Clock synchronization is a crucial requirement for coordinated activities in distributed networks. As an effective solution, Precision Time Protocol (PTP) is tailored to provide tight synchronization which, however, suffers from packet delay variation (PDV). Therefore, it is a challenging task to mitigate the uncertainties of PDV so that clock skew and offset can be estimated with higher accuracy and robustness. Under the assumption of delay symmetry, PDV is caused by unavailable prior information about the statistical distributions of stochastic delay. This paper investigates a robust estimator that can achieve joint estimation for clock skew and offset under delay with unknown distributions by employing the competitive learning-based rival penalized expectation maximization (RPEM) algorithm to learn the unknown probability density function (pdf) of stochastic delay. Moreover, for the asymmetric delay scenario, besides the unknown delay distributions, delay asymmetry is another important influence on synchronization accuracy. Therefore, the optimal invariant robust estimator is developed to simultaneously handle the performance degradation caused by delay asymmetry and unknown delay distributions. The estimator can even cope with the scenario where the random delays in the uplink and downlink follow different distributions. The effectiveness of the estimation schemes is validated by the computer simulations.

Precise clock synchronization is a critical requirement in industrial networks. To meet this need, the Precision Time Protocol (PTP) has been widely adopted as the standard solution. Nevertheless, the protocol’s accuracy is often compromised by unknown and unmodeled random delays, presenting a significant obstacle to reliable network operation. In this article, we propose a robust clock offset estimation scheme to enable a much higher synchronization accuracy for PTP in the presence of unknown and unmodeled delay distributions. To make this possible, we first derive the probability density function (pdf) of the delays via a maximum entropy approach, which is constrained by fractional moments and an unbiased likelihood estimation. Based on the derived pdfs, we further develop a low computational cost L-estimator for the clock offset, which is a linear function of the order statistics and robust against unknown and unmodeled random queueing delays. Simulation results illustrate that the proposed clock offset estimation scheme has much lower computational complexity and yields much more accurate estimation results than the compared existing methods in the presence of unknown and unmodeled disturbances.

In industrial wireless networks, millimeter wave (mmWave) communication is able to provide ultra-low latency and ultra-high bandwidth, emerging as a key technology to meet the real-time requirements of future industrial applications. In order to maintain the stability of the mmWave connection, the traditional approach is to perform beam alignment by frequent beam sweeping, but this results in additional beam overhead. Recently proposed vision-aided beam prediction models emerge as a solution to the above problem. However, the usage of models with complex structures, such as deep convolutional neural networks, requires substantial system storage and causes high computational costs. To this end, we propose a lightweight multimodal environment semantics aided beam prediction scheme. It extracts and combines the absolute and relative positions of the target from the locator and images as the environment semantics, then inputs them into an efficient and lightweight prediction model to output the optimal beam index. Experimental results demonstrate that, compared to the existing mainstream vision-aided or vision-position-aided beam prediction methods, the proposed model achieves a maximum top-1 beam prediction accuracy improvement of 7.6%, while saving the model size by about 83.4% and reducing the running time of the beam prediction stage by one order of magnitude.

Wireless Sensor Network (WSN) reliability evaluation is essential for ensuring the stable operation of network. Traditional methods usually focus on the network topology structure, and calculate the normal operation probability of WSNs. However, these methods usually ignore the energy consumption and network lifetime. In this paper, a novel reliability evaluation algorithm TLR is proposed, which calculates the network lifetime under dynamic network environment according to the pre-set network topology structure reliability threshold, and realizes the comprehensive reliability analysis of network topology and lifetime. In addition, as the basis for reliability evaluation, this paper proposes a new deep reinforcement learning network framework GNN-AC combining graph neural network and actor-critic network, which solves the challenge of constructing Virtual Backbone Network (VBN) in dynamically operating networks. Based on the self-defined fitness matrix and fitness value, the objective function is set to optimize the VBN construction scheme to accurately calculate the network lifetime, and the relationship between the reliability of network topology and network lifetime is discussed. Simulations are carried out for various sizes of WSNs to show the advantages and effectiveness of the proposed approach in estimating network lifetime and reliability evaluation.

In the Industry 4.0 era marked by cyber–physical systems (CPSs), the flourishing Internet of Things (IoT) technologies offer development potential for intelligent transportation systems. Physical devices in automobile systems are connected to the Internet to enable data collection and exchange. However, due to vulnerabilities in IoT devices and communication protocols, IoT-enabled CPSs are susceptible to attacks. As a core component of vehicle systems, malicious attacks on the vehicle’s active suspension can severely degrade passenger comfort and threaten vehicle safety. This article investigates the security of IoT-enabled active vehicle suspension systems (AVSSs) under resourceful attackers. First, a discrete-time Takagi–Sugeno fuzzy suspension model is developed to capture the inherent system uncertainties. Second, to counter resourceful false data injection (FDI) attackers who can acquire system knowledge and construct attacks, a dual-mode security algorithm integrating proactive and reactive defense mechanisms is proposed. The proactive defense mechanism leverages the moving target defense (MTD) strategy, which dynamically alters system parameters to hinder attackers’ reconnaissance capabilities. This increases attackers’ costs while reducing defenders’ burden in countering attacks. Furthermore, a high-order observer-based controller is designed to resist attacks, incorporating both the MTD strategy and a homogeneous polynomial parameter-dependent control law. On the other hand, the reactive defense mechanism employs an MTD-based detector to detect attacks. Finally, the proposed method is validated through simulations and hardware-in-the-loop (HIL) tests.

Age of Information (AoI) is a crucial metric for data freshness in 5G ultrareliable low-latency communication systems. It is affected by various factors, such as the channel quality, the energy of the nodes, and so on. To overcome the severe path losses and environmental obstacles, intelligent reconfigurable surface (IRS) is adopted to reconfigure signal propagation environments, thereby mitigating the significant losses and creating additional signal reflection links. Different from the existing research on AoI in IRS-assisted wireless networks, which overlooks the impact of energy consumption on AoI and assumes ideal phase shift models, we investigate the entire process of data generation to arrival and propose a double-IRS cooperative scheme for data transmission with practical phase shift models in blocked wireless networks. The joint optimization problem of the long-term average AoI and node energy consumption is formulated. Given the nonconvexity of the joint optimization problem, we decompose it into an outer data transmission scheduling problem and an inner double-IRS phase shift optimization using a two-layer optimization framework. Then, we use the deep Q-network algorithm to solve the outer problem. For the inner problem, we propose a penalty-based alternating optimization (PB-AO) algorithm to solve the coupling between double-IRS phase shifts and the nonconvex constraints of practical phase shift models. The extensive simulation results verify the effectiveness of the proposed PB-AO algorithm in optimizing double-IRS phase shifts under the practical phase shift model and demonstrate the superiority of our double-IRS cooperative data transmission scheme compared with other benchmark ones.

In order to handle the uncertainty of a class of cyber-physical systems with external disturbances, an observer-assisted gain-scheduling tracking control scheme is proposed in this article. First, a covert-switching-based (CSB) attack mechanism is proposed from the attacker’s point of view for the first time to enhance the expected destructive power. The sensor-to-observer (STO) channel and the observer-to-controller (OTC) channel are subjected to mixed network attacks and deception attacks, respectively. Different from most existing attack models, the attack mechanism proposed in this article can dynamically select the channel where the attack occurs by changing the scheduling parameters. Therefore, greater system performance degradation can be expected. Second, corresponding to the proposed CSB attack mechanism, the controller and observer are designed from the defender’s perspective through the high-order multi-mode switching free-weighting matrix (HMSFM) mechanism to reduce the negative impact of the attacks. At the same time, many time-varying balance matrices (TVBMs) for various switching modes of system dynamics are proposed to reduce the conservatism of the designed controller and observer. Then, a sufficient condition for exponential stability is successfully established to guarantee the$H_{\infty }$performance of the augmented error system. Eventually, the dynamic adjustment ability of the CSB attack mechanism and the anti-attack performance of the HMSFM mechanism are demonstrated through experimental simulation and active suspension hardware-in-loop (HIL) tests, and the advancement of the proposed attack-defense mechanism is thus verified.

Millimeter wave (mmWave) wireless communications are significant technologies that support Internet of Things (IoT) systems to achieve fast and stable data transmission, and the guarantee of its communication quality usually depends on accurate beam prediction. Due to the advantage of not relying on channel state information, beam prediction schemes using visual data and artificial intelligence become popular. Most of the current vision-aided beam prediction schemes directly predict the index of the optimal beam in the codebook. However, these methods are only applicable to certain codebooks and have limited generalization and scalability. To address the issues, we propose a vision-aided beam prediction scheme based on deep reinforcement learning (DRL). The scheme takes the original image as input and extracts the position and velocity of the user through the object detection algorithm. Subsequently, combined with the current state of the base station, it outputs a continuous angle value and finally matches it with the beam index in the codebook. Furthermore, we integrate the attention mechanism into the actor network of the deep deterministic policy gradient (DDPG) and propose a scheme of DDPG with attention mechanism (DDPG-A), which can perform differential processing on features, thereby enhancing algorithmic performance. The simulation test utilizing real datasets demonstrates that the proposed scheme has good generalization and scalability while considerably reducing the beam training overheads.

Consensus-based time synchronization, which combines multiagent consensus and distributed network techniques, becomes an essential approach to achieve accurate time synchronization in wireless sensor networks. In actual synchronization process, random communication delays will have a negative impact on information exchange, which leads to a significant degradation in the synchronization performance. Besides, the convergence speed of synchronization errors in existing average consensus synchronization algorithms poses challenges due to the iterative update. To deal with these issues, this article proposes a maximum consensus time synchronization scheme based on the multihop virtual links. Primarily, a multihop virtual links mechanism is proposed, which shares clock information among nodes to shorten the convergence time. Meanwhile, a noniterative clock parameters compensation approach based on maximum consensus is developed to further speed up the synchronization process. In addition, a moving average estimator is designed to minimize the adverse impact of delays. The convergence performance of the proposed scheme is proved theoretically. Simulation results verify the correctness of theoretical analysis and also demonstrate that the proposed scheme outperforms existing algorithms in terms of synchronization accuracy and convergence speed under delays.

It is challenging to ensure the safety of vehicles in the presence of malicious attacks. For this reason, the security control problem of nonlinear active quarter-vehicle suspension systems (QVSSs) is investigated in this paper. Within transportation cyber-physical systems, a specific Takagi-Sugeno fuzzy representation is adopted to capture the nonlinear behavior of vehicle dynamics. Firstly, accurately capturing the randomness and variability of attacks within complex driving environments poses a challenge. To precisely model the randomness of attacks, a probabilistically uncertain denial of service (PUDoS) attack strategy is adopted, thereby providing a practical range for potential attacks. Secondly, a novel probability-dependent homogeneous polynomial non-quadratic control law (PHNQCL) is designed. On the one hand, the high-order character of the controller enables the introduction of more groups of gain matrices, enhancing the control flexibility and reducing the conservatism. On the other hand, the designed controller exhibits strong defense capabilities against PUDoS attacks. Subsequently, feasible criteria for the PHNQCL are established using a high-order Lyapunov function, which not only stabilizes the active QVSSs but also reduces the conservatism. Finally, hardware-in-the-loop tests serve to validate the feasibility of the proposed method. Note to Practitioners—As a critical part of automotive chassis, the active quarter-vehicle suspension systems (QVSSs) are crucial for improving ride comfort and ensuring handling stability. However, in real-world environments, external disturbances, uncertainties, and nonlinearities can degrade their performance. As networks evolve, the interconnections in active QVSSs increase. This makes communication channels more vulnerable to attacks, further compromising vehicle safety. This paper explores security control for nonlinear QVSSs, modeling them using T-S fuzzy logic to capture vehicle dynamics. To simulate a complex vehicle driving environment, a probabilistically uncertain DoS attack strategy is designed. The challenge is to design new control methods to mitigate the performance degradation caused by attacks while enhancing vehicle safety in diverse scenarios. Hardware-in-the-loop tests are performed to verify that the proposed control algorithm can provide strong operational stability, ride comfort, and protection of vehicle components.

Clock synchronization is a prerequisite for the proper operation of industrial networks. IEEE 1588 Precision Time Protocol (PTP) can provide tight synchronization for a vast number of industrial applications. However, the joint tracking of clock skew and offset for IEEE 1588 under the more realistic delay asymmetries scenario is a challenging problem in sophisticated industrial networks. In this paper, we first investigate the biased estimation problem for clock parameters tracking in the presence of delay asymmetries, which significantly degrade the synchronization accuracy. Based on the investigation, a state-space model is developed that is capable of overcoming the uncertainty caused by the unknown packet delay distribution. A recursive joint clock skew and offset tracking scheme that employs the robust three-step recursive Kalman filter (R3SRKF) is proposed to estimate the time-varying clock parameters in a minimum-variance unbiased manner under the asymmetric packet delays scenario. Also, a variant of the R3SRKF is derived to joint track clock skew and offset with asymmetric packet delays in multi-hop networks. Simulation results indicate the effectiveness and performance enhancement of the presented robust tracking scheme.

Age of Information (AoI) is a popular information freshness metric and is widely used to evaluate the real-time performance of industrial wireless networks (IWNs). In IWNs with heterogeneous traffic, there may be differentiated real-time requirements. In this article, the peak AoI (PAoI) is introduced to assess the information freshness of the traffic with the random updating pattern. Meanwhile, the deadline related to delay for the traffic with the period updating pattern is considered to ensure the timeliness of packet delivery. An optimization problem for minimizing the average PAoI violation rate under the delay constraints is investigated. To address the NP-hard optimization problem with constraints, the tool from Lyapunov optimization theory is utilized to transform the constraint satisfaction into a queue stability problem to obtain an unconstrained optimization problem. Then, by decomposing the decision of each slot, a two-step scheduling scheme is proposed, which consists of one main policy based on deep reinforcement learning and two subpolicies. Numerical results show that the proposed scheme can minimize the average PAoI violation rate while satisfying the delay constraints.

Vehicular edge computing (VEC), as an emerging computing paradigm, enables the timely processing of computing tasks at the network edge through on-vehicle servers, thereby meeting users’ demands for information freshness. In this paper, we introduce the Age of Information (AoI) to measure information freshness and investigate the scheduling problem minimizing the long-term average AoI in VEC-assisted Internet of Things systems. The main challenge lies in the strong coupling between link scheduling and server selection under the location constraints of VEC. To address this issue, we design a scheduling strategy based on deep reinforcement learning and improve the neural network structure using a branch network approach, reducing complexity by decreasing the number of actions represented in the network’s output layer. Moreover, we introduce an action masking scheme that accelerates the algorithm’s convergence in this system. Numerical results show that the proposed scheduling algorithm can achieve up to a 25.4% performance gain compared to existing advanced algorithms.

Time synchronization is a significant premise for the proper functioning of wireless networks. Several energy-efficient protocols targeting time synchronization have been proposed to maximize accuracy while minimizing energy consumption. Since no packet transmission is required, receiver-only synchronization has been extensively studied in wireless networks. However, there is a performance loss in synchronization accuracy and robustness because inactive nodes are synchronized by passively receiving messages over a single link. In wireless environments, an inactive node inevitably overhears paired synchronization messages from multiple communication links due to the broadcast characteristic. Based on this consideration, an accurate and robust clock parameters estimation algorithm for energy-efficient synchronization with multi-link overhearing is developed. Specifically, a robust joint estimator of clock skew and offset based on invariant decision rules is derived with the assumption that prior information regarding the availability of overhearing links is known. In the absence of such information, as in harsh wireless channel conditions, we also propose a linear aggregation estimation mechanism with outlier diagnostic, which further enhances the robustness and resilience of time synchronization over wireless networks. Finally, numerical simulations are implemented and simulation results demonstrate that the synchronization accuracy and robustness are significantly enhanced.

Timestamp-free synchronization protocol is tailored to provide a global time understanding for resource-limited wireless networks as it eliminates timestamp interaction, thereby minimizing additional resource overheads. However, the existing two-hop based timestamp-free protocols are not suitable for synchronizing all nodes in multi-hop networks, as they necessitate multiple response times to establish the timestamp relationship between each pair of neighboring nodes. To this end, we introduce a novel multi-hop timestamp-free synchronization protocol. The proposed protocol allows any two nodes to be synchronized using only local timestamps and the skew estimates embedded within packets traversing the reverse path. Furthermore, considering that synchronization accuracy suffers from delay variation resulting from packet loss or retransmission in wireless networks, we derive a Pitman estimator to estimate the clock skew under arbitrary delay models, given known information. To further target unknown arbitrary delay distributions, we approximate the probability density function (pdf) of stochastic delays using a Gaussian mixture model, and then learn the pdf using the rival penalized expectation maximization algorithm. With the aid of the learned pdf, the robustness-enhanced Pitman estimator is derived, which is robust against arbitrary distributed delays without known knowledge. The effectiveness and performance enhancement of estimators are validated by simulations.

To measure the freshness of information more accurately and satisfy the timeliness demands for a wide range of time-sensitive applications, Age of Information (AoI) becomes a popular network performance metric. In this paper, we investigate the optimization of real-time performance in an Internet of Things (IoT) system with heterogeneous traffic, where periodic traffic for control and random traffic for update coexist. Generally, the periodically arriving data used for control is strictly deadline-sensitive, while the randomly arriving data used for updates has freshness requirements. Thus, we seek to develop a scheduling scheme that minimizes the long-term average AoI under the delay tolerance constraint, to meet the varying transmission timeliness requirements for different arrival models. The optimization problem is modeled as a constrained Markov decision process, and then it is decoupled into two classes of optimization sub-problems for single-node by relaxing the hard constraint of channel resources with the Lagrange multiplier. On this basis, a multi-node scheduling scheme consisting of a master policy and two sub-policies is designed. Specifically, two sub-policies are built for the two classes of optimization sub-problems, respectively, with Lyapunov and linear programming methods. For the hard constraint, a master policy is designed by introducing deep reinforcement learning for scheduling opportunities allocation, and a truncated sub-policy is proposed based on one of the sub-policies. Numerical results show that the proposed scheme can obtain a smaller long-term average AoI compared to other advanced algorithms while satisfying the delay constraint.

With the widespread application of 5G technology in the Industrial Internet of Things (IIoT), dividing nodes into different network slices according to delay tolerance requirements can facilitate reasonable resource allocation and guarantee quality of service (QoS). In this paper, we investigate the network slice resource allocation algorithm with delay tolerance based on traffic prediction. A traffic prediction algorithm is proposed combining convolutional neural network (CNN) with attention mechanism and bidirectional long-short term memory (Bi-LSTM) to obtain the spatiotemporal features. Based on the predicted traffic, the problem of minimizing the usage of physical resource blocks (PRBs) is studied, and a two-layer structure resource allocation algorithm based on deep reinforcement learning (DRL) is proposed. Specifically, Dueling Double DQN (D3QN) is used to allocate PRBs between slices, and a heuristic algorithm is used to allocate PRBs among nodes in the slice. Furthermore, we consider the joint optimization problem of PRBs and power. In light of the coupling between PRBs and power aggravates the high dimension of the action space, we propose a resource allocation algorithm which using the branch structure to decoupling the action space. Simulation results show that the proposed algorithms can satisfy the QoS and outperform the baseline algorithms.

In industrial wireless sensor networks, the existing event-based consensus clock synchronization schemes can achieve network-wide clock synchronization without considering the communication delays. However, communication delays are a significant factor restricting the achievement of clock synchronization in realistic scenarios. Besides, a constraint to be considered in the design of synchronization scheme is the limited communication resources of sensor nodes. Therefore, in this article, we investigate an event-based consensus clock synchronization scheme under delays. On the one hand, a new low-pass filter is used to estimate the relative skew to resist the influence of communication delays. On the other hand, a new event-triggered scheme is utilized to reduce unnecessary information transmission. Finally, we provide the convergence proof and simulation results of the proposed scheme, which further proves that it can achieve clock synchronization under communication delays while decreasing communication overhead.

In industrial wireless networks (IWNs), age of information (AoI) and delay are two significant metrics to measure data freshness and delivery timeliness. In this article, an IWN system with delay-sensitive and normal data is considered. To perform a joint optimization of the average AoI and the deadline-related overdue rate, we investigate the scheduling policy under time-varying channels. With the channel state knowledge available, we evaluate the expected gain obtained with the assumption that whether the data are scheduled or not for each sensor node, and develop a low-complexity scheduling policy. Under the hypothesis that channel state knowledge is not available, we utilize the model-free learning property of dueling double deep Q-network (D3QN) for the learning of scheduling policy and design a weighted expert knowledge-based exploration scheme that can achieve a higher convergence speed compared to the classical D3QN. Simulation results show the tradeoff between AoI and delay and demonstrate that the two proposed policies outperform existing state-of-the-art algorithms.

Real-time data delivery is significant for the Industrial Internet of Things (IIoT). Age of information (AoI), a popular real-time metric, is usually used to measure the data freshness of the IIoT systems. If the data most recently received by the destination at time $t$ was generated at time $t_{1}$ , then the AoI is $t-t_{1}$ . In this paper, we consider a multi-sensor multi-server IIoT system and develop scheduling algorithms to minimize the average AoI. The challenge lies in the strong coupling between link scheduling, server selection, and service preemption. To address this issue, we propose a guided exploration-based deep Q-Network (GE-DQN) algorithm utilizing a fixed advantage policy, which has a faster learning speed compared to classical deep Q-Network. Moreover, we use a shared decision module followed by several network branches to transform the structure of GE-DQN and propose a guided exploration-based Branching Dueling Q-Network (GE-BDQN) algorithm. Since the branch structure of GE-BDQN can decompose the high-dimensional action, GE-BDQN can reduce the approximate exponential growth of the number of output neurons with the increase of the number of sensors to linear growth compared to GE-DQN, ensuring the applicability of the algorithm under large-scale systems. From the simulation results, it can be found that the proposed two algorithms can achieve better average AoI compared to the advanced algorithms, and the GE-BDQN algorithm can achieve up to 36% performance gain.

Age of information (AoI) is a popular metric of data freshness, however, it neglects the usability of data. In light of this, we introduce a new metric,Age of Usage Information(AoUI), which can jointly capture the freshness and usability of correlated data in the Internet of Things in a fine-grained manner. Based on the proposed metric, we investigate the optimization problem of minimizing average AoUI, where the correlated nodes transmit data to the destination via noisy channels. To seek the optimal data scheduling policy, we first develop a virtual queue based (VQ) policy under the assumption that the priori knowledge of the channel state is known. Then, considering the case where the channel state is unknown, we utilize the model-free characteristic of double deep Q-network (DDQN) to design an improved exploration based DDQN (IE-DDQN) policy which does not require a priori knowledge of the channel state. Furthermore, we investigate the development of joint data scheduling and usage policy and introduce decoupled action branches to improve the structure of the neural network of DDQN proposing a decoupling action based DDQN (DA-DDQN) policy. Simulation results show that the proposed VQ, IE-DDQN, and DA-DDQN policies all exhibit superior performance compared to baseline algorithms such as the classical DDQN method and the greedy policy of scheduling the node with the largest product of AoUI and usable factor, which may be due to the consideration of the stability of the virtual queue, the improvement of the exploration process, and the reconstruction of the neural network structure, respectively.

Clock synchronization is crucial requirement for coordinated activities in distributed networks. Precision Time Protocol (PTP) is a popular synchronization scheme to provide tight synchronization which, however, suffers from packet delay variation (PDV). In real networks, prior information on the statistical distribution of stochastic delays is always unavailable. Therefore, it is significant to estimate the clock skew and offset in the presence of stochastic delays with unknown statistical distributions. This paper investigates a robust estimation approach applicable to scenarios where the delay distributions are unknown, which utilizes the Gaussian mixture model to approximate the probability density function (pdf) of random delays, and then employs the rival penalized expectation maximization (RPEM) algorithm to learn mixture model parameters. The optimum invariant estimators for the clock skew and offset are derived based on the learned pdfs of random delays. The effectiveness of the estimation scheme is validated by the computer simulations.

Receiver-only synchronization (ROS) is a strategy in which nodes only need to receive packets to achieve clock synchronization. Since no packet transmission is required, it has been extensively studied in wireless sensor networks (WSNs) where energy resources are rare. Nevertheless, there is a performance loss in synchronization accuracy and robustness of the inactive nodes in the ROS scenario because these nodes are synchronized with a reference node only by overhearing. In addition, it is observed that in a practical network, the execution of ROS inevitably overhears paired synchronization messages from multiple communication paths. With the help of this feature, on the basis of the clock parameters estimation method and information aggregation technique, a linear aggregation estimation mechanism with outlier diagnostic is presented to improve the synchronization accuracy and robustness in ROS with multi-link overhearing scenario. Finally, numerical simulations are implemented based on two single path clock parameters estimation algorithms of inactive nodes, and simulation results demonstrate that the synchronization accuracy and robustness are significantly enhanced.

Wireless sensor networks (WSNs) have a broad range of applications, and time synchronization is essential to ensure their correct operation. In this paper, a clock skew estimation scheme is presented for timestamp-free synchronization, which greatly simplifies computation while economizing energy consumption. Considering that no single delay model can fit all cases in WSNs, a robust estimator with low complexity based on this scheme is developed that can estimate clock skew accurately without prior statistical characteristics of delays. Simulation results demonstrate the effectiveness of the proposed scheme.

In UAV-enabled wireless power transfer (WPT) systems, the UAV is usually planned to fly and charge the ground nodes (GNs), so as to prolong the lifetime of wireless sensor networks. But in fact, the long charging distance between the UAV and the GNs makes the GNs receive less energy. In order to maximize the received energy of GNs, this article focuses on the charging process and proposes a V-shaped WPT scheme, where the UAV descends to the optimal hover position and charges the GNs, so as to transfer more energy to the GNs. Moreover, considering that the GNs far from the hover position receive little energy in the V-shaped WPT scheme, we further develop an Inverted Trapezoidal WPT scheme to improve the fairness of the energy received by the GNs, in which the UAV continuously charges the GNs whether it is hovering or flying horizontally after lowering its altitude. Because the UAV’s flight and maneuvering are strictly constrained by its loading energy, we establish the UAV’s propulsion power consumption model in different flight attitudes and formulate the optimization problems of the GNs’ received energy in two schemes, and then develop two algorithms to solve them. The simulation results show that the performance metrics of UAV’s energy consumption, the efficiency, and fairness of GNs’ received energy are better than the compared schemes.

Memory network has been extensively used to record prototypical normal patterns to prevent overgeneralization of the network to reconstruct anomalies for video anomaly detection. However, existing memory-based methods only record the lossy representation of normal item prototypes, without recording the rich relationships between them. In this work, we propose an Associative Memory with Spatio-Temporal Enhancement (AMSTE) which introduces the global context information constraint of motion to enhance the appearance features and learn the normal item prototypes and their relationship. Specifically, we utilize two encoders to extract spatio-temporal features with the Spatio-Temporal Enhancement Module (STEM) to enhance appearance features with global motion constraints. Then, the prototypical patterns of normal data and their relationships are recorded in the item memory and relational memory, respectively. Finally, we retrieve features from the memory pools and reconstruct the video frame through the decoder. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our approach. The code will be released athttps://github.com/HuYongting/AMSTE.

Age of information (AoI) is an effective performance metric for time-critical industrial Internet of things (IIoT) applications. We investigate status prediction and data aggregation with prediction error awareness, to enhance the AoI performance for short-packet transmission (SPT) in time-critical IIoT. A predict-compare (PredComp) transmission scheme is proposed, where proactive transmission termination is employed in case of prediction error, by comparing the predicted and real updates at source. It is proved to achieve a significant average AoI performance gain over the case without prediction, even under high prediction error probability. In addition, a predict-aggregate-compare (PredAggComp) transmission scheme is proposed, where two status updates are predicted with different prediction horizons and aggregated by utilizing their time correlation. That allows a good tradeoff between the prediction accuracy and the transmission error probability. A closed-form threshold that the PredAggComp scheme outperforms the PredComp scheme is derived. Moreover, prediction horizon adaptation is conducted to minimize the average AoI of the proposed transmission schemes. Simulation results verify the analytical results and show the superiority of the proposed PredComp and PredAggComp schemes, with an average AoI reduction of up to 64% over the case without prediction.

Age of information (AoI) is a new metric that can measure the data freshness of the industrial Internet of things (IIoT) systems. Focusing on a hybrid scenario where periodic and random sampling devices exist simultaneously, we investigate AoI-aware scheduling schemes under deterministic delay constraint of devices with periodic sampling in noisy channels. We first consider that the probability of successful delivery of data obeys a known fixed probability, and develop a dynamic scheduling scheme utilizing the slot-based Lyapunov drift framework. Second, in the case where the prior knowledge of the probability of successful data delivery is unknown, we introduce deep reinforcement learning (DRL) to learn the model-free scheduling and propose a scheduling policy based on the dueling deep Q network (D3QN). Numerical results show that the proposed Lyapunov policy and D3QN policy can minimize the average AoI while subjecting to the delay constraint.

Some published WiFi-based passive human tracking systems have achieved sub-meter accuracy. However, they require the location of WiFi devices to be known in advance for passive human tracking, which limits their application in practical indoor scenarios. In this paper, we propose WiSen, a novel passive human tracking system using one pair of commodity WiFi devices with unknown locations. First, we introduce a signal power model for human-related signal extraction and multi-dimensional parameter estimation. Due to low-resolution parameter estimates and noise, we further design a confidence-aware-based path pruning method that combines the distribution of path parameters from successive windows to select reliable paths of interest. Before that, we adopt a data augmentation method to increase the number of available paths to learn parameter distributions better. Then, we statistically estimate the transmitter's location using a kernel density estimation method and ultimately yield the user's location using an improved Gaussian Sum filter approach. We validate the performance of WiSen in real-life indoor environments. The experimental results show that WiSen can realize the sub-meter level accuracy for passive human tracking and device localization.

With its low delay and high reliability, 5G technology can meet the requirement of interconnection for Industrial Internet of things (IIoT). However, industrial heterogeneous networks have different quality of service (QoS) requirements, so 5G slicing technology is necessary to logically isolate them from each other. A main challenge lies in how to reasonably allocate network resources for each slice and the nodes in it to ensure the low delay and reliability requirements of IIoT. In this paper, we focus on the problem of minimizing the usage of physical resource blocks (PRBs) under delay constraints, and propose a dynamic allocation algorithm based on traffic prediction. In order to analyze the spatial and temporal features of network traffic, we combine convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), and then add attention mechanism to form a traffic prediction model CNN-Bi-LSTM with attention mechanism (CBL-A). For dynamic resource allocation, according to the traffic prediction results, on the basis of Dueling Double DQN (D3QN), a heuristic PRBs scheduling policy (PSP) is embedded to obtain a D3QN model with PSP (D3QN-PSP), which reduces the action space and accelerates the convergence speed. The simulation results show that the proposed algorithm can minimize the consumption of PRBs while guaranteeing the delay and slice isolation constraints.

Age of Information (AoI) is a recently introduced metric to capture data freshness. In this article, we consider a multiuser single-destination Internet of Things (IoT) system with periodic state updating, and investigate the scheduling methods of minimizing the long-term average AoI with limited retransmission times. In view of the retransmission mode, the AoI optimization problems for retransmission with and without feedback are analyzed. For the retransmission without feedback, we formulate the expected decision loss (EDL) function of the AoI optimization problem with the finite retransmission times and propose a loss-greedy policy by minimizing the EDL at each step. For the retransmission with feedback, a potential optimal solution is to construct the AoI optimization problem as an infinite-horizon Markov decision process (MDP) and solve the corresponding Bellman optimal equations. However, this optimal solution is prone to suffer from the curse of dimensionality and is hard to implement. To address this issue, we decouple the infinite-horizon MDP into the finite-state MDP in each single frame, and then propose a low-complexity slot-based max-weight (SBMW) policy to minimize the long-term average AoI. Numerical results show that, compared with an ALOHA-like baseline policy, the proposed Loss-Greedy policy can achieve up to 48% reduction of the average AoI for the retransmission without feedback, while the SBMW policy can reduce the average AoI by 57% in the retransmission with feedback. The average performance gain of the proposed policies over the state-of-the-art policies is at least 10%.

Age of Information (AoI) is a newly rising metric for measuring the freshness of information. In this article, we consider a multidevice computing-enabled Internet of Things (IoT) system with a common destination, in which the status update sampled by the device can be offloaded directly to the destination for computing or computed by the device and then delivered to the destination, and jointly design offloading and scheduling policies to minimize the average weighted sum of AoI and energy consumption. The challenge lies in computing mode selection and its strong coupling with scheduling decisions. To address this issue, we formulate the optimization problem as a bilevel discrete-time Markov decision process (MDP) and approximate the optimal solution by relative value iteration. Furthermore, the threshold structure of the MDP policy is shown. However, with the expansion of the system scale, the MDP policy will suffer from the curse of dimensionality. In light of this, we develop a learning-based algorithm based on emerging deep reinforcement learning (DRL) to reduce the dimensionality of state space and utilize a late experience storage method to train two heterogeneous artificial neural networks (ANNs) synchronously during the training process. Simulation results show the structure of the MDP policy and verify the performance of the DRL policy is near-optimal.

Clock synchronization is crucial for applications in wireless sensor networks, such as event scheduling and data fusion. In practical wireless networks, environmental changes cause the oscillator to be imperfect. Thus, it is necessary to track the nonlinear varying clock dynamically. In this paper, we propose a timestamp-free clock skew (frequency difference) and offset (time difference) joint tracking algorithm based on extended Kalman filter (EKF), which can be embedded into the general network data flow to achieve long-term synchronization without additional communication overhead. To further improve energy efficiency of clock synchronization, this paper develops an EKF clock skew tracking algorithm for silent nodes in receiver-only synchronization. For the situation that silent node overhears synchronization information from multiple active nodes, we present multi-information timestamp-free synchronization and multi-observation clock skew fusion scheme for silent nodes. Simulation results show the effectiveness of the proposed tracking algorithms.

Existing packet-based synchronization approaches take extensive energy for industrial wireless sensor networks due to substantial timing message exchanges. Combined with acknowledgement (ACK), this article proposes an energy-efficient synchronization protocol without exchanging timestamps. Also, a mapping rule of ACK response time is presented for the joint estimation of skew and fixed delay without requiring a following response in each cycle, so that estimation can be implemented in parallel to data transmission. Then, the timestamp-free synchronization technique is extended to the receiver-only synchronization scenario to further reduce communication overhead. Assuming Gaussian variable delays, the maximum likelihood estimators and corresponding lower bounds for slave node and silent node are developed, as well as a low-complexity estimator for silent node. In addition, considering the dynamics of clock, a timestamp-free clock skew tracking algorithm using Kalman filtering is developed. Simulation results validate the efficiency of the proposed estimators.

Device-Free Localization and Tracking (DFLT) acts as a key component for the contactless awareness applications such as elderly care and home security. However, the random phase errors in WiFi signal and weak target echoes submerged in background clutter signals are mainly obstacles for current DFLT systems. In this paper, we propose the design and implementation of MuTrack, a multiparameter based DFLT system using commodity WiFi devices with a single link. Firstly, we select an antenna with maximum reliability index as the reference antenna for signal sanitization in which the conjugate operation removes the random phase errors. Secondly, we design a multi-dimensional parameters estimator and then refine path parameters by optimizing the complete data of path components. Finally, the Hungarian Kalman Filter based tracking method is proposed to derive accurate locations from low-resolution parameter estimates. We extensively validate the proposed system in typical indoor environment and these experimental results show that MuTrack can achieve high tracking accuracy with the mean error of 0.82 m using only a single link.

For clustered wireless sensor networks, this letter presents a time synchronization scheme with low overhead that is based on the maximum consensus approach. The synchronization process is initiated by the cluster head and includes three steps: 1) threshold-based intracluster time synchronization (TITS); 2) forwarding-based intercluster time synchronization (FITS); and 3) one-way intracluster time synchronization (OITS). Specifically, first, TITS achieves the logical clock of cluster head to be synchronized to the largest logical clock of intracluster nodes with three times point-to-point message exchange in two cycles. Especially, by comparing the logical skew of each intracluster node with the logical skew of cluster head, member nodes with smaller logical skew will not reply to cluster head to reduce the number of message exchanges. Then, in FITS, the cluster heads synchronize with each other and the message exchanges of cluster heads are realized through the message forwarding of the gateway nodes. At last, OITS utilizes the one-way communication to synchronize all the intracluster nodes with cluster head via two times broadcasting of cluster head. Theoretical analysis and simulation results demonstrate that the proposed scheme can reduce the communication traffic greatly and improve the convergence rate.

Time synchronization is indispensable for convenient network management, device monitoring, security, and other fundamental operations in industrial wireless sensor networks (IWSNs). Over the past few decades, a wide variety of highly accurate clock synchronization protocols have been investigated by employing powerful statistical signal processing techniques. However, most two-way exchange estimation schemes do not readjust the node's local clock upon every resynchronization before the clock parameters are estimated. And it may not be appropriate in IWSNs where time synchronization is consistently required. Based on the two-way message exchange mechanism, this paper investigates the clock synchronization schemes of active node and overhearing node with immediate clock readjustment. The maximum-likelihood estimators of the clock skew and the corresponding Cramer–Rao lower bounds are derived assuming Gaussian delays. Simulation and experimental results validate the performance of the proposed estimators.

The clock synchronization problem for wireless sensor networks (WSNs) is inherently related to parameter estimation. Nowadays, extensive studies on time synchronization have been conducted by adopting statistical signal processing methods. However, most estimation schemes do not readjust clock offset during the process of synchronization parameter estimation, and it would lead to unsatisfactory clock accuracy during synchronization. Thus, the applications of these methods are greatly limited in WSNs. This paper analyzes time synchronization of sensor nodes with immediate clock adjustment at every cycle under multihop scenario, and presents an estimator of clock skew under the Gaussian linear delay model and the corresponding algorithms for finding the estimator in detail. Simulation results verify that the proposed estimator is efficient.

Time synchronization is a significant component in Wireless Sensor Networks (WSNs) for maintaining synchrony among nodes. Most time synchronization protocols in WSNs utilize dedicated synchronization packets for clock accuracy optimization. However, in practical WSNs, the joint design of time synchronization and Media Access Control layer protocols should be considered. One approach is adding timestamps directly into data packets and the corresponding acknowledgements (ACKs). Therefore, communication and energy overhead could be saved greatly and time synchronization could be seamlessly integrated into networks. In this letter, we investigate the time synchronization scheme of listening nodes overhearing the neighboring two-way timing packet exchange based on periodical ACK mechanism and present an efficient clock skew estimation algorithm with clock correction upon every synchronization. The Maximum Likelihood Estimator (MLE) of clock skew for Gaussian random packet delay is derived, and the corresponding Cramer-Rao Lower Bound (CRLB) is obtained. In addition, the MLE shows that the clock skew could be estimated without any prior knowledge of the fixed packet delay and the time of adjustment. Simulation results verify that the MLE is efficient.

Selection cooperation is an attractive cooperative strategy for its simplicity and automatic repeat request (ARQ) mechanism can bring additional diversity benefit for wireless networks. In this paper, we combine the distributed selection cooperation protocols with ARQ mechanism to develop more powerful cooperative schemes for delay-tolerant wireless networks and analyze their performance from the perspective of diversity-multiplexing-delay (D-M-D) tradeoff. For small networks where any two nodes have direct links, we investigate the general ARQ scheme which directly extends the selection cooperation protocol with single round of feedback to multiple rounds. We show that the D-M-D tradeoff is determined by the ability of relays in signal combining and demonstrate that allowing relays to perform combining reception can achieve optimal D-M-D tradeoff. Then we propose a simplified scheme which greatly reduces the number of feedbacks and almost achieves the optimal performance. For large networks where direct links are limited in the neighbors of each node, we present a diffusion ARQ protocol which can effectively exploit the channels of nodes that have no direct links with the source. The D-M-D tradeoff analysis and simulation results demonstrate the significant performance improvement of all the proposed schemes.

Single relay selection has been shown to be an attractive strategy for cooperative communications. In this work, we extend the distributed selection cooperation protocol with feedback to multisource multidestination cooperative networks and investigate the best relay confliction problem existing in the scenario. In the high signal-to-noise ratio (SNR) regime, due to the fact that sharing the same best relay by multiple source nodes has no influence on the diversity-multiplexing tradeoff (DMT) performance for each communication pair, the single relay sharing method is a simple and effective solution. For some practical systems with low or medium SNR, we propose two power allocation algorithms for the shared best relay and show that the method based on maximizing the number of successful relay flows is efficient in distributed scenarios with low complexity.