the decision-feedback part of the A-ML-SDFE to the transmitter. The proposed A-ML-SDFE achieves much better performance than linear minimum mean square error (MMSE) and MMSE-DFE with a lower complexity. The pre-equaliser further improves the system performance at low signal-to-noise ratio with a reduced receiver complexity.