Mixture-of-Agents (MoA) improves LLM performance through layered collaboration, but its dense topology raises costs and latency. Existing methods employ LLM judges to filter responses, yet still require all models to perform inference before judging, failing to cut costs effectively. They also lack model selection criteria and struggle with large model pools, where full inference is costly and can exceed context limits. To address this, we propose RouteMoA, an efficient mixture-of-agents framework with dynamic routing. It employs a lightweight scorer to perform initial screening by predicting coarse-grained performance from the query, narrowing candidates to a high-potential subset without inference. A mixture of judges then refines these scores through lightweight self- and cross-assessment based on existing model outputs, providing posterior correction without additional inference. Finally, a model ranking mechanism selects models by balancing performance, cost, and latency. RouteMoA outperforms MoA across varying tasks and model pool sizes, reducing cost by 89.8% and latency by 63.6% in the large-scale model pool. Code is available at https://github.com/Jize-W/RouteMoA.

Cross-domain data collection in the maritime Internet of things (IoT) requires coordinated operation between autonomous underwater vehicles (AUVs) and unmanned surface vehicles (USVs). This coordination is challenged by heterogeneous AUVs and USVs, environmental dynamics and uncertainty, stringent energy budgets, and the intrinsic coupling between task allocation and path optimization. These factors jointly degrade collection efficiency, inflate energy consumption, and reduce temporal responsiveness. Distinct from existing studies, we propose an integrated cross-domain path planning and data collection framework that combines a novel dual-mapping Fermat-point method, a learning-based adaptive distributed kalman filter (LADKF), and an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. By generating the individual AUV trajectories, the dual-mapping Fermat-point method then constructs the USV route, simplifying the original complex dynamic collaboration problem, which enables efficient collaborative task allocation for cross-domain data collection. Concurrently, the LADKF adjusts process and measurement noise covariance online to improve state estimation robustness under complex sea conditions. Additionally, the improved MADDPG performs dynamic optimization that jointly accounts for data collection efficiency, energy expenditure, and task completion ratio. Simulation results show that, in scenarios affected by noise and ocean currents, the proposed dual-mapping Fermat-point method and LADKF–MADDPG framework significantly enhance cross-domain cooperative data collection efficiency, reduce energy consumption, and provide reliable support for large-scale maritime IoT deployments.

The cloud–fog automation (CFA) paradigm accelerates Industry 4.0 by enabling fully automated industrial systems through interconnected wired and wireless devices. However, dynamic uncertainties such as environment-induced network delays and disturbances challenge the stability and efficiency of these systems. To address these issues, this paper proposes a control–communication co-design framework named C3L (Control– Communication Coupling Loop), which integrates 5G and Time-Sensitive Networking (TSN) to coordinate transmission and control across cloud and fog layers. A hierarchical control strategy is introduced based on a delay threshold derived from Linear Matrix Inequality (LMI) analysis, enabling dynamic selection between cloud and fog control units for both stability and responsiveness. To ensure timely and reliable control input delivery under uncertain networks, we develop a deterministic transmission mechanism centered on a novel metric, Packet Loss Tolerance (PLT), which quantifies how many consecutive losses the system can endure while maintaining stability. Additionally, a new optimization criterion, Pareto Deviation Value (PDV), is proposed to avoid combining control and communication costs with incompatible physical units. Simulation results demonstrate that the proposed method enhances control robustness and communication efficiency in complex industrial networks.

Multi-object tracking (MOT) is crucial for edge-enabled industrial video surveillance. Many video streams are delivered to the edge via limited, dynamic communication channels to be processed efficiently for accurate, on-time surveillance responses. However, time-varying communication conditions and constrained edge computational resources challenge surveillance accuracy and latency. This paper proposes an edge-based industrial video surveillance framework with bandwidth adaptation and edge-end collaboration that operates under limited communication conditions. Besides, a communication-aware industrial video surveillance with edge-end collaboration method (CIVS) is devised to improve surveillance accuracy and latency performance across different bandwidth environments. Furthermore, an NP-hard integer non-linear problem is formulated to maximize the accuracy while minimizing the latency of MOT by configuring the matches between an industrial video network and an edge computing network, which is solved by a branch and bound algorithm. Simulations show CIVS achieves 0.5892s latency and 68.9% multi-object tracking accuracy, outperforming other state-of-the-art.

Designing safety-critical controllers for the blast furnace is crucial yet challenging due to its complex inherent dynamics. Recent advances in reinforcement learning (RL) have shown promise in designing effective controllers for complex industrial systems. However, direct application of RL control to an on-site blast furnace is hindered by stringent safety requirements and model discrepancies. To address these challenges, this paper presents a duo-stage RL-based safety-critical control framework (DRLSC Framework) that integrates safety-critical offline pretraining and an online agent transfer mechanism for long-term safe control of the blast furnace. An offline pretraining algorithm is designed to provide an adequate initial policy for online control, which consists of a data-driven blast furnace environment and a Soft Actor-Critic (SAC) agent. The offline stage incorporates a neural control barrier function (NCBF) to guarantee system state safety, which is trained jointly with the RL agent. During the online stage, a transfer mechanism facilitates rapid agent adaptation to the actual system dynamics, mitigating performance degradation arising from model discrepancies. Finally, silicon content ([Si]) tracking control experiments are carried out using a real dataset obtained from the #2 blast furnace of Liuzhou Steel in China. Compared with model-predictive-control-based methods, our DRLSC Framework can decrease the long-term tracking error by 50.21% when facing simulation discrepancy and maintain state safety continuously.

Rigid gang scheduling, which enables multiple threads of real-time tasks to execute concurrently on a fixed number of different processors, has recently gained attention. Compared to preemptive rigid gang scheduling, non-preemptive rigid gang (NPRG) scheduling improves predictability by requiring fewer context switches. However, NPRG scheduling is vulnerable to 2D-blocking, where lower-priority tasks can block a higher-priority task multiple times, leading to severe priority inversion at runtime and pessimism in schedulability analysis. The root cause is the work-conserving execution behavior, in which tasks are immediately executed whenever the required processors are idle, regardless of priority and the potential blocking of subsequent tasks. This paper focuses on the global fixed-priority NPRG scheduling and introduces the NPRG-SS scheduler. NPRG-SS leverages a selective stalling (SS) mechanism that selectively stalls lower-priority jobs whenever their execution could delay the start time of a higher-priority job in the ready queue. This non-work-conserving approach inherently mitigates multiple blocking. Additionally, we present the first schedulability analysis for NPRG scheduling with SS and propose a novel heuristic priority assignment technique, Iterative Priority Refinement (IPR). Experimental results show that NPRG-SS with IPR effectively mitigates priority inversion, accepts up to 42% more task sets than the baselines at runtime, while our proposed schedulability test accepts up to 80% more task sets than the baselines in worst-case scenarios.

Resource competition and intentional disruptions, grounded in rational intergroup conflict theory, play a central role in driving strategic rivalry in networked games. These mechanisms mirror real-world conflict dynamics, profoundly shaping decision-making processes and interfering with systemic stability. This study investigates the modeling and dynamics of networked evolutionary games with intergroup conflict (NEGs-IC). In the proposed framework, players are assigned a finite number of health points, which decrease when attacked–affecting both survivability and strategic interactions. Leveraging logical dynamical system modeling, we capture the co-evolution of strategies, payoffs, health points, and player actions, demonstrating that NEGs-IC can be effectively represented as a logical dynamic system. To characterize collective interest in NEGs-IC, we introduce an objective function that balances group cooperation and health point attrition. Based on this formulation, we define three evaluation criteria–optimal, suboptimal, and weak–to assess collective interest. An illustrative example is also presented to analyze network-based conflicts, offering insights into strategic behavior in adversarial environments.

Recent efforts have yielded substantial progress in backstepping platoon control for connected and automated vehicles (CAVs). While most existing studies focus on guaranteeing individual vehicle stability and string stability, their deployment in nonlinear vehicle platoons may face challenges from the so-called "butterfly effect." That is, even with guaranteed string stability, potential instantaneous spacing changes may imply unpredictable, uncomfortable fluctuations in vehicular velocity and acceleration. To address this issue, a parallel error-fluctuation suppression control framework is proposed in this work. Specifically, tunable triple-layered error boundaries (i.e., spacing, velocity, and acceleration) are constructed to reactively confine all propagated errors within predefined envelopes. By integrating a Barbalat-lemma-enhanced filtering-compensating mechanism and an adaptive approach based on the approximation capability of radial basis function neural networks (RBFNNs), asymptotic error tracking is realized to proactively suppress potential fluctuations. An adaptive backstepping control approach-integrating proactive and reactive suppression strategies-is then proposed to mitigate the unquantifiable "butterfly effect." Theoretical analysis and simulations demonstrate the validity and superiority of the proposed approach.

Coded distributed machine learning mitigates straggler effects and provides privacy protection by introducing redundancy through coded computing. However, the system remains vulnerable to privacy breaches when the number of honest-but-curious nodes surpasses the designed threshold, or when outsider adversaries eavesdrop on sensitive data. To address these limitations, we propose a privacy-preserving and straggler-resilient distributed learning framework, namely, differential privacy-based Lagrange coded computing (DP-LCC). First, we design a three-layer protection strategy against privacy threats and stragglers by retaining labels at the master, obfuscating features via Lagrange interpolation, and injecting calibrated noise into local computations. Second, we theoretically prove that the aggregated gradient is an unbiased estimator with bounded variance, and derive convergence bounds under both Gaussian and Laplace mechanisms, revealing the trade-off between privacy budgets and model utility. Third, we provide a comprehensive analysis of the system's computational complexity, privacy composition, and heterogeneity to verify the framework's efficiency and adaptability in realistic distributed environments. Extensive experiments on four benchmark datasets validate the theoretical results, demonstrating the robustness of DP-LCC against varying system parameters and heterogeneous environments.

Modern integrated sensing and communication (ISAC) technology enables the tight integration of sensing and transmission within the framework of maritime Internet of Things (IoT) to achieve enhanced performance. However, in actual maritime industrial environments, such maritime IoT systems are often susceptible to various sources of interference, particularly anthropogenic jamming, making the application of antijamming ISAC technology in maritime IoT an imperative challenge. To address this issue, this article proposes a novel algorithm integrating dynamic attack detection for solving transmission scheduling optimization problems under Denial-of-Service (DoS) attacks. First, a novel learning-based adaptive Kalman filter (LAKF) algorithm is introduced. Even in time-varying noise environments, the algorithm can still dynamically adjust the covariance matrix of observation noise through reinforcement learning to match the actual noise characteristics. Second, an intelligent detection algorithm is designed to counter time-varying attack patterns, which leverages a temporal window approach incorporating long short-term memory networks and an attention mechanism. Finally, we propose an energy-aware LAKF-attack pattern detection-twin delayed deep deterministic policy gradient tracking (APD-TD3T) algorithm, which integrates the LAKF and APD algorithms and uses the improved TD3 algorithm (an improvement of the DDPG algorithm) as the core algorithm. Specially, it also incorporates a delayed reward design, which effectively addresses the anti-DoS transmission scheduling optimization problem in continuous action space. Simulation experiment and comparison results demonstrate that the proposed LAKF-APD-TD3T algorithm achieves superior performance, significantly decreasing both tracking error and energy consumption in target tracking scenarios.

Existing zero-shot fault diagnosis methods are typically system-specific and numerically sensitive, which lack adaptive deployment capabilities across heterogeneous photovoltaic (PV) system scales and topologies. Multimodal large language models (MLLMs) emerge as a powerful solution in cross-system generalization, but their adoption in PV fault diagnosis has been limited by the lack of PV knowledge integration and challenges in processing diverse operating conditions. To bridge this gap, an MLLMs-empowered framework for zero-shot PV fault diagnosis is proposed for the first time, which jointly integrates data-driven and knowledge-driven schemes. First, a chain-of-thought-based data augmentation pipeline is constructed to achieve data-knowledge alignment and interpretable results. Second, a two-stage adaptation strategy is specifically designed for PV data to overcome system scales, diverse topologies, and numerical differences. It consists of a Kolmogorov–Arnold networks-based condition adaptive layer embedded in vision transformer and a low-rank adaptation-based PV domain fine-tuning. Third, we design a microservices-based architecture for PV-MLLM deployment that enables flexible component decoupling and adaptive inference, significantly reducing hardware requirements and resource consumption. The proposed method achieves 99.66% and 97.25% diagnostic accuracy on simulated and real-world datasets.

In microservice architecture, each industrial application is decomposed into multiple microservices and deployed on cloud servers to provide timely services to users. However, existing methods rarely optimize service deployment strategies from the user's perspective to reduce the leasing costs of cloud servers. Furthermore, accurately estimating the required number of instances and resource utilization for microservices remains challenging, and decision-making in large-scale scenarios also faces significant timeliness constraints. To solve this problem, this article proposes a Column Generation deployment strategy, which decomposes the microservice deployment problem into a master problem for scheme selection and subproblems for scheme generation and proves the gap between its convergent solution and the optimal solution. A Distributed Column Generation strategy is further introduced to enable efficient problem-solving. Experimental results based on real-world server pricing demonstrate that the proposed method exhibits a high degree of consistency with the theoretically optimal solution. Compared to the baseline methods, it reduces the average total cost of ownership (TCO) for users by 11.3%, while the decision-making time is only 21.5% of that of the comparative methods. At the same time we used our approach to make deployment decisions for real industrial microservices and deployed them on real cloud servers. Compared to the baseline approach, it reduces TCO by 4%, but decision-making is 97% faster.

With the widespread application of Internet of thingstechnology in smart grid, the integration of numerous intelligent terminals exacerbates network congestion and data loss, leading to increased load tracking deviations. Simultaneously, the automatic generation control (AGC) employed for maintaining supply-demand balance faces high costs, slow response rates, and limited adaptability to renewable energy. Employing gas-to-power technology in conjunction with AGC can enhance overall system efficiency and stability. This paper proposes a power allocation and pricing strategy utilizing a two-level Stackelberg game framework to reduce utility costs while boosting profits for telecom operator and gas company. We develop an electricity cost model for utilities considering regulation errors from direct load control in smart grid. Using an iterative algorithm and backward induction, we derive the Nash equilibrium for the Stackelberg game. Simulation results show that this strategy reduces utility costs and increases profits for telecom operator and gas company.

Aviation electrical connectors are essential components in the aircraft electrical wiring interconnection system (EWIS), responsible for information and energy transmission. Even a minor fault in a connector pin can critically affect the reliability and stability of the EWIS. However, traditional faulty pin detection methods rely heavily on manual visual inspection, which is inefficient and susceptible to missed or false detections due to the inherent limitations of human observation. To address these challenges, this article introduces a novel system-level approach that transforms the defect detection process from the physical domain to a virtual one powered by digital twin (DT) technology. A general framework for DT-based defect detection is proposed and instantiated through the design and implementation of a DT-enabled automated faulty pin detection (DT-AFPD) system. The DT-AFPD system integrates 3-D machine vision into a complete detection pipeline encompassing equipment design, data acquisition, DT model construction, algorithm development, and system deployment. Specifically, a 4-degree-of-freedom (4-DOF) device equipped with a 3-D structured light camera is developed to acquire point cloud data of aviation connectors. Several preprocessing techniques are applied to reduce data volume and enhance point cloud quality. Based on this, a connector structure-aware faulty pin detection algorithm, named CSA-FPD, is designed to detect short and bent pins using limited data. The proposed DT-AFPD system is validated on 13 representative types of aviation electrical connectors, covering over 3600 pins. Experimental results demonstrate that the system achieves an average detection precision of 99.85%, effectively reducing the probability of EWIS reinstallation and enhancing the reliability of faulty pin detection.

Edge computing empowers the new generation of industrial cyber-physical systems to perform efficient distributed sensing, even under high data loads and frequent transmission demands. In the sensing process, observability is essential for complete state estimation and subsequent precise control. However, observability guarantee has become increasingly challenging due to the growing scale of sensing networks and the deployment constraints of sensing devices in industrial environments. For this problem, an observability guaranteed hybrid wired/wireless distributed edge sensing method is proposed, which optimizes accuracy and efficiency while guaranteeing observability. The dynamics-aware structural observability is proposed to bridge dynamics and observability under sensor scheduling. The capability of the system to achieve observability is quantitatively analyzed, and a novel necessary and sufficient condition for observability guarantee is derived. Furthermore, based on observability analysis and topology of networks, an energy-efficient heuristic algorithm is developed, which assigns wired transmissions between selected sensor–edge computing unit pairs for observability guarantee. Besides, deep reinforcement learning methods are adopt to improve sensing performance in the sense of expectation for wireless sensor scheduling, overcoming the difficulty of analytically expressing the objective function. Finally, our proposed method is applied to slab temperature estimation in the industrial hot rolling process, and its effectiveness is fully verified by simulation results.

Port underwater inspection is essential for ensuring maritime safety and operational continuity. However, low visibility and complex environments make it challenging to achieve reliable detection. This study addresses the issue of underwater target recognition based on multibeam sonar imagery. Specifically, we propose YOLO-ShuffleNet-BiFPN-EIOU, an enhanced YOLOv5 detection framework with three key improvements: first, the adoption of the lightweight backbone network ShuffleNetv2, second, the incorporation of a bidirectional feature pyramid network, and third, the optimization of the enhanced intersection over union loss function. Furthermore, we theoretically derive the maximum permissible velocity threshold for autonomous underwater vehicle (AUV), leading to a novel velocity-constrained controller that improves sonar imaging quality. A general transformation function is applied, and a functional dependence with quasi-linear characteristics between the independent and dependent variables is established, converting the partially constrained AUV system into an unconstrained one. Finally, experiments conducted in a pool and a real-world port demonstrate that the proposed method achieves significant improvements in accuracy and efficiency compared to YOLOv5m, with [email protected] increasing by 3.4%, [email protected]:0.95 improving by 5.3%, giga floating-point operations per second reduced by 89.8%, and the velocity-constrained AUV operation effectively enhances detection performance.

In mega low Earth orbit (LEO) satellite constellations, guaranteeing deterministic communication performance is essential for mission-critical applications and reliable service delivery. Unlike terrestrial networks, these large-scale satellite networks present unique challenges: highly dynamic topologies, significant propagation delays, limited on-board processing capabilities, and cumulative jitter uncertainty that increases with hop count. In this case, traditional deterministic mechanisms, i.e., Time-sensitive networking (TSN), become ineffective due to their inability to handle both the scale and dynamics of mega constellations. To address this issue, we propose a TSN-enabled LEO satellite network, whereby we derive single-hop delay upper bounds using network calculus, specifically accounting for high-priority preemption and dynamic satellite characteristics. Then we propose DomainR, a novel domain-based deterministic routing framework that effectively manages large-scale satellite networks through strategic network decomposition and adaptive routing. DomainR consists of two phases: first, a domain-based network compression phase that partitions mega constellations into manageable domains while preserving path continuity and routing efficiency, and, second, a delay budget-aware dynamic routing phase that adaptively allocates delay budgets based on aforementioned delay upper bounds and real-time network conditions. Through rigorous mathematical analysis, we derive the optimal domain sizing rules and establish a theoretical minimum domain count. Comprehensive simulations demonstrate that DomainR significantly reduces end-to-end jitter, improves network resource utilization, and capacity in mega LEO constellations.

In beyond 5G multi-cell networks, cell-edge users generally experience poor communication quality due to their greater distance from the base station (BS), and increased interference from neighboring cells, which severely impacts their user experience. To address this issue, achieving fair and efficient resource scheduling is key to ensuring the quality of service for edge users. Therefore, this paper formulates a joint optimization problem of spectral subband selection and power control, aiming to maximize the proportional fair sum rate of the multi-cell network. However, most existing algorithms require instantaneous global channel state information, which results in poor scalability and is impractical, especially in highly dynamic wireless network environments with user mobility. Noting that the considered problem can be modeled as a decentralized partially observable Markov decision process, we propose a multi-agent deep reinforcement learning (MADRL) scheme based on a hierar chical centralized training and distributed execution framework (MADRL-HE), enabling agents to make spectral subband and transmit power selections using only local information and some outdated non-local information. Simulation results demonstrate that the proposed scheme features excellent scalability and fast convergence. Moreover, its proportional fair sum rate performance consistently outperforms the existing MADRL scheme in dynamic environments and surpasses centralized iterative optimization schemes in most dynamic scenarios.

Underwater target tracking is a fundamental technology for marine development, providing real-time position estimates of the interested targets. However, due to the harsh underwater environment and the noncooperativity of targets, improving tracking accuracy remains a challenge, especially for highly-maneuvering targets. To address this problem, based on multi-autonomous underwater vehicle (multi-AUV) networks, this paper extends the idea of interacting multiple models (IMM) and designs a bearing-only cooperative tracking algorithm in the consideration of the harsh underwater acoustic channels. Specifically, in position prediction, the combination of historical information and the concept of IMM reduces the severe time-lagged effect in traditional prediction methods and the model reliance in standard IMM filters. Then, during position update, a rigidity-assisted relative position representation is designed based solely on bearing measurements, which alleviates the impact of information loss due to communication interruptions, significantly enhancing the continuity of target tracking. Moreover, the algorithm design also considers various uncertainties that may concurrently occur underwater (e.g., error accumulation and model mismatches), and robust optimization strategies with the principle of maximum entropy are designed to enhance the environmental adaptability. Through various simulations and field experiments, the advantages of the proposed method have been validated.

Web 3.0-enabled edge computing provides a promising foundation for decentralized application (DApp) deployment. However, optimizing execution modes for interdependent task patterns remains challenging due to high computational overhead and blockchain consensus latency. This paper proposes an elastic hybrid architecture for Web 3.0-Enabled Edge Computing Systems (EDGEWEB3.0) that integrates on-chain and off-chain execution. DApp contracts are modeled as Directed Acyclic Graphs (DAGs) and partitioned into dependent task patterns, enabling a structured decomposition of execution. We formulate a DApp task scheduling problem and introduce the Decision Transformer-based Dependent Scheduling (DTDS) algorithm. DTDS employs masked self-attention to capture DAG dependencies and incorporates regularization terms to enforce constraints on service delays, gas costs, and computing capacity. We implement a real-world Web 3.0 testbed based on Ethereum, Goerli, and zkSync. Experimental results demonstrate that DTDS consistently outperforms state-of-the-art baselines in gas costs and service delays, providing a scalable solution for DApp execution in environments.

In the evolving landscape of Web 3.0, deploying and scheduling decentralized applications (DApps) presents significant challenges due to the complexity of heterogeneous nodes, edges, and their intricate interactions. Traditional approaches, particularly graph-based reinforcement learning (RL) methods, often rely on homogeneous graphs, which fail to capture the diverse relationships inherent in heterogeneous Web 3.0 environments. This limitation results in inefficient resource allocation, suboptimal task scheduling, and unclear security requirements. To address these issues, this paper introduces the Heterogeneous Graph Deployment Scheduler (HGDS), a novel framework that leverages Heterogeneous Graph Neural Networks (HGNNs) to model users, edge servers, and DApp tasks within Web 3.0 environments, and incorporates RL to optimize DApp scheduling policies. HGDS captures heterogeneity in Web 3.0 environments by jointly modeling node–edge interactions and heterogeneous edge relationships, enabling the generation of dynamic, task-aware embeddings that integrate both node and edge features. RL is further employed to adaptively optimize scheduling and resource allocation based on real-time network feedback. Experiments on a Web 3.0 testbed show that HGDS outperforms baseline methods by 12.2% in reward, while reducing service delay and gas consumption.

The steel hot rolling production line is a representative Industrial Cyber-Physical System (ICPS), where accurate temperature estimation of the moving steel strip is crucial for ensuring product quality. Existing models of temperature dynamics in hot rolling face challenges due to the strip’s continuous motion and complex thermo-mechanical interactions. Many rely on continuous mathematical models that are difficult to apply for real-time estimation, or they neglect key factors. To address these limitations, we propose a discrete-time state-space model that incorporates air cooling, contact heat transfer, and deformation-induced heating. This formulation more accurately captures the actual rolling process and enables efficient temperature sensor scheduling for improved estimation accuracy. Building upon this model, we develop an Integrated Model Based Method (IMBM) that employs deep reinforcement learning for real-time perception and estimation of the temperature field in the continuously moving and deforming strip. Simulation results demonstrate that IMBM achieves accurate temperature estimation with low computational cost, exhibiting superior performance and strong practical applicability.

Underwater target tracking is a critical challenge in marine exploration and defense applications due to the unknown maneuvers of target and the complex marine environment. To overcome the above challenge, this paper develops a digital twin (DT)-driven unknown maneuver target tracking strategy via remotely operated vehicles (ROVs). In order to capture the maneuver characteristics of target, a state prediction-based DT framework is constructed, where the neural network learning strategy is designed to estimate the unknown state transition matrix of target. Based on the predicted target state, a reinforcement learning (RL)-based tracking controller is designed for the virtual ROVs in DT model, such that the optimal tracking policy from DT model can be implemented to physical ROVs. To reduce the matching error between virtual and physical ROVs, an RL-based optimization algorithm is conducted by using the data interaction between DT model and ROVs. Note that the DT-driven target tracking strategy not only can reduce the communication energy consumption by periodically feeding back the real-data of ROVs to the DT model, but also can relax the dependence of target maneuver model via the state prediction method. Finally, experimental results are provided to verify the effectiveness of our strategy.

Wireless control systems (WCSs) are gaining rapid development in industrial automation. Compared to the star topology, mesh networks offer greater compatibility for large-scale applications that require high reliability, scalability, and extended coverage. In WCSs, multiple control loops share the multi-hop mesh network, leading to non-negligible and long- span communication latency in critical flows, which can severely degrade the overall control performance. Additionally, the criticality of each control flow largely depends on the features of the physical plant dynamics and the mesh network configuration, which is essential to properly and exactly represent. Moreover, the online scheduling and reconfiguration for large-scale mesh network for WCSs also pose unique challenges. In this paper, we propose a mesh network scheduling mechanism based on cyber-physical sensitivity. Firstly, we model each control loop as a switched system to represent the impact of arbitrary and fluctuating communication latency. Second, we propose a novel online criticality indicator, cyber-physical sensitivity (CP-Sensi), which accurately reflects the criticality of each control flow by synthesizing the switched model, runtime physical states, and network conditions. Finally, we design a CP-Sensi-based scheduling mechanism and an efficient piggyback-based network reconfiguration protocol tailored for mesh networks. Extensive studies with 12 control loops demonstrate that the proposed CP-Sensi and online mesh network scheduling achieve superior control performance compared to state-of-the-art approaches.

This paper considers a 5G dense cellular network scenario where wireless channels are dynamically changing. To address the resource allocation problems in 5G dense networks, a distributed real-time and fair resource allocation algorithm based on a single deep Q-network (DQN) and multiple local deep neural networks (DNNs) architecture (DRFRA-SDML) is proposed. Specifically, each downlink is modeled as an agent. Each downlink is equipped with a local deep neural network (DNN), allowing each agent to input locally observed information into the local DNN to select spectral subband and transmission power in real-time. In the core network, the global experience replay buffer is utilized to collect local experiences collected by all local agents to train a global weight vector of the train DNN, which is shared by all local DNNs. This ensures that the computational complexity of each local agent does not depend on the size of the cellular network, resulting in excellent scalability. To achieve fairness between user devices, a reward function based on fairness has been designed. The simulation results demonstrate that DRFRA-SDML significantly outperforms IFP-FRA and FP-FRA-D in terms of sum rate in most scenarios, while ensuring fairness among user devices and achieving markedly better real-time performance.

The uncertainty of wireless communication seriously affects the control performance of the platoon. In this paper, a model predictive control (MPC) method based on dynamic event-triggering is proposed to address the impact of non-ideal communications on multi-vehicle systems. Firstly, a hierarchical platoon architecture combining backbone layer and sub-platoon layers is proposed to achieve efficient coordination among multiple platoons, and a communication-aware control mechanism is designed for each vehicle to effectively mitigate the adverse effects of non-ideal communication scenarios. Secondly, a dynamic event-triggering mechanism is established for each vehicle, which is related to time-varying delay and random packet loss parameters, so as to effectively reduce the frequency of MPC solutions and data transmission. Additionally, based on different communication topologies, distributed model predictive controllers are suggested for backbone layer and sub-platoon layer to achieve precise and comfortable tracking. Then, sufficient conditions are provided to ensure the recursive feasibility of the MPC algorithm and the stability of the platoon system. Finally, extensive simulations are conducted, and the results demonstrate the effectiveness of the proposed algorithm in terms of control performance and low computational complexity under non-ideal communication conditions.

Accurate positioning is essential for meaningful data collection in underwater acoustic sensor networks (UASNs), and localization has become a fundamental technology that provides real-time position estimates for sensor nodes. However, due to the harsh underwater environment as well as the difficulties and high expenses in network maintenance, localization in UASNs has always been a challenging problem. Different from previous works that rely on fixed anchors (e.g., buoys), this article uses an autonomous underwater vehicle (AUV) as a mobile anchor and proposes a reinforcement-learning-based trajectory planning method that allows the AUV navigation to meet the localization requirements of all sensor nodes. Specifically, based on gridded scenarios, this work models the node position uncertainties with information entropy and formulates AUV trajectory planning as a process of reducing the entropy of the whole network. Moreover, a modified actor-critic-based deep deterministic policy gradient (DDPG) reinforcement learning algorithm is designed to shorten AUV trajectory on the premise of ensuring a certain localization accuracy for UASNs. Through various numerical comparisons, the advantages of the proposed method have been validated in terms of efficiency and localization accuracy.

With the emergence of 5G technology, the demand for data communication between mobile users has significantly increased, and the number of cellular network infrastructure and mobile devices has also grown rapidly. However, a large number of base stations conducting wireless communication simultaneously inevitably brings serious interference due to the limited spectrum resources and dense distribution. Since the channels in the 5G dense cellular network with mobile users are complex and it is difficult to capture the channel state, the power allocation scheme adapting to a dynamic environment has become an important issue. In this article, a multiagent deep Q-network (DQN) distributed algorithm based on environmental awareness (MADQN-EA) is proposed. Specifically, the downlink between each base station and the user is treated as an agent, and a multiagent distributed approach is developed to improve the scalability of the algorithm. In response to the time-varying nature of the 5G dense cellular network environment, an environmental awareness training method is adopted. This method provides the agent with the opportunity to observe more changes in the 5G dense cellular network environment during the training process. This design significantly enhances the robustness of the proposed algorithm under the changing channel conditions. The proposed MADQN-EA is compared to fractional programming with a perfect CSI (FP-PC), multiagent DQN with experienced instance transfer (MADQN-EIT), and the random power selection scheme (Random). Simulation results show that MADQN-EA is robust against dynamic environment and achieves a higher sum rate performance.

Considering the harsh and complex environments in Underwater Wireless Sensor Networks (UWSNs), where the malicious nodes exist, the secure routing design is investigated in this paper. The malicious nodes are a serious threat to the security of sensor networks. This paper proposes a secure routing protocol, named Trust Based Routing (TBR), that focus on how to evaluate and find the malicious nodes and then determine the reliable routing. The core idea of TBR is that a new trust management model is designed by considering the various historical behaviors of nodes in the interaction process, which is able to assess the trust value of a node based on past behaviors between nodes and protect against potential attacks in the network. And the extra factors such as the remained energy of nodes, the distance between nodes and the trustiness are included in the routing criterions. Finally, a strategy for selecting relay nodes is proposed based on these considerations. Simulation results show that the proposed TBR algorithm can effectively defend against attacks from inside the network and is also more efficient and reliable compared to other existing routing protocols.

The confluence of Internet-of-Things (IoT) and artificial intelligence has advanced smart home (SH) systems, enabling the provision of complex scene-aware services. Central to these services is the precise perception of the indoor environment. Indoor scenes present unique challenges due to diverse layouts, frequent object occlusions, and dynamic human activities, which hinder the comprehensive understanding by individual SH devices/sensors. Moreover, the diversity of sensors equipped by SH devices introduces the multimodal data issue, necessitating the reconciliation of discrepancies among various data modalities. This article presents multimodal collaborative perception (MMCP), a collaborative perception paradigm for SH systems with multimodal raw data. MMCP leverages the intermediate collaboration framework and tailors it to an edge-assisted SH system. It deploys dedicated encoders at SH devices to convert multimodal raw data to uniform intermediate features, which are then sent to an edge computing box for aggregation and perception. MMCP introduces a critical information identifier to selectively transmit informative parts within intermediate features, thereby mitigating the communication overhead for bandwidth-constrained SH devices. Moreover, MMCP designs collaborative infomax (CIM) to facilitate intermediate feature aggregation. CIM defines multiview mutual information (MVMI) to capture dependencies between the aggregated feature and individual intermediate features from multiple SH devices. It employs contrastive learning to estimate and maximize MVMI in an unsupervised manner, such that the aggregated feature can retain discriminative information from individual intermediate features. We evaluate MMCP in four real-world indoor scene datasets. Experimental results show that MMCP outperforms noncollaborative strategy by 18% in average precision (AP). Particularly, MMCP strikes a favorable balance between perception performance and communication overhead, compressing intermediate features to a ratio of 13% while maintaining higher AP compared to state-of-the-art methods.

Edge computing-enabled virtual reality (VR) is increasingly explored in smart healthcare systems due to its potential to deliver immersive, real-time medical services. However, ensuring ultra-low latency and interpretable decision-making in such systems remains a significant challenge. In this paper, we propose an explainable deep reinforcement learning (XDRL)-enhanced VR video streaming framework for immersive healthcare systems to provide smooth and reliable VR services. Specifically, we first model the VR content analysis process at the edge sides and formulate a joint caching, communication, and computing (3C) resource optimization problem to maximize the VR quality of service (QoS) and minimize service latency. To address this complex 3C resource scheduling decision problem, we propose HXRL, a novel implementation of explainable deep reinforcement learning (XDRL), specifically designed to optimize immersive healthcare VR services. Comprehensive experiments show that our HXRL method improves average tile bitrate by 18.3%, cache hit ratio by 24.7%, and reduces system latency by 32.5% compared to baselines on real-world VR healthcare datasets. Moreover, a class activation map (CAM)-based visual analysis is integrated to interpret the learned policies of our model, highlighting the spatial attention of decision-making and enhancing trustworthiness in medical contexts.

Smart homes depend on collaborative sequential prediction tasks to optimize energy consumption and appliance scheduling. Federated learning (FL) offers a promising approach by enabling decentralized model training to balance privacy and usability. Yet, standard FL techniques fail to effectively address data diversity and individual user preferences in smart home contexts. To address these issues, we propose P3FL: a Privacy-Preserving Personalized Federated Learning framework that integrates tailored model training and privacy enhancements for federated collaborative predictions and decision-making. Our framework introduces the Personalized Collaborative Decision-Making (PCDM) algorithm, which dynamically adapts to different household environments while ensuring privacy and personalization. P3FL combines a global model for knowledge aggregation with a personalized adaptation module to provide fine-tuned predictions based on user preferences, environmental factors, and device configurations. Theoretical convergence bounds analysis confirms the robustness and efficiency of PCDM under conditions of strong convexity, smoothness, and bounded variance. Extensive experiments on real-world smart home datasets demonstrate that P3FL outperforms state-of-the-art methods, with PCDM achieving a training accuracy of 92.14%. Our approach enhances operational efficiency and ensures personalized user satisfaction, privacy enhancement in smart homes.

In response to carbon peaking and carbon neutrality, DC microgrids ( MG), as a key pillar, have facilitated efficient and reliable power transmission between renewable energy sources, energy storage devices, and various loads. In the process, the heavy reliance on communication networks exposes them to potential cyber-physical security risks. Namely, attackers can inject false data to achieve current or voltage overload without triggering an alarm by eavesdropping the communication data between the converter and MG center. For this reason, an attack detection and localization framework using data-driven is constructed in this paper. Utilizing the subspace identification methods, a data-driven I/O model aiming at sketch the process input-output data-based framework for DC-MG dynamic processes is established. Afterward, the necessary theory on the data collected for the observability and controllability of the proposed data-driven model is given. Based on this, an attack detection and localization framework for data-driven design of DC-MG system is presented. The proposed framework includes a bank of adaptive residual generators, adaptive detection threshold and localization observers, whose parameters can directly be obtained from process data. Finally, simulation tests on the meshed DC-MG system consisting of four distributed generation units are presented to demonstrate the superiority of the developed attack detection and localization framework.

This paper explores the computation enhancement problem in an intelligent reflecting surface (IRS) enabled wireless powered mobile edge computing system serving the intelligent transportation relying on powerful computational support. Initially, in the downlink, the base station (BS) with edge server transmits energy signals to the battery-powered roadside units (RSUs) with computing capabilities, which are grouped into distinct clusters. Subsequently, RSUs leverage the harvested energy for local computing and offloading their tasks to the BS by using a hybrid rate splitting multiple access (RSMA) and time division multiple access (TDMA) strategy. Finally, the computed outcomes from the edge server are transmitted for integration with the computations carried out locally at the RSUs. The objective is to maximize the minimal computation rate of RSU clusters, where the transmit power of the BS and RSUs, the downlink and uplink beamforming of the IRS, the CPU frequency of the RSUs, and the time slot assignment are jointly optimized. To address the bottleneck issues constraining computation rate, this paper proposes an iterative algorithm that combines sequential rank-one constraint relaxation and block coordinate descent methods. Ultimately, the simulation results confirm the effectiveness of the proposed algorithm in enhancing the weakest link constraining the system computation rate when contrasted with the baseline algorithms.

Underwater acoustic sensor networks (UASNs), which are formed by a number of interconnected mobile vehicles and static sensors, have emerged as a promising solution to explore and utilize the ocean resources. Typically, the localization, communication and control are the fundamental services for the applications of UASNs. Although they are closely related, the localization, communication and control issues are usually separately tackled. The separate design directly affects the localization accuracy, transmission reliability and control efficiency, especially for resource-constrained UASNs. In this regard, it is essential and necessary to co-design the localization, communication and control systems for UASNs. At present, the theoretical framework of the above integration design is still in the construction phase, and some key problems remain unresolved. Therefore, this article aims to give a survey on the integration design of localization, communication and control for UASNs. We first present the communication architecture, through which the main challenges aiming at the integration design are analyzed. After that, a holistic survey on the underwater localization, communication and control basics is provided. Followed by this, the recent advances on the integration design are given. At last, we make an outlook to the future research directions on the integration design of localization, communication and control for UASNs.

With the development of Industry 4.0, mobile agents are deployed to coordinate with multi-loop control systems to perform manufacturing tasks, leading to heterogeneous Internet of Things (IoT). Due to shadow fading induced by the movement of mobile agents, the wireless channel closing the control loops is inherently unreliable, which can compromise the control system performance. This paper addresses the co-design problem of transmission scheduling and agents’ movement to ensure both control performance and energy efficiency. A generalized cyber-physical-agent framework is proposed to capture the coupling between IoT systems and a mobile agent through a state-dependent fading channel. Moreover, the movement of the mobile agent among areas exhibiting different levels of shadow effects is modeled by Markov decision process. To address such heterogeneous dynamics, the co-design problem is formulated into the optimization of a logic-based stochastic switched system by utilizing the semi-tensor product technique. Based on state mergence andH-representation methods, a tractable and effective algorithm is then proposed to design co-design policies, minimizing the average joint cost of communication and control. A parallel scheme is further developed to alleviate the computation burden of the algorithm. Theoretical guarantees are provided to ensure control system performance. Finally, simulation results are given to demonstrate effectiveness of the proposed method.

Security issues of cyber-physical systems have attracted more and more attentions these years. In this letter, we investigate an energy-constrained optimal hybrid fake-acknowledge attack strategy on state estimation over infinite-time domain, where an attacker will choose certain time instants to generate or block (denoted as Phase I and Phase II, respectively) the feedback flag-acknowledgment (ACK) signals based on a given sensory transmission schedule (STS). We first provide an explicit threshold form of the optimal STS under power constraint. Based on this, the optimal attack strategy for Phase I is presented and proved to satisfy a threshold form. We continue to discuss the optimal state distribution at the threshold, which contributes as a bridge to link these two phases, and consequently derive out the optimal attack strategy for Phase II. Moreover, the condition of convergence is also analytically provided. Comparisons with other feasible schedules are accomplished to verify the correctness and effectiveness of our proposed results.

In this letter, anestimation-oriented power-constrained sensor scheduling problem over multi-hop sensor networks is studied. Two different online scheduling schemes for multi-hop transmission, i.e., global-delay-based scheduling (GS) and covariance-based scheduling (CS) are proposed, respectively. We propose a stochastic triggering scheme to satisfy power constraints. A Markovian model is adopted to formulate the state transition relationship in the scheduling process. In order to calculate the switching threshold and selection probabilities, an algorithm for parameters determination is further properly designed to overcome the coupling property between hops. We have explicitly analyzed the performance of GS by figuring out the upper and lower bounds of cost. Moreover, the superiority and optimality of CS have been theoretically proved by using the optimal state distribution method. Numerical simulations and comparisons with existing methods have been conducted illustratively to verify the correctness and effectiveness of our proposed schemes, algorithms and results.

The security issues of cyber-physical systems have received wide attentions in recent years. In this letter, we consider an offline periodic Denial of Service (DoS) attack scheduling problem against the transmission between sensor and estimator over infinite-time horizon. In order to handle offline infinite-time scheduling, different from existing methods which only solve online scheduling problem or provide implicit sub-optimal solutions, we propose a novel approach based on sub-schedules and sub-costs to describe the infinite-time schedule and its cost. Furthermore, an interesting property of periodic decline is discovered for the state distribution, thus we equivalently transform the primal scheduling problem into a finite-time one within a period with respect to the introduced sub-schedules. By analyzing the optimal state distributions within a period, we derive out the global optimal attack schedule in an analytical and explicit form. Numerical comparisons with other schedules (including the online one obtained by the state-of-the-art reinforcement learning-based method) validate the correctness and superiority of the proposed schedule and approach.

Massive field data is wirelessly transmitted to the edge side to facilitate sensing and control in the emerging Industrial Internet of Things (IIoT) systems. Under the expanding transmission scheduling space and dynamic network conditions, balancing control performance and limited transmission resources is a fundamental challenge. For this problem, we propose a novel deep reinforcement learning (DRL)-based transmission scheduling method (DTSM), where sensing performance guarantee is introduced for its criticality in ensuring complete system observation and effective control. Specifically, taking system observability as the key metric, the time slots for multi-sensor data transmission under different control demands are properly reserved with theoretically guaranteed performance. Then, the primal-dual DRL framework is adopted to further improve the overall performance of system control and resource utilization by dynamically scheduling the transmission number of each sensor. The scheduling is based on the real-time states of sensing and wireless network, and the action space is determined according to our reserved time slots. Besides, after primal-dual updates, the scheduling results can satisfy the estimation error-evaluated constraint imposed for the ultimate control effect. Finally, the proposed method is applied to the industrial laminar cooling process and its effectiveness is fully demonstrated. Note to Practitioners—This paper is motivated by the requirement of balancing control performance and scarce transmission resources in industrial automation fields such as steel manufacturing, where massive sensor data is transmitted to the edge side through wireless networks. The expanding transmission scheduling space and dynamic network conditions have led to increased interest in advanced deep reinforcement learning (DRL) methods. However, few previous works have explored the impact of control demands on intelligent transmission scheduling design. For these issues, we propose a novel DRL-based transmission scheduling method (DTSM), where the time slots for multi-sensor data transmission are delicately reserved according to different control demands and dynamic scheduling is realized based on real-time states of sensing and wireless network. The overall performance of system control and resource utilization is improved, and practitioners can easily adjust method parameters to achieve the desired balance between the two aspects according to practical demands. Case studies in the industrial hot rolling process demonstrate the superiority of DTSM. Our future work will consider the joint scheduling of uplink-downlink transmissions and design the collaboration among multiple edge computing nodes (ECNs) to address the limitations of centralized learning methods. Besides, the proposed method can be extended to other industrial applications such as flight control system testing.

State monitoring plays an important role in industrial automation, where smart sensors are deployed in production sites to estimate the state of physical processes. Critical monitoring information is expected to be delivered timely to ensure estimation performance. However, ensuring the necessary data rate for such data transmission under bandwidth limitations requires larger transmission power, increasing the energy consumption of devices. To address this contradiction between estimation performance and energy consumption, a distributed estimation and communication co-design scheme using a cross-layer optimization technique is proposed in this paper. An event-triggered quantized distributed estimation algorithm is proposed to reduce energy consumption. Then, the impact of system dynamics, event-triggered threshold, and number of quantization bits on the convergence of estimation errors is investigated. Based on this relationship, the quantization in the application layer, event-triggered communications in the transport layer, and transmission power in the physical layer are jointly optimized. This constrained minimization problem is formulated as a mixed-integer nonlinear programming problem and solved with a cross-layer optimization method based on the alternating direction method of multipliers. The global convergence of the optimization method is analyzed. Finally, a numerical case study in the hot rolling process shows the superiority of the co-design scheme in balancing estimation accuracy and energy consumption.

Cooperative localization aims to continuously provide position estimates for multiple-autonomous underwater vehicle (multi-AUV) systems during task execution such as marine monitoring; it is preferable over noncooperative schemes due to its high accuracy and strong robustness. However, various uncertain factors underwater, including model mismatches, accumulated errors, measurement noises and biases, time-varying communication channels, etc., still challenge the accuracy and robustness of cooperative localization. When such uncertainties arise, the performances of traditional methods degrade significantly. Therefore, this paper proposes a robust multi-AUV cooperative localization method that is able to combat these uncertainties by leveraging the principle of maximum entropy. To be explicit, a message-passing scheme is established using factor graphs, over which a distributed position estimation strategy for AUVs is designed based on belief propagation. To reduce the damages of uncertainties, maximum-entropy distributions are designed respectively for the prediction and correction processes of localization and are realized by particles. Specifically, through enlarging the particle coverage, uncertainty-induced misleading in position estimation is alleviated, and hence higher robustness is achieved. Simulations and field experiments show the advantages of the proposed algorithm over the state-of-the-art methods in terms of localization accuracy, robustness, and scalability.Note to Practitioners—In practical applications, due to the unavailability of global positioning systems underwater, AUV localization still lacks mature and stable solutions. In harsh underwater environments, most theoretical models often fail to accurately describe the practical conditions, leading to widespread mismatches, which severely degrade localization performance. Moreover, uncertainties such as accumulated errors, measurement noises, and position deviations would further reduce the position estimation accuracy. Existing methods usually consider these uncertainties independently, while, in practical uses, uncertainties often emerge in combination, making it challenging to maintain localization accuracy. What is worse is that most sophisticatedly designed algorithms pursue high accuracy, and their adaptability in engineering applications is difficult to guarantee. Hence, this work provides a robust solution for multi-AUV cooperative localization that can handle uncertainties simultaneously. By reducing the sensitivity to different uncertainties, the proposed algorithm can provide sustained high-accuracy position estimates for AUVs in complex underwater environments. The proposed method is experimentally verified and suitable for multi-AUV applications such as oceanic rescue, resource development, and marine monitoring.

Flight control system testing (FCST) is one of the most important process to check whether flight control surfaces can operate properly according to commands during aircraft assembly. Traditional testing method relies heavily on manual labor, leading to low efficiency and inconsistent quality. In this paper, we apply digital twin (DT) technology to the FCST process for the first time. We firstly design an architecture of DT-enabled FCST including four layers to support further development. Then, we present a triangular mesh alignment-based angle measurement (TMA-AM) algorithm to efficiently collect deflection angle data for DT-enabled FCST. Extensive experiments conducted on a aircraft wing subassembly platform show that the TMA-AM algorithm achieves an average angular measurement error of less than 0.1°, outperforming existing methods. Moreover, we develop a virtual experimental platform named DT-FCST aligned with a real aircraft wing subassembly platform. In addition, TMA-AM algorithm is integrated with the DT-FCST platform. By integrating real-time data from the cockpit, real-time physical-virtual interaction of aircraft control sticks and flight control surfaces are achieved, ensuring consistency between physical and virtual movements. The integration of DT technology with the TMA-AM algorithm enables real-time synchronization, monitoring, and unified data management, significantly enhancing the efficiency and accuracy of the FCST. Note to Practitioners—To address the inefficiencies and low monitoring quality associated with traditional manual testing methods in flight control system testing (FCST), we firstly introduce digital twin (DT) technology to this process. To support effective and accurate measurement during the FCST, we propose a vision-based method tailored to accurately measure deflection angles of flight control surfaces. This method replaces manual measurements with a non-contact approach, significantly improving measurement accuracy and efficiency. We provide a detailed description of the construction process of the DT-FCST platform including requirement analysis, DT model construction, and on-site experiments. This DT-based approach achieves real-time synchronization between virtual and physical testing processes, enhancing monitoring quality and overall testing effectiveness. Specifically, it can achieve a 90% reduction in the number of operators and shorten the single testing time to 16.7% of the traditional testing method.

Multi-energy systems with energy storage can coordinate various energy carriers to facilitate the integration of large amounts of distributed energy sources and promote the overall efficiency of energy use, which needs distributed dispatch with the requirement of security and privacy. This paper studies the distributed economic dispatch based on information from neighboring agents only. In order to handle the non-convexity due to the complementarity constraint of energy storage, it is proved that simultaneous charging and discharging is suboptimal for the multi-energy systems. Based on this, an equivalent convex problem is reformulated. A scalable distributed algorithm based on parallel ADMM and dynamic consensus mechanism, termed DEED-ADMM, is then proposed. It is shown that DEED-ADMM is scalable in terms of per-agent energy consumption and computational complexity with centralized methods. Moreover, under general convex cost functions, convergence properties of DEED-ADMM are theoretically analyzed by adopting the Lyapunov-based approach. It is proved that the primal problem and the dual problem can be simultaneously solved. Finally, case studies demonstrate the effectiveness of the proposed algorithm. Note to Practitioners—This paper is motivated by the problem of coordinating various energy carriers as well as energy storage in multi-energy systems to promote the overall efficiency of energy use. The coupling among different energy carriers and the complementarity constraint of non-simultaneous charging and discharging of battery storage make the problem non-convex. Existing distributed approaches require stringent assumptions on the cost functions, or suffer from a heavy computational burden. To address the above challenges, a fully distributed algorithm is developed, which is scalable and suitable for large-scale systems. Moreover, it is the first distributed algorithm that solves the economic dispatch problem and the dual problem simultaneously in multi-energy systems with general convex cost functions. Practitioners can easily adjust the coefficients of the proposed algorithm to guarantee convergence for the IEEE 30-bus or even 116-bus systems, as long as the economic dispatch problem is feasible. Our future work will focus on designing resilient mechanisms under potential attacks and considering more practical situations such as power loss.

This paper addresses the fault-tolerantH∞output regulation problem of multi-agent systems (MASs) subject to input saturation, structural uncertainties, and actuator faults. TheH∞output regulation problem is reformulated as a distributed two-step zero-sum game problem to enhance both steady-state and transient performance in the presence of disturbances. A novel anti-saturation reinforcement learning algorithm with a feedforward-feedback structure is proposed, enabling saturation-free optimal output regulation while effectively mitigating both modeled and unmodeled disturbances. An active fault-tolerant control (FTC) approach based on the anti-saturation policy algorithm is also introduced to compensate for actuator faults and structural uncertainty. The salient feature of the proposed algorithm is its ability to prevent saturation during optimal control policy learning, while improving both steady-state and transient performance. Finally, simulation studies are conducted to validate the effectiveness of the proposed approach.

Secure data transmission and real-time state updates are critical yet challenging requirements for Cyber-Physical Systems (CPS) to maintain stability under adversarial conditions. While existing works have separately explored covert communications for security and Age of Information (AoI) optimization for timeliness, their interdependencies remain unaddressed, leading to suboptimal trade-offs between detection resistance, control performance, and resource efficiency. To bridge this gap, this paper proposes a novel co-design framework that jointly optimizes covert communication and AoI-aware control strategies. First, we rigorously derive a linear relationship between average AoI and control cost, termed the Cost of Update Delay (CoUD), which quantifies how outdated information exacerbates state fluctuations and increases stabilization efforts. Building on Kosta et al.’s Geo/Geo/1 queuing model, a closed-form expression for average AoI is further established as a function of sampling rate and packet delivery probability, explicitly linking communication parameters to control efficacy. Subsequently, this paper formulate a constrained optimization problem to minimize CoUD while guaranteeing covertness against eavesdroppers, leveraging Dinkelbach’s transformation and Lagrangian duality to decouple nonlinear constraints, derive optimal sampling rates, and transmission powers. Simulation results demonstrate that the proposed co-design framework achieves significant reductions in CoUD and superior freshness compared to baseline methods, while robustly maintaining covertness requirements. Notably, the proposed integration of sampling rate adaptation into the detection error rate model markedly enhances both AoI performance and resource efficiency, outperforming state-of-the-art disjoint designs. Therefore, this work provides a unified methodology to harmonize security, timeliness, and stability in resource-constrained CPS.

It is one of the most essential processes in the industrial cyber-physical system (ICPS) that multiple edge computing nodes (ECNs) collect field sensor information and cooperate for sensing and control. The exchange of sensing information on the edge side is critical for these ECNs that serve as multiple edge estimators and one edge controller. However, the limited transmission resources and the diverse performance demands of ECNs for sensing and control make it challenging to design the edge network topology delicately. For this issue, a novel dynamic observability (DO) condition is proposed to balance sensing-control performance and transmission cost under various demand settings. Based on the quantitative analysis of the relationship between overall transmission cost and each ECN's effective observability, DO gives the criterion for desirable network topologies with spatio-temporal dynamics. Then for the given performance demands, a dynamic observability guaranteed method (DOGM) is proposed to determine the network topology by triggering proper sensing links. In this way, the set of triggered sensing links may vary in a dynamic manner to satisfy the DO condition, and the overall performance of sensing and control is theoretically guaranteed. Finally, the comprehensive advantages of DOGM are demonstrated by the simulation study in the hot rolling laminar cooling process.

This article investigates resilient source seeking problem of second-order multirobot systems (MRSs) under mixed cyberattacks, which consist of misbehaving and Denial-of-Service (DoS) attacks. The misbehaving attacks can cover several types of malicious attacks, such as false data injection, stubborn, and Byzantine, while the network connectivity may be compromised by DoS attacks, potentially resulting in a time-varying and disconnected digraph. To this end, a resilient source seeking algorithm is proposed by designing an auxiliary point for each agent such that the coordination problem is transformed into a point tracking one. A reference velocity is calculated to guide benign robots toward the source, leveraging their historically optimal positions with the highest signal strength. This ensures the auxiliary points converge to the source, clustering benign robots nearby. When DoS attacks occur on some edges, the latest sampling data acquired before the attacks is used to hold the control signals for the robots. Then, sufficient conditions are established through rigorous stability analysis. In comparison to existing methods, the proposed approach extends the safe-kernel-based resilient consensus algorithms to a resilient source seeking algorithm for a general discrete-time second-order dynamics, while also can withstand a mixed cyberattack comprising both misbehaving and DoS attacks. Finally, simulation and experimental results are presented to validate the efficacy of the proposed algorithm.

Imbalanced image classification faces critical challenges in balancing the quality and diversity of synthetic minority samples. This article proposes the improved estimation distribution algorithm-based latent feature distribution evolution (MEDA_LUDE) algorithm, an evolutionary algorithm-assisted deep distribution learning framework that optimizes latent feature distributions through a multivariate Gaussian mixture (GM) assumption and a novel four-phase training strategy. We introduce a large-margin GM (L-GM) loss to dynamically model covariances for feature learning and design a MEDA that evolves latent features via a similarity-guided fitness function, thus enhancing diversity while preserving synthesis quality. Extensive experiments demonstrate significant improvements: MEDA_LUDE achieves 95.9% accuracy on MNIST (imbalanced ratio-IR:100), surpassing state-of-the-art methods by 1.26% on CIFAR-10. For industrial fabric defect data sets, it elevates accuracy by 1.45% on DHU-FD and 0.92% on ALIYUN-FD, especially with precision and G-mean improvements of 2.5% and 1.17%, respectively, on DHU-FD. Visualizations confirm that MEDA_LUDE generates minority samples with superior quality-diversity tradeoffs. The framework's success in real-world fabric defect classification underscores its practical value in addressing imbalanced learning challenges.

Localization is a key premise for implementing the applications of underwater acoustic sensor networks (UASNs). However, the inhomogeneous medium and the open feature of underwater environment make it challenging to accomplish the above task. This paper studies the privacy-preserving localization issue of UASNs with consideration of direct and indirect data threats. To handle the direct data threat, a privacy-preserving localization protocol is designed for sensor nodes, where the mutual information is adopted to acquire the optimal noises added on anchor nodes. With the collected range information from anchor nodes, a ray tracing model is employed for sensor nodes to compensate the range bias caused by straight-line propagation. Then, a differential privacy (DP) based deep learning localization estimator is designed to calculate the positions of sensor nodes, and the perturbations are added to the forward propagation of deep learning framework, such that the indirect data leakage can be avoided. Besides that, the theory analyses including the Cramer-Rao Lower Bound (CRLB), the privacy budget and the complexity are provided. Main innovations of this paper include: 1) the mutual information-based localization protocol can acquire the optimal noise over the traditional noise-adding mechanisms; 2) the DP-based deep learning estimator can avoid the leakage of training data caused by overfitting in traditional deep learning-based solutions. Finally, simulation and experimental results are both conducted to verify the effectiveness of our approach.

With the ability to provide deterministic transmission, time sensitive networking (TSN) has been widely used in various industrial scenarios. However, most of the existing TSN research focuses on traffic scheduling over predefined network topology. In industrial applications, optimizing network topology can reduce the number of network devices and the length of cables thereby lowering material and management costs, yet considering topology within TSN scheduling greatly increases the problem’s complexity. In this article, we first incorporate topology planning and traffic scheduling together into the TSN network design problem (TDP). A mathematical model for TDP is formulated, with the objective of minimizing the total weight and cost of a TSN network while satisfying the end-to-end deterministic transmission requirements. Subsequently, we establish the metric of capacity of TSN flow groups (CoG), and the proposed CoG estimation method enables feasibility assessment of TDP solutions. Since CoG measures a network’s capacity to accommodate TSN flows, we utilize it as the evaluation metric within our heuristic algorithm, CoG analysis based TSN network design algorithm (CATDA), reducing ineffective searches and enhancing the solution efficiency of TDP. Experiments show that compared to other algorithms, the proposed CATDA achieves the lowest-cost TSN network design solution and performs over 100 times faster than other algorithms.

Industrial flexible production, such as flexible assembly incorporating multiple processing machines, aims to operate in a high-quality and low-energy-consumption fashion. This article proposes a data-driven integration approach for scheduling and control to enhance product quality and energy efficiency. Closed-loop dynamic prediction is crucial for the integration of scheduling and control. Its accurate acquisition remains challenging in unknown nonlinear multimode production processes. First, the dynamic linearization (DL) method is used to transform the unknown nonlinear process into an equivalent linear form suitable for different time scales. Then, a closed-loop dynamic real-time optimization (CL-DRTO) problem is formulated at the scheduling layer to select the appropriate processing modes and assign cost-optimized production objectives to them. The control action for the closed-loop dynamic prediction is generated by a model-free adaptive predictive control. It is designed as a control subproblem based on DL and embedded into the CL-DRTO problem. Finally, the equivalent equality and inequality constraints for the control subproblem are established through the KKT conditions, simplifying the complexity of solving the optimization problem. The feasibility of the data-driven CL-DRTO algorithm is demonstrated through a thread-tapping process. The DL methods achieve a mean absolute error of less than 3%. The diversity of DL structures enables flexible controller design. Compared to open loop DRTO, the feedback adjustment mechanism of CL-DRTO enables the system to achieve the desired value more rapidly and accurately. On average, the CL-DRTO algorithm under single-mode operation ensures production quality with a 6% energy saving. The multimode operation further enhances energy efficiency by flexibly switching between modes.

Simultaneous target approaching is a fundamental ability for multiple autonomous underwater vehicle (multi-AUV) systems to continuously move toward the target for surrounding, capturing, information gathering, etc., and the efficiency and accuracy of the approaching heavily rely on the qualities of planned trajectories. This work presents a cooperative trajectory planning method for the simultaneous approaching of multi-AUV systems, and further provides strategies for connectivity maintenance and position errors to enhance the adaptability in practical uses. To be specific, a cooperative planning framework is designed with the inspiration of sensor selection, where trajectory planning is converted to a successive candidate selection problem and modeled as convex optimization. Under the framework, the considered simultaneous approaching, connectivity maintenance, and position errors are formulated as constraints and integrated into one convex optimization problem, which can be easily solved via well-established solvers. Various simulations and field experiments have validated the advantages of the proposed method by comparisons with alternative methods.

The prediction of sintering quality indicators is essential for the automation of the ironmaking process. However, various challenges, including variable time delays and relatively weak correlations, present difficulties in achieving precise modeling of the sintering process. To address these challenges, we introduce a novel method based on the nonuniform time delay embedding technique for predicting sintering quality indicators. First, we construct input variables with different compensated time delays. Second, we develop a low-dimensional approximation of the joint mutual information criterion to search the optimal variables and time delays. This approach effectively leverages valuable information from process variables while minimizing irrelevant information. Next, we implement a hybrid modeling technique that combines extreme gradient boosting and support vector regression to predict sintering quality indicators, allowing for adaptation to various operating conditions. Finally, we validate the effectiveness of our proposed method using real production data, specifically focusing on FeO content.

Intelligent fault diagnosis of photovoltaic (PV) arrays in small-sample scenarios remains challenging due to poor model accuracy and generalization. Existing methods fail to simultaneously address issues of varied operation conditions and insufficient samples, leading to the limited applicability of models built by few-shot learning. In addition, factors, such as data transmission and computation costs, also need to be considered. Therefore, this article proposes a cloud-edge collaborative self-correcting-guided generalized contrastive learning framework for small-sample PV fault diagnosis. First, an end-to-end self-correcting model is proposed to eliminate the influence of variable environments. Then, a self-correcting scheme is integrated with contrastive learning to achieve model generalization, and a type screening method is designed to improve model accuracy. Furthermore, a fast fault filtering mechanism is proposed to enhance the algorithm efficiency with cloud-edge collaboration. Both simulation and real data are utilized to validate the proposed method.

Joint estimation is crucial in the industrial Internet of Things (IIoT) by integrating data from diverse devices to improve monitoring accuracy. Network slicing can meet the heterogeneous needs of devices through logical isolation. However, existing methods often overlook the interaction of multiple slices on estimation performance, leading to potential estimation bias and ineffective resource costs. To address this, we propose an Age of Task (AoT)-driven associated network slicing method tailored for joint estimation scenarios. Specifically, we design an association-oriented slicing architecture for joint estimation that considers both the heterogeneous requirements of individual slices and the interactive effects of multiple slices. We define slice association based on the AoT to quantify the coupling relationship between slicing strategies and estimated performances. Moreover, we develop a dynamic-fitness multivariable particle swarm optimization algorithm to achieve associated slicing. Simulation results show that the associated slicing scheme achieves a flexible balance between timeliness and accuracy.

Real-time transmission of high-frequency signals in online fault diagnosis challenges the limited bandwidth. However, reducing the volume of data transmission will compromise the data quality and drop the accuracy of fault diagnosis. This article synergizes data transmission and fault diagnosis to simultaneously achieve high transmission reduction ratio and fault diagnostic accuracy. First, a novel long sequence dual prediction scheme (L-DPS) is proposed to reduce the high-frequency data transmission online while ensuring the data precision. Second, a resource-efficient transformer model is proposed to improve the precision and speed of long sequence prediction in L-DPS, thus effectively improving its transmission reduction ratio and applicable frequency. Finally, a precision-adaptive fault diagnosis model is proposed to tackle precision differences in the transmission-restored data, thus effectively improving the accuracy of fault diagnosis. Experiments based on the real-world dataset confirm that the solution can cope with high-frequency data up to 20.69 KHz and achieve 94.16% transmission reduction and 99.43% fault diagnosis accuracy.

As an emerging communication technology, time-sensitive networking (TSN) holds the potential to enable real-time and deterministic interactions for streams within the Industrial Internet of Things. However, effectively and promptly scheduling large-scale streams in the TSN network poses a significant challenge due to high computational complexity. In this article, we conduct a schedulability analysis to preprocess the stream set with given routing paths, avoiding invalid searches and providing optimized guidance for stream routing. To accelerate the feasibility validation of potential solutions, an efficient stream conflict detection approach is proposed leveraging stream grouping with correlation analysis to compress the detection space. Integrating the above preprocess and efficient conflict detection, we develop a scalable scheduling algorithm with an incremental schedule synthesis to enhance scalability while ensuring low slot occupancy for all links. Evaluation results demonstrate that the proposed algorithm significantly reduces synthesis time and achieves low slot occupancy of all links compared to existing scheduling methods.

Industrial time-sensitive networking (TSN) is pivotal for ensuring real-time and reliable flow transmission. There is a growing focus on its scalable scheduling for time-critical flows pursuing ultralow latency and jitter. Its time-aware shaper protocol tackles uncertain delay and frame loss but introduces high scheduling complexity. However, existing works lack a scheduling feature mining mechanism. They impose unnecessarily tight rules to simplify the problem but sacrifice scheduling optimality. To address this, especially in industrial networks with large-scale complex flows, we propose a flow-overlap graph based distributed scheme to improve scheduling scalability concerning schedulability, scheduling efficiency, and latency and jitter. The distributed framework is established with the pipeline-parallelism pattern and verified superior in scalability. It first incorporates the deterministic feature into the distributed TSN configuration standard. Under this, specific scheduling is refined by building a so-called flow-overlap graph that efficiently characterizes flow-based scheduling features and further designing a hierarchical scheduling algorithm GFD. This scheme Pareto dominates the three scalability criteria theoretically and simulatively.

Formation control of autonomous underwater vehicles (AUVs) has been regarded as the basis of many sophisticated marine missions. However, the complex marine environment and the weak acoustic communication on AUVs make it hard to achieve the formation task. This paper attempts to overcome the above challenge from graph theory and intelligent learning perspectives. A local topology estimator is first designed by observing the coupled state evolution of AUVs, such that the unknown interaction relationship of AUVs can be inferred on the basic of local sensing. Based on this, we adopt the graph direction and contraction to generate an optimally persistent topology for AUVs, whose aim is to reduce the communication redundancy and guarantee the topology connectivity. With the optimized network topology, a model-free inverse reinforcement learning (IRL) formation controller is developed for AUVs to keep the desired formation shape. The innovations can be summarized as follows: 1) the local topology estimator can reveal the interaction topology relationship of AUVs with multiple degrees of freedom (DOF); 2) the optimally persistent topology can balance energy efficiency and topology connectivity as compared to the neighboring rule-based solutions; 3) the IRL-based formation controller has better adaptability to the underwater unknown environment as compared to the traditional reinforcement learning solutions. Finally, simulation and experimental results are both conducted to verify the effectiveness of our solution.

Intelligent reflecting surface (IRS) and rate-splitting multiple access (RSMA) technologies are at the forefront of enhancing spectrum and energy efficiency in the next generation multi-antenna communication systems. This paper explores a RSMA system with multiple IRSs, and proposes two purpose-driven scheduling schemes, i.e., the exhaustive IRS-aided (EIA) and opportunistic IRS-aided (OIA) schemes. The aim is to optimize the system weighted energy efficiency (EE) under the above two schemes, respectively. Specifically, the Dinkelbach, branch and bound, successive convex approximation, and the semidefinite relaxation methods are exploited within the alternating optimization framework to obtain effective solutions to the considered problems. The numerical findings indicate that the EIA scheme exhibits better performance compared to the OIA scheme in diverse scenarios when considering the weighted EE, and the proposed algorithm demonstrates superior performance in comparison to the baseline algorithms.

Time-sensitive networking (TSN) is proposed to satisfy the increasingly stringent demands of Industrial 4.0 for deterministic transmission. This is achieved by generating a series of centrally configured gate control lists to strictly restrict the forwarding time of arriving flows. However, such a centralized scheme requires prior information of all flows, severely impeding TSN from providing an online response to dynamic industrial applications. To solve this problem, we innovatively propose to use admission control (AC) to realize deterministic transmission in the virtualized TSN network. In this approach, AC is distributively executed on each node and link, whereby flows of applications are served by passing through a series of virtual network functions (VNFs). This distributed AC execution is regarded as a VNF embedding (VNE) process. Specifically, we propose a two-stage online framework, Smart Admission Control (SmartAC), to cater to dynamic applications. The first stage, referred to as thestatic stage, obtains a deterministic VNE solution by synthesizing AC decisions of individual TSN nodes and links. The second stage, referred to as thedynamic stage, fine-tunes VNE solutions obtained from thestatic stageto adapt to the harsh environment with insufficient resources or limited VNF migration budgets. Simulation results demonstrate the effectiveness of SmartAC in improving response rate and resource utilization ratio. Notably, SmartAC reduces runtime by 90% compared to existing algorithms and exhibits robustness across different network topologies.

Flocking control of autonomous underwater vehicles (AUVs) has been regarded as the basis of many sophisticated marine coordination missions. However, there is still a research gap on the flocking of AUVs in weak communication and complex marine environment. This article attempts to fill up the above research gap from graph theory and intelligent learning perspectives. We first employ the bearing rigidity graph to describe the topology relationships of AUVs, through which an iterative gradient decent-based localization estimator is provided to obtain the position information. In order to improve the localization accuracy and energy efficiency, a min-weighted bearing rigidity graph generation strategy is developed. Along with this, we adopt the semi-supervised broad learning system (BLS) to design the model-free flocking controllers for AUVs in obstacle environment. The innovations of this article are summarized as follows: 1) the min-weighted bearing rigidity-based localization strategy can balance the localization accuracy and communication consumption as compared to the neighboring rule-based solutions and 2) the semi-supervised broad learning-based flocking controller can decrease the training time and solve the label limit over the supervised learning-based controllers. Finally, simulation and experimental studies are provided to verify the effectiveness.

In the operation of multiple autonomous underwater vehicle (AUV) networks, AUV self-localization, inter-AUV communication, and AUV trajectory planning (collectively referred to as LCP) are fundamental technologies. Among them, accurate position estimation is often a prerequisite for effective planning, while communication is essential in both parts. Previous studies have typically addressed these three areas independently, yet their conflicting demands on resources, objectives, etc., frequently pose significant challenges to performance improvement. This paper investigates the LCP from an integrated perspective and a co-designed approach jointly addresses all of them is proposed based on graphical models and belief propagation. Specifically, guided by the various underwater communication conditions, a cooperative localization algorithm for AUVs is designed with the assistance from trajectory planning. Different from the previous, planning results are directly used for localization to resist harsh communications, rather than indirectly affecting localization accuracy by, for example, optimizing geometric relationships. As a result, the feedback enhancement from trajectory planning to AUV localization is realized and the mechanism of their mutual promotion for different communication conditions is achieved. Moreover, considering the changing trajectories, the proposed cooperative localization algorithm is further refined for underwater obstacle-avoidance scenarios. Various simulations and field experiments validate that the proposed algorithm can improve the overall performance of LCP by comparisons with state-of-the-art alternative methods.

Formation control of autonomous underwater vehicles (AUVs) has been regarded as the basis of many sophisticated marine missions. However, the complex marine environment and the high communication energy consumption make it hard to achieve this task. This article is concerned with an energy-efficiency formation issue of AUVs via angle measurement. Particularly, the single vector hydrophone is used to measure the single-frequency signal emitted by AUVs, through which the relative angles among AUVs can be estimated. Based on this, a minimally angle rigid topology generation algorithm is designed to balance the tradeoff between communication energy efficiency and topology connectivity, while a model-free inverse reinforcement learning (IRL)-based formation controller is developed to steer AUVs to reach the target while maintaining a specific shape. The innovations are summarized as follows: 1) the angle measurement in this article can eliminate the reliance on the position information of AUVs; 2) the minimally angle rigid topology in this article can improve the formation stability and reduce the communication energy consumption as compared to the neighboring rule-based solutions; and 3) the IRL-based controller can avoid manually designing cost functions and improve environmental adaptability as compared to traditional-learning-based controllers. Finally, simulation and experimental results are both conducted to verify the effectiveness.

The topside separation system is an important device installed on offshore oil exploration platforms for the treatment of produced water. Due to its operation in high-moisture and salt-infested environments, the system is susceptible to valve malfunctions. Additionally, the presence of strong couplings and slugging disturbances in the system further complicate the development of fault-tolerant control (FTC). To achieve this, this article investigates the fault-tolerant$ H_{\infty } $control problem in the topside separation system. To recover control performance against actuator faults while reducing disturbance sensitivity, the fault-tolerant$ H_{\infty } $control problem is formulated for the topside separation system and is expressed as a two-player differential game problem. A Nash equilibrium solution to the fault-tolerant$ H_{\infty } $control problem is derived by solving the game algebraic Riccati equation (GARE). Considering the tailor-made property and difficulty in full-state sensing in industry, an output feedback reinforcement learning (RL) algorithm is proposed to implement the fault-tolerant$ H_{\infty } $control method without the need for system dynamics. Simulation studies are performed to verify the effectiveness of the proposed algorithm.

Flight control system testing (FCST) is one of the most important testing during aircraft final assembly, however, traditional testing method highly rely on manual labor, resulting in low testing quality and efficiency. Targeting at improving the testing quality and efficiency, in this paper, we apply digtial twin (DT) technology in the FCST process for the first time. A virtual experimental platform named DT-FCST is constructed which is identical to a real wing experimental platform, supporting testing elements management and physical-virtual mapping experiments. The development process of the DT-FCST platform is detailed. Several experiments are conducted to demonstrate the effectiveness of DT technology. Based on motion scripts integrating real-time data from the cockpit, the cockpit control sticks and flight control surfaces are driven to achieve consistency between physical and virtual motions. With the help of DT technology, the entire movement process of control sticks and flight control surfaces are mapped with high fidelity in the DT-FCST platform, greatly enhancing the testing efficiency and monitoring quality.

During the flight control system testing (FCST), it is crucial to accurately measure the deflection angles of flight control surfaces to determine whether they respond precisely to commands. However, the traditional measurement method, which relies on the manual use of angle measuring rulers for inspections, is inefficient, prone to wear, and lacks precision. To address this issue, we introduce a vision-based angle measurement method for FCST that replaces manual measurements, thereby significantly enhancing testing efficiency and accuracy. Our proposed triangular mesh alignment based angle measurement algorithm (TMA-AM) is a non-contact measurement method that involves two key procedures, capturing 3D coordinates from images and calculating deflection angles. The TMA-AM algorithm converts deflected angles into angular differences between two coordinate systems, while accounting for the curved characteristics of control surfaces. We evaluate TMA-AM algorithm on a 3D-printed wing test platform and an aircraft wing test platform. Experimental results demonstrate that our method achieves high accuracy, with an average angular measurement error below 0.05○.

Modern control systems are embracing multi-tier architectures integrating end devices and edge servers. However, due to the distinct control performance demands associated with each control task, it is a formidable challenge to optimize the control performance of multiple control tasks subject to stringent computation resource constraints while guaranteeing stability. Moreover, inherent contradictions exist in the timing aspect between the stability guarantee, which relies on offline analysis, and the run-time control performance, which should be enhanced online. It is essential to bridge the gap between the real-time scheduling of control tasks and their actual control performance. In this paper, we propose a novel real-time scheduling approach for multi-tier control systems, which leverages end devices for executing real-time control tasks and edge devices for runtime coordination. Specifically, we first introduce a new datadriven value function, called time/state/utility functions (TSUF), for modeling control system performance. TSUF captures not only timing but also the dynamic states of the physical plants. Subsequently, we propose value-based control scheduling (VCS), which is a multi-granularity scheduling mechanism based on our TSUF value function. VCS distinguishes the scheduling of stability jobs for ensuring system stability and performance jobs for optimizing real-time control performance based on run-time physical states. Finally, through realistic case studies involving multiple control loops, we demonstrate the advantages of VCS over existing scheduling approaches in terms of both control and real-time performance.

Industrial Internet of Things (IIoT)-based control is growing rapidly, such as smart factories and industrial automation. Sensing and transmitting physical state measurements is the first step and the prerequisite for IIoT-based control. However, sensor interference (e.g., electromagnetic interference on sensing, temperature, and humidity variations in the field) and network interference (e.g., metal obstacles and background noises) may destroy the control performance by interfering with sensing and communication processes. Most of the present upstream “fixed sensors-networking-state estimation” approaches cannot effectively deal with sensor and network interferences due to the fixed measurements/estimation and network resource limitations. To optimize the performance of IIoT-based control, we propose a smart sensing and communication co-design (SSCC) framework to select more potential sensors and establish the corresponding network scheduling. SSCC consists of a smart estimator (SE) and a sensing communication mode switching (SCMS) agent. The SE detects sensor interference and obtains resilient state estimation based on collaborative sensing. SCMS agent dynamically switches sensor selections and network configurations (routing and transmission number) in an integrated manner based on the network and plant states by solving a performance optimization problem. We propose a lightweight SCMS approach by searching a predefined mode table. We perform simulations integrating TOSSIM and MATLAB/Simulink, and semi-physical experiments on a real wireless sensor-actuator network composed of TelosB nodes. The results show that the SSCC framework can effectively improve the control performance and enhance network energy efficiency under various types of interference by dynamically selecting sensors and allocating network resources.

In recent years, underwater acoustic sensor networks (UASNs) have played an increasingly important role in ocean exploration. However, underwater sensor networks suffers from severe propagation attenuation, limited energy and sensor mobility compared with terrestrial networks. Also, the value of sensing data is quite different in some applications of underwater data collection. To tackle these challenges, this paper proposes a hierarchical collection strategy based on value of information (VoI). Taking account of the mobility of nodes close to sea level, we divide the network into two layers according to the Ekman drift current model. In the upper layer, the nodes move violently with the sea water. We adopt opportunistic routing to allow these nodes to search for the appropriate next hop nodes actively. Meanwhile, nodes in the lower layer are clustered. According to the rarity of the data received in the historical data, we propose a novel mathematical formula to measure the VoI of the data, and define the ratio of received data value to energy consumption as the evaluation index of network energy efficiency. AUV-aided transmission and multi-hop transmission are utilized separately to determine the tradeoff between energy consumption and network performance. The choice of transmission mode depends on the VoI in a cluster. Simulation results indicate that the proposed strategy shows satisfactory performance in improving energy efficiency.

During the flight control system testing (FCST) process, multiple testing tasks should be completed. Battery-powered wireless sensors are used to measure the motion state of each flight control surface. In this paper, we investigate a multi-task scheduling problem to enhance overall monitoring accuracy during the FCST process. However, the decline in sensor battery levels, along with limited time slot resources, impacts the transmission quality of measurement data, leading to reduction in monitoring accuracy. Thus, we analyze the relationship among battery levels, transmission power, and monitoring accuracy to transform the original problem into an expectation probability maximization problem. Three important factors of monitoring accuracy are identified, based on which, we present the accuracy-oriented testing task scheduling (AOTS) algorithm. To validate the effectiveness of AOTS algorithm, we compare its performance among three different scheduling orders. Simulation results demonstrate that AOTS algorithm can not only improve the testing accuracy, but also reduce the fluctuation in accuracy among all testing tasks. Additionally, there are various elements in the FCST process that need to be uniformly managed to enhance the level of digitization. To address this issue, we design a digital twin enabled FCST (DT-FCST) system to manage data, models and algorithms in the FCST process. Finally, we implement the AOTS algorithm into developed DT-FCST system.

This study focuses on an intelligent reflecting surface (IRS) enabled simultaneous wireless information and power transfer (SWIPT) system with the coexistence of legitimate users (LUs) and Eavesdroppers (Eves). The main objective is to jointly optimize the transmit beamforming and artificial noise covariance matrix at the access point, the phase shift matrix at the IRS, and the power splitting ratio at the LUs, to maximize the system’s min-secrecy rate. Due to the imperfect channel state information of Eves, an outage rate constraint is contained. The formulated problem is a challenging nonconvex optimization problem since it involves nonconvex objective function and constraints, and the outage rate constraint does not have simple closed form expression. To address this problem, an algorithm based on the alternating optimization method is proposed, which breaks down the nonconvex problem into three subproblems. The algorithm employs several techniques to solve these subproblems. Specifically, the outage rate constraint is approximated using the Bernstein-type inequality. And the Taylor formula, semi-definite relaxation, and successive convex approximation methods are employed to transform the nonconvex subproblems into convex ones. Simulation results demonstrate the effectiveness of the proposed algorithm compared to baseline algorithms under different conditions.

Increasing demands for Quality of Experience (QoE) lead to massive connectivity and intensive computation in future vehicular networks. This article proposes a device-to-device (D2D)-based mobile edge computing (MEC) network architecture to provide effective communication connections and sufficient computing abilities for vehicular networks. However, the available communication and computing resources are limited in the D2D-based vehicular MEC networks, and an imbalanced resource allocation always leads to suboptimal optimization of overall performances. To address this challenge, we formulate a Lyapunov optimization method-based resource allocation framework to balance communication and computing by compromising energy efficiency (EE) and time delay. However, the long-term resource allocation framework is ineffective when it ignores the dynamic characteristics of vehicular networks, i.e., channel state changes due to the movement of vehicles and a dynamic queue backlog with data fluctuations. Considering the time-varying channel state and dynamic queue backlog, the proposed framework aims to balance resource allocations while primarily maintaining network stability. Finally, we propose a Lyapunov optimization-based long-term dynamic resource allocation algorithm to develop real-time allocation strategies. Simulation results illustrate that the proposed algorithm balances communication and computing resources by tuning the control parameter V. Furthermore, the results confirm that the proposed algorithm outperforms baseline algorithms in real-time transmission and offloading ability.

Mobile-edge computing (MEC), pushing the centralized cloud computing, storage, and communication capability to the edge close to vehicular terminals, is proposed as a promising solution to support computation-intensive and delay-sensitive services. This article proposes a cooperative MEC-enabled task offloading framework where the computational task of each vehicle is divided and computed by multiple collaborative MECs located on the roadside. However, existing MEC-enabled offloading research is based on offline settings or static networks and fails to address the dynamic communication environments. These dynamic environments involve variations in temporality (real-time channel state) and spatiality (uncertain data-queue backlogs as vehicles pass through different coverage areas of MECs). In the dynamic vehicular networks, the degradation of utility energy efficiency (UEE) and time delay is inevitable and significantly impacted. To tackle this issue, we propose an online dynamic scheme to solve the problem of maximizing UEE while meeting time-delay constraints. We then introduce a novel online dynamic optimization algorithm based on Lyapunov optimization theory to adaptively create strategies for task offloading and communication resource allocation in parallel. Numerical simulations demonstrate that the proposed algorithm achieves a balance between UEE and delay, striking a flexible tradeoff by tuning the control parameter$V$. Furthermore, the results confirm that the proposed algorithm outperforms baseline algorithms in terms of real-time communication and transmission capability.

The booming development of 5G and Internet of Things (IoT) technologies significantly promotes the revolution of industrial IoT systems. Age of Information (AoI) is expected to play a critical role in industrial IoT systems, especially for time-sensitive monitoring and control applications. In addition, edge computing (EC) will be leveraged to effectively support industrial tasks in the limited communication and computing resources environment, bringing threefold benefits of shorter end-to-end delay, improving information timeliness, and reduced communication burden. Thus, we propose an edge-assisted co-design architecture of sampling-scheduling-control to improve the overall system performance. Under this architecture, a new definition, Age of Task (AoT), is proposed first to measure the timeliness of multielement and compute-intensive monitoring tasks in the industry. By analyzing the coupling relationship between AoT and control performance, an analytical expression of estimation error based on AoT is derived. Furthermore, we prove that the optimal control law could be expressed in a certain equivalent form, making it possible to decompose the design of control and network resource allocation (sampling, scheduling). According to the relation between AoT and estimation error, a co-design method, event-triggered sampling and max-age-reduce-first scheduling (ETMA), is proposed to minimize the system cost, including control cost and network energy consumption. The simulation results show that our co-design scheme has the optimal system cost among the four state-of-the-art schemes.

This letter investigates the cooperative localization of autonomous underwater vehicle (AUV) systems in underwater anchor-free environments. Different from previous works, the impacts of mutual asynchronization among AUVs and acoustic wave bends are collectively considered to enhance localization accuracy. Specifically, a novel relative measurement representation called modified range difference (MRD) is firstly designed to mitigate the measurement errors jointly caused by the above two factors, which can be obtained with every two different underwater acoustic communications and further corrected with sound speed profiles. Corresponding to MRD, an extended state is also designed to explore the use of historical position information. As a result, an improved cubature Kalman filter is proposed, which can tackle the nonlinearity of MRD and the commonly unknown measurement variance. Consequently, the proposed method can provide higher localization accuracy by mitigating the combined effects of asynchronization and acoustic wave bends without synchronization operations and anchor assistances, which delivers superior adaptability for anchor-free scenarios. Simulations verify that the proposed method outperforms state-of-the-art ones in terms of localization accuracy.

Along with the fourth industrial revolution, industrial automation systems are evolving into a multi-tier end-edge computing architecture. Edge controllers, which are equipped with a larger computing capacity compared to local controllers, can communicate with local plants over mainstream wireless networks such as WirelessHART, Wi-Fi, and cellular networks. Well-known challenges induced by networks, such as uncertain time delays and packet drops, have been intensively investigated from various perspectives: control synthesis, network design, or control and network co-design. The status quo is that the industry remains hesitant to close the loop between the edge controller and the actuation side due to safety concerns. This work offers an alternative perspective to address the safety concern, by exploiting the design freedom of an end-edge computing architecture. Specifically, we present a smart actuation framework, which deploys (1) an edge controller, which communicates with physical plant via wireless network, accounting for optimality, adaptation, and constraints by conducting computationally expensive operations; (2) a smart actuator, which is co-located with the physical plant on the end tier and executes a local control policy, accounting for system safety in the view of network imperfections, (3) the end-edge control co-design strategies and cooperation logic for both performance and stability. For certain classes of plants, semi-globally asymptotic stability of the resulting end-edge control systems is established when the edge controller is the model predictive control (MPC), or policy iteration-based learning control. We also provide an adaptation strategy for the end-edge control systems facing model parameter mismatches when the edge controller employs reinforcement learning. Extensive simulations demonstrate the advantages of the proposed end-edge co-design and cooperation procedures. Note to Practitioners—Edge computing is gaining momentum in areas that require low latency and high efficiency, i.e., mobile computing, video analytics, and autonomous driving. Industrial automation systems are also evolving into a multi-tier end-edge computing architecture. It pays obvious dividends to leverage the cooperation between end and edge, benefiting from fast and reliable communication on the end side, and powerful computation capacity on the edge side. The current end-edge cooperation focuses on how to partition tasks and offload computation resources in order to minimize delay and energy consumption, as well as how to balance the tradeoff between them. However, the impacts of end-edge cooperation on the safety, optimality, and cost of industrial automation have not been systematically studied. This paper aims to tailor end-edge cooperation in a smart actuation framework, for industrial automation to reconcile the above aspects by leveraging co-design of end and edge controllers and their switching logic. Extensive pure and semi-physical simulations demonstrate the advantages in performance and system stability of the proposed end-edge co-design and cooperation procedures.

Although wireless control is one of the key technologies for future industries, most wireless networks are only used for monitoring. When wireless networks are applied to transmit control commands, the uncertain link qualities and limited network resources may destroy the performance of multi-loop control systems. Hence, it is critical to allocate these resources to optimize the control performance as the network condition changes and plants evolve. This article presents comprehensive optimal scheduling strategies for wireless control systems based on adaptive dynamic programming. First, we propose an effective adaptive dynamic programming scheduling (ADPS) strategy to solve the optimal scheduling problem based on the single-step control performance at runtime while significantly reducing computational complexity. Moreover, to overcome the “short-sightedness” of single-step performance prediction, we extend ADPS to ADPS-m ( m ulti-step prediction), which optimizes multi-step performance by incorporating a longer-horizon evolution of the plants. Furthermore, we propose ADPS-H ( H eterogeneous flow scheduling) to support heterogeneous flows with different data rates and sizes and ADPS-H-m ( m ulti-step prediction for H eterogeneous flow scheduling), which schedules heterogeneous flows in a longer prediction horizon. We prove that all these scheduling strategies can achieve optimality and stability under mild assumptions. Extensive experiments integrating TOSSIM and MATLAB/Simulink are performed to evaluate all of the proposed methods in case studies of four- and ten-loop control systems. The simulation results demonstrate that these strategies can effectively improve the control performance at lower computing costs under both cyber and physical disturbances. Under the noise level of \(-\) 76 dBm, for the four-loop case, ADPS achieves the same control performance as the linear programming while saving 99.5% of the execution time. ADPS-m further improves the control performance by up to 27.0% compared with ADPS at the prediction horizon of 3, and ADPS-H-m improves the performance by up to 32.3% and 8.4% compared with round-robin and ADPS-H, respectively. The ten-loop case indicates the effectiveness and scalability of the proposed approaches.

The asynchronization among autonomous underwater vehicles (AUVs) is inevitable due to the inherent offsets and drifts in clocks, which critically degrades the accuracy of AUV cooperative localization (CL), especially in underwater anchor-free environments. This article designs a novel relative measurement representation called time difference of communication (TDOC) to eliminate the impact of asynchronization, which only requires one-way inter-AUV communications at different time steps even in the presence of large asynchronization. By exploiting TDOC, a joint spatial-temporal CL model is established with specially constructed measurement vectors, and a CL method, termed TDOC-CL, is proposed. It transforms the nonlinear observations into a weighted least-square (WLS) formation and is solved via iterative optimization, where various coupled uncertainties (i.e., measurement biases, measurement noises, and position errors) are involved and analyzed. Moreover, to compensate the accuracy degradation caused by the uncertainties, the TDOC-CL with bias reduction and error compensation, named iTDOC-CL-BREC, is further designed. In addition, the hybrid Cramér-Rao lower bound (HCRLB) is derived as the performance benchmark to evaluate the localization behaviors. Simulations and field experiments show that iTDOC-CL-BREC can provide higher localization accuracy compared with state-of-the-art methods, especially for long-duration tasks.

Network games primarily explore the intricacies of individual interactions and adaptive strategies within a network. Building upon this framework, the present study delves into the modeling, analysis, and control of heterogeneously networked evolutionary games with intergroup conflict (HNEG-IC), where attacking players possess area-monitoring capabilities with limited attacking power. To begin with, a mathematical model is introduced to capture intragroup strategy dynamics and intergroup conflicts of HNEGs-IC via the algebraic state space representation (ASSR). A necessary and sufficient condition for achieving global cooperation of HNEGs-IC is established. Then, a criterion for verifying the κ -cooperation below a certain mortality is presented. Considering the HNEGs-IC with strategy feedback control, it is proven that the feedback control, subject to global cooperation, is robust to conflicts when the intersection of the strategy threshold set and the reachable set of the preset initial strategy profiles is empty. Finally, for verification and demonstration, the obtained results are applied to a simplified virtual game model of the NATO and the Warsaw Pact.

This article investigates the control problem of bearing-based formation tracking for underactuated autonomous underwater vehicles (AUVs) considering actuator constraints and unknown disturbances. A leader-follower structure is adopted, where the leaders move with an unknown reference velocity. For the followers, an integrated strategy is proposed, which includes i) a bearing-based control method composed of a reference velocity estimator, a virtual velocity for achieving the desired formation, and an adaptive robust formation controller to track the virtual velocity under disturbances; and ii) a parameter tuning method based on control parameterization approaches and heuristic algorithms. By employing the cascade system theory, asymptotic convergence of errors in the overall system is proved in the presence of unknown disturbances. The tuning method optimizes controller gains to ensure, all while preserving the convergence properties of the closed-loop error system constraint feasibility and performance optimality. As a result, convergence, robustness, feasibility, and optimality are all achieved. Extension to the case where AUVs have sideslip motions in 3-D space is also discussed. Simulation results are presented to demonstrate the effectiveness of the proposed strategy.

The increasing scale of industrial production is leading to a greater demand for communication and computing capabilities, thereby increasing the likelihood of resource competition and conflict. This can cause stochastic overall task latency (including communication and computing latency), resulting in the occurrence of overdue tasks. To guarantee deterministic delays, the integration of edge computing (EC) with time-sensitive networking (TSN) emerges as a promising technology. However, the deterministic feature of TSN increases vulnerabilities to attacks within this integration. Particularly, uncertain denial-of-service (DoS) attacks can exhaust system resources and disrupt determinacy, causing prolonged delays, or communication failures. To this end, this article proposes an attack-tolerant TSN-based edge computing (TSN-EC) architecture to guarantee task determinacy. Based on the architecture, a task-level no-wait scheduling mechanism of TSN is proposed under packet switching mode, which ensures deterministic communication delays. A robust and deterministic task offloading scheduling (RDTOS) strategy is developed to minimize the number of overdue tasks by identifying the worst-case scenario of uncertain DoS attacks, considering each task's importance. To reduce computational complexity, a two-layer decomposition algorithm is proposed by further decomposing the master problem of the conventional C-CG algorithm. Experimental results conducted on a TSN-EC testbed demonstrate the superiority of the RDTOS strategy in enhancing security and providing overall task determinacy compared to related algorithms.

In the industrial Internet of Things, state estimation of large-scale physical systems is performed by multiple sensors in a distributed manner. However, frequent communications during an estimation interval can increase the energy consumption of sensors, causing thermal stress and reliability issues. Although system reliability can be improved by data compression, the compression-induced distortion may lead to the divergence of the estimation error. To address these challenges, a distributed estimation and communication co-design scheme is proposed in this article, which balances estimation performance and energy efficiency under the system lifetime reliability constraint. First, a consensus-based distributed estimation algorithm is proposed to adapt the data compression configuration. Then, the impact of system dynamics, network connectivity, and data compression configuration on estimation performance is investigated. Based on the relationship, the distributed estimation algorithm and the channel allocation with power control are jointly optimized to minimize the estimation error and energy cost under the system lifetime reliability constraint. This constrained minimization problem is formulated as a mixed-integer nonlinear programming problem and solved with the designed decomposition method. Finally, simulation results demonstrate that the proposed co-design scheme shows superiority in improving both the estimation accuracy and energy efficiency under the system lifetime reliability constraint.

This article presents a hybrid time-of-arrival (TOA) and angle-of-arrival (AOA) cooperative localization algorithm, termed HTACL, for the multiple-autonomous-underwater-vehicle (multi-AUV) navigation in the absence of anchors (devices with known positions). Compared with traditional TOA-based methods, the joint use of TOA and AOA measurements improves the quality of position prediction and inter-AUV relative position information in localization. Specifically, by exploring the relationship between AOA measurements and AUV attitudes, the impact of the accumulated errors in inertial measurements on the position prediction is alleviated. Moreover, the directivity and resolution of relative position representation is improved through involving AOA information. As a result, the localization accuracy can be improved by HTACL. The design of HTACL also considers the influence of the long propagation delay during AUV cooperation to enhance its adaptability to harsh underwater environments. Moreover, HTACL is naturally distributed and with good scalability. The abovementioned features make it applicable and suitable for multi-AUV localization issues. Through various simulations, the advantages of HTACL are verified and it could stably provide better localization accuracy compared with the state-of-the-art alternative methods under different conditions.

In intelligent factories, multiple industrial cameras capture continuous videos and upload them to edge nodes for automatic preinspection. For reliability, video chunks with low preinspection accuracy must be delivered to quality inspectors for manual reinspection. Video edge caching is an urgent technology for fast and efficient manual reinspection. However, time-varying and constrained industrial network conditions cannot meet the increasing demand for video-oriented quality reinspection. This article proposes a reinspection necessity-based video collaborative edge caching method, called Reine, utilizing video superresolution (VSR) for industrial quality reinspection. First, a video edge collaborative caching framework is established for industrial quality reinspection. Second, a reinspection necessity metric is designed, and a video edge caching strategy based on VSR benefit is proposed for edge nodes. Third, an NP-hard integer nonlinear programming problem is formulated for collaborative video caching and adaptive bitrate decisions. Simulation validates Reine outperforms the state-of-the-art video edge caching methods.

The harsh industrial environment and the high exposure of wireless communication networks (WCNs) seriously degrade the control performance of edge-enabled Industrial Internet of Things systems. Recently, the codesign of control and scheduling has been studied as a promising method to improve system performance. However, due to the dynamic feature of multiple unreliable factors, the impact of communication randomness on data timeliness, and the difficulty to gather sensing data, it is challenging to jointly design the schedule and control policy to mitigate the adverse effects of WCNs. To address these issues, this article presents a trust-age of information (AoI)-aware codesign scheme (TACS). We first propose a learning-based trust model with the aid of a conditional generative adversarial network to handle the sparse industrial data and a deep-neural-network-based trust online prediction to comprehensively measure the WCNs' reliability. Then, we study the impact of AoI on control performance and design the optimal controller based on the separation principle. Moreover, we derive a trust-AoI-aware scheduling policy at the edge side to dynamically select the optimal data to participate in plant control, which maximizes the control system performance and the trust of WCNs. Simulation results reveal the effectiveness of the TACS in terms of improving the system performance significantly.

Industrial Internet of Things (IIoT)-based control is growing rapidly, such as smart factories and industrial automation. In practice, imperfect wireless networks and time delay caused by delayed completion of computing tasks in IIoT may deteriorate the control performance. To enhance the performance of the control system, a multidomain resource allocation problem is formulated by co-designing control, communication, and computation resources, which is a mixed-integer nonlinear programming (MINLP) problem. In this article, a bilevel optimization framework is proposed to solve the MINLP, in which the sharing decision is derived in the upper level, and then, the optimal allocation of multidomain resources is derived in the lower level. A control-aware distributed bilevel (CADB) algorithm is developed, where these two levels interact with each other. In each round, the upper level optimization problem is updated based on the last resource allocation and solved by a primal-decomposition algorithm with provable finite-time feasibility. Then, according to the newly derived sharing decision, the lower level optimization problem is solved by the proposed mixed proximal-gradient-tracking algorithm. It is shown that CADB algorithm enables control systems to achieve enhanced control performance and energy consumption. Finally, simulations are conducted to verify the effectiveness of the proposed algorithm.

With the rapid development of Industrial Internet of Things (IIoT), time-sensitive networking (TSN) with deterministic and real-time features has gained broad interest. However, most existing research focuses on the network scheduling with fixed task placement and computing resource allocation, restricting the scheduling space of coupled task-network. To tackle this coupling problem, an efficient task-network scheduling (ETNS) scheme is proposed in this article for TSN. A task-conflict metric (TCM) is established to quantify the competition degree of scheduling resources. For increasing the overall scheduling space, a TCM-aware prescheduling method is proposed by optimizing task placement and routing paths to reduce the potential conflicts between tasks. Integrated with the prescheduling method, we design a TCM-aware parallel group-scheduling algorithm by reducing the conflicts between task groups to enhance schedulability and scalability. Experiments show that our ETNS scheme significantly improves the schedulability and scalability performances compared with the existing scheduling approaches. The larger the number of tasks, the higher the performance improvement.

Driven by the demand of Industry 4.0, the integration of 5G and time-sensitive networking (TSN) is proposed to provide ubiquitous connection and deterministic transmission. However, the heterogeneous access mechanisms and scheduling resolutions between 5G and TSN make it still intractable to schedule 5G and TSN resources jointly. To address this issue, we develop the network function virtualization-enabled 5G-TSN framework to offer unified resource management, where flows are scheduled by network slicing and virtual network function embedding, respectively. Specifically, a novel full-path age of information (FP-AoI) model is proposed as a new metric of the 5G-TSN integrated scheduling by innovatively encapsulating the 5G system as the sampling process of the virtual TSN network. To tackle the long latency tail brought by 5G, the 5G and TSN scheduling is formulated by a risk-aware FP-AoI minimization problem. Then, a decomposition and augmentation-based joint scheduling (DAS) algorithm is proposed to solve this NP-hard problem by decomposing it into three subproblems. The first two subproblems are proved to be convex. For the third subproblem, i.e., TSN scheduling, a FP-AoI-driven TSN scheduling scheme (FvQI) is designed by constructing an augmented logical topology according to constraints of service function chain and TSN characteristics. It realizes the TSN scheduling with low complexity. Simulation results demonstrate that our algorithms offer higher reliability, efficiency, and service acceptance ratio than benchmarks. Moreover, the DAS algorithm achieves a better tradeoff between the performance of time cost and AoI violation ratio with a small optimality gap.

Formation control of remotely operated vehicles (ROVs) has been regarded as the basis of many sophisticated marine missions. However, the high communication energy consumption and weak environment perception ability on ROVs make it challenging to achieve this task. To overcome the above challenge, this article develops a digital twin (DT)-driven formation control approach for ROVs. We first establish a virtual twin model for each ROV by extracting the motion parameters and environment information. With the collected states from ROVs, an integral reinforcement learning (IRL) based formation controller is designed to drive the motion outputs of DT model. After that, the optimal control policy from the DT model is employed to accomplish formation task for each ROV. To reduce the matching error and ensure the formation stability, an IRL-based optimization algorithm is conducted by using the data interaction between DT model and ROVs. Note that the DT-driven formation solution not only can reduce the communication energy consumption by periodically feeding back the real-data of ROVs to the DT model, but also can improve the perception ability of ROVs by reconstructing a virtual twin environment. Finally, experimental results are provided to verify the effectiveness of our solution.

In this paper, we investigate a mixed-traffic control problem considering uncertainties of HDVs flow. The challenges mainly lie in modeling the stochastic characteristics of mixed-traffic flow and developing less-conservative algorithm to deal with the uncertainties. To tackle the problem, we propose a stochastic model predictive control (MPC) strategy based on data-driven distributionally robust optimization (DRO). First, a stochastic mixed-traffic model, extended from cell transmission model, is proposed to describe the traffic dynamics. Then, utilizing historical traffic data, an incremental principal component analysis (IPCA) based method is given to construct ambiguity set and incorporate generalized moment information of uncertainties. Based on the above predictive model and ambiguity set, a DRO-based MPC problem is formulated and further converted into an equivalent dual form for efficient solutions, i.e., ramp metering and variable speed limit control. Finally, simulation results based on real data collected in Shanghai, China, demonstrate that our proposed strategy can significantly reduce traffic congestion, achieving 5.74 % total travel time reduction compared to robust MPC.

Cooperative adaptive cruise control (CACC) can optimize velocity planning by interaction between connected vehicles, and thus increase energy efficiency. It is much significant for platoon of electric vehicles (EVs) powered by hybrid energy storage system (HESS). HESS which is composed of battery and supercapacitor (SC), has been implemented to improve energy efficiency by regulating the distribution of internal energy sources. Different from most existing works on energy management for EVs, this paper is concerned with a bi-level control strategy for platoon’s velocity planning and HESS management. The upper layer focuses on the cooperative velocity planning for platoon to guarantee internal stability of individual vehicle and platoon robust string stability. The optimal velocities for each vehicle in the platoon could be determined by using distributed model predictive control (DMPC) method. The safety condition and the constraint on communication delay are considered. In the lower layer, a rolling horizon optimization method is proposed to optimize the power of HESS with the assistance of planned velocities in the upper layer. The simulation results indicate the effectiveness of the proposed strategy and methods.

Due to energy constraint and reliability consideration, autonomous underwater vehicles (AUVs) have fewer independent actuators than their degrees of freedom (DOFs). Additionally, the position and velocity of AUV are sometimes limited due to physical constraints. The current solutions, such as Barrier Lyapunov Function (BLF) and Nonlinear State Dependent Function (NSDF), depend on the upper bounds of virtual controllers and dynamic surface control (DSC) technique. This paper develops a new trajectory tracking controller for underactuated AUV systems with state constraints. A quasi-linear relationship is established between the independent and dependent variables of the transformation function. The Nussbaum functions are employed to address algebraic loop problem, which avoids using the DSC and any approximator. Moreover, an auxiliary controller is introduced to deal with underactuation problem. The Lyapunov theory proves that the proposed controller can guarantee asymptotic tracking of desired trajectory while keeping the position and velocity of the AUV within their constrained bounds. The method can be extended to the nth-order parametric-strict-feedback nonlinear systems. Finally, both simulation and experimental results reveal that tracking performance can be guaranteed by the proposed control scheme.

This paper presents dual-band Wi-Fi backscatter (DBscatter), which is the first system supporting 2.4GHz and 5GHz Wi-Fi backscatter simultaneously in a single tag. Our key insight is that most existing Wi-Fi devices communicate in the clean 5GHz band. The 5GHz band provides more chances for ”free riding” with less interference, while the 2.4GHz band presents better NLoS performance. DBscatter combines the strengths of the existing 2.4GHz band with the unexplored 5GHz backscatter in a unified design, developing a robust and high-throughput ambient Wi-Fi backscatter system. We make the following technical contributions: (1) We design a dual-band RF frontend to support dual-band Wi-Fi signals. (2) We propose a tag data demodulation algorithm, which merges the common phase error in multi-antenna received signals, improving the tag transmission reliability while reducing the number of required receivers. (3) We build a prototype of DBscatter system using COTS FPGAs and SDRs. Compared to TiScatter and FreeRider, DBscatter boosts Wi-Fi backscatter throughput by 3.74X and 7.35X, and energy efficiency by 1.78X and 1.38X respectively.

Since the last decade, deep neural networks have shown remarkable capability in learning representations. The recently proposed neural ordinary differential equations (NODEs) can be viewed as the continuous-time equivalence of residual neural networks. It has been shown that NODEs have a tremendous advantage over the conventional counterparts in terms of spatial complexity for modeling continuous-time processes. However, existing NODEs methods entail their final time to be specified in advance, precluding the models from choosing a desirable final time and limiting their expressive capabilities. In this article, we propose learnable final-time (LFT) NODEs to overcome this limitation. LFT rebuilds the NODEs learning process as a final-time-free optimal control problem and employs the calculus of variations to derive the learning algorithm of NODEs. In contrast to existing NODEs methods, the new approach empowers the NODEs models to choose their suitable final time, thus being more flexible in adjusting the model depth for given tasks. Additionally, we analyze the gradient estimation errors caused by numerical ordinary differential equations (ODEs) solvers and employ checkpoint-based methods to obtain accurate gradients. We demonstrate the effectiveness of the proposed method with experimental results on continuous normalizing flows (CNFs) and feedforward models.

Motion planning of underwater vehicles is regarded as a promising technique to make up the flexibility deficiency of underwater sensor networks (USNs). Nonetheless, the unique characteristics of underwater channel and environment make it challenging to achieve the above mission. This article is concerned with a communication-efficient and collision-free motion planning issue for underwater vehicles in fading channel and obstacle environment. We first develop a model-based integral reinforcement learning (IRL) estimator to predict the stochastic signal-to-noise ratio (SNR). With the estimated SNR, an integrated optimization problem for the codesign of communication efficiency and motion planning is constructed, in which the underwater vehicle dynamics, communication capacity, collision avoidance, and position control are all considered. In order to tackle this problem, a model-free IRL algorithm is designed to drive underwater vehicles to the desired position points while maximizing the communication capacity and avoiding the collision. It is worth mentioning that, the proposed motion planning solution in this article considers a realistic underwater communication channel, as well as a realistic dynamic model for underwater vehicles. Finally, simulation and experimental results are demonstrated to verify the effectiveness of the proposed approach.

Industrial Time-Sensitive Networking (TSN) provides deterministic mechanisms for real-time and reliable flow transmission. Increasing attention has been paid to efficient scheduling for time-sensitive flows with stringent requirements such as ultra-low latency and jitter. In TSN, the fine-grained traffic shaping protocol, cyclic queuing and forwarding (CQF), eliminates uncertain delay and frame loss via traffic timing in and out of queues. However, it inevitably causes high scheduling complexity. Moreover, complexity is quite sensitive to flow attributes and network scale. The problem stems in part from the lack of an attribute mining mechanism in existing frame-based scheduling. For time-critical industrial networks with large-scale complex flows, a so-called hyper-flow graph based scheduling scheme is proposed to improve the scheduling scalability in terms of schedulability, scheduling efficiency and latency & jitter. The hyper-flow graph is built by aggregating similar flow sets as hyper-flow nodes and designing a hierarchical scheduling framework. The flow attribute-sensitive scheduling information is embedded into the condensed maximal cliques, and reverse maps them precisely to congestion flow portions for re-scheduling. Its parallel scheduling reduces network scale induced complexity. Further, this scheme is designed in its entirety as a comprehensive scheduling algorithm GH2. It improves the three criteria of scalability along a Pareto front. Extensive simulation studies demonstrate its superiority. Notably, GH2 is verified its scheduling stability with a runtime of less than 100 ms for 1000 flows and near 1/190 of the SOTA FITS method for 3000 flows.

This paper for the first time demonstrates how to enable OFDMA in Wi-Fi backscatter for capacity and concurrency enhancement. With our design, the excitation signal is reflected, modulated and shifted to lie in the frequency band of the OFDM subcarrier by the tag; OFDMA is realized by coordinating tags to convey information to the receiver with orthogonal subcarriers concurrently through backscatter. The crux of the design is to achieve strict synchronization among communication components, which is more challenging than in regular OFDMA systems due to the more prominent hardware diversity and uncertainty for backscattering. We reveal how the subtle synchronization scenarios particularly for backscattering can incur system offsets, and present a series of novel designs for the excitation signal transmitter, tag, and receiver to address the issue. We build a prototype in 802.11g OFDM framework to validate our design. Experimental results show that our system can achieve 5.2-$16Mbps$aggregate throughput by allowing 48 tags to transmit concurrently, which is 1.45-$5\times $capacity and$48\times $concurrency compared with the existing design respectively. We also design an OFDMA tag IC, with the corresponding simulation and numerical analysis results show that the tag’s power consumption is in tens of$\mu W$.

The microservice architecture has gained widespread adoption in smart manufacturing, enabling the collaborative completion of production tasks through the integration of multiple microservices. However, migrating microservices in dynamic environments poses challenges for maintaining production quality and service efficiency. First, there are complex dependencies between microservices, such as layered and chain structures, making microservice migration a difficult process. Second, large-scale production scenarios require rapid decision-making based on high-dimensional variables to adapt to the dynamic environment. Third, microservice migration can cause interruptions, so careful selection of microservices is crucial to minimize production stagnation during migration. To tackle these challenges, we develop an efficient and economical migration strategy ($E^{2}MS$). This approach considers the complex dependencies between microservices and optimizes the system cost by selecting appropriate microservices for migration. We formulate an integer non-convex quadratic programming problem and employ techniques such as variable reduction, penalty functions, and successive convex approximation (SCA) to solve it. The proposed strategy enables efficient decision-making for microservice migration in dynamic production environments and exhibits strong scalability. Our experimental results demonstrate the exceptional dynamic performance of the proposed method, significantly reducing the time required to obtain migration strategies and achieving a 90% reduction in microservice interruptions compared to other methods.

Joint monitoring, by integrating observations from multiple types of equipment, is essential for a thorough understanding of physical processes in the Industrial Internet of Things (IIoT). However, it does demand sufficient resources to ensure reliable and timely delivery of such observations. Although network slicing is widely used to meet such heterogeneous requirements, it falls short in this system, because it causes interconnected impacts on system performance across multiple slices. In this paper, we introduce an innovative associated network slicing framework for joint monitoring, which focuses on system cost minimization while accounting for slice associations. Particularly, to better understand the characteristics, we introduce a new concept, Age of Inexact Task (AoIT), to capture inter-slice associations. We then decompose the optimization variables to facilitate efficient Associated Network Slicing (ANS) algorithmic design, leading to a closed-form solution for intra-slice small-timescale resource allocation and an iterative block coordinate gradient descent algorithm for inter-slice large-timescale resource allocation. Simulation results demonstrate that our proposed ANS balances heterogeneous requirements and associations, showing significant reductions in system costs compared to existing solutions.

The emerging Metaverse has challenging requirements for the reliability of extended reality (XR) data transmission. Configurable communication is a promising technology to improve the XR communication performance, where the intelligent reflecting surface (IRS) is representative of the ability to control transmission channels. However, because of the absence of incentives and untrust among IRS and Metaverse users, there is no easy way to establish the configuration resource scheduling for XR communication. Existing trusted third party-based methods face single-point/collusion attacks, inefficiency in arbitration, and low intelligence problems. To solve these problems, we propose a swarm learning (SL)-based secure configurable resource trading mechanism for reliable 6G-Metaverse XR communication. First, an SL-based configurable resource trading framework is established, which includes two designed subchains for decentralized IRS resource management and intelligent allocation. Second, a smart contract-enabled configurable resource trading scheme is designed, where decentralized trust is built among IRS devices, Metaverse users, and base stations. Third, we propose a decentralized federated learning (FL)-driven IRS allocation scheme, which consists of XR communication-related data collection, model training, and resource configuration. Finally, experimental results demonstrate the effectiveness of the proposed SL-based configurable resource trading for reliable XR communication.

Reconfigurable reflective arrays can be used to program the radio propagation environment in order to form favorable wireless channel conditions. Previous designs have used large-scale arrays containing hundreds to thousands of reflecting elements located external to the receiving node, with the reflection coefficients of all array elements managed by a controller. However, these designs can be costly to deploy and are challenging to quickly adapt to the time-varying nature of wireless channels caused by mobility.

The uncertainty of human-driven vehicles (HDVs) has a significant impact on the movement of connected and automated vehicles (CAVs), especially on the CAV platoon. In this regard, the future trajectory information of the preceding HDV is essential for CAV platoon to develop a safe and smooth speed control strategy. However, constrained by computation and energy resources, on-board unit can not provide accurate predictions, which results in inefficient control strategy and even safety issues. Hence, we propose an edge-assisted prediction and a flexible cooperative adaptive cruise control (CACC) strategy in this paper. The challenges mainly lie in the design of coordination mechanism and control strategy. Considering the inaccuracy of prediction, an error-aware communication trigger scheme is first designed to decide whether to request the roadside unit (RSU) for performing accurate prediction with the aid of mobile edge computing (MEC). Further, a novel flexible tracking policy is proposed to deal with prediction error, which is integrated with model predictive control method to determine control inputs. Simulations based on real data, collected in Shanghai, China, demonstrate that our proposed method can significantly improve driving safety and speed smoothness.

V2X communication is a key technology for intelligent transportation system to improve traffic safety and efficiency. Electric vehicles can reduce greenhouse gas emissions and fossil fuel dependence, but they face challenges such as limited driving range, high battery cost, and long charging time. This paper proposes a V2X communication assisted hierarchical cooperative control strategy for EVs that aims at improving driving performance and energy efficiency. The upper layer uses improved model predictive control (MPC) method for cooperative motion control. A mechanism is designed for V2X communication loss in the algorithm. The lower layer employs a hybrid energy storage system for powertrain management. The algorithm in the lower layer uses the predictive information from the upper layer to enhance the powertrain efficiency and prolong the battery life. The proposed strategy is simulated using a Prescan-Carsim simulation platform. The results demonstrate that the proposed method can improve the battery lifetime over 13% compared with baseline method.

Mobile-edge computing (MEC) is a key technology to enable multitasking and low-latency user experiences for 5G Internet of Things (IoT) devices. The nonorthogonal multiple access (NOMA) technology is used in this context to enable large-scale connectivity and improve spectrum efficiency, with the unmanned aerial vehicle (UAV) serving as both computing units and relays for mobile users (MUs). Energy efficiency (EE) remains challenging given the limited energy available to the UAV and MUs. In this article, a UAV-assisted NOMA–MEC communication network architecture is studied to maximize the EE of the total system by jointly optimizing the user’s communication scheduling, resource allocation, and UAV flight trajectory. Among them, the resource allocation problem can further be divided into the transmit power optimization problem and the task computation allocation problem, whereby the corresponding time slot scheduling is obtained. The objective function is a nonconvex mixed-integer nonlinear fractional programming (MINLFP) problem, which is too complex to solve directly. Therefore, it is decomposed into more manageable subproblems and solved iteratively. Fractional problems are solved using the Dinkelbach method, which transforms their original subproblems into convex forms with methods such as successive convex approximation (SCA). Simulation results demonstrate the convergence of our proposed algorithm and its significant advantage over existing strategies in terms of EE.

The resilient defense strategy for the false data injection attacks (FDIAs) from the perspective of structural vulnerability of smart grids, by using graph and cybernetic approaches, is investigated in this article. FDIA is a kind of well-designed cyberattack that can bypass traditional bad data detection methods and, thus, cause serious damage to smart grids. To defend against FDIAs, a resilient defense control strategy based on virtual hidden networks is proposed to improve the structural vulnerability of smart grids in this study. First, a reduced-dimensional local consensus dynamic model is derived by the Kron reduction method. Second, a virtual hidden network interconnected with the grid is constructed and designed by using the graph theory to indirectly improve the structural vulnerability of the smart grid. In addition, a competitive interconnection approach based on the network zero-sum game is used to further improve the structural vulnerability of the smart grid. The resilience of the smart grid can be effectively improved by changing the topology of the virtual hidden network and the interconnection bipartite graph. Third, the stability of the virtual hidden network-based defense controller is demonstrated separately under either cases without or with FDIAs. The results show that the addition of the virtual hidden network does not change the steady-state operating point of the original grid, but can effectively mitigate the impact of FDIAs on the smart grid. Finally, the effectiveness of the proposed approach is demonstrated on the IEEE 14-bus grid and on the New England 39-bus grid, respectively.

Edge sensing can achieve high-performance state estimation in industrial IoT systems by supporting task offloading and data processing at powerful edge estimators. Accurate edge sensing depends on low offloading delay. However, it is challenging to decrease offloading delay due to the harsh industrial environment and limited communication-and-computation resources. In this article, a closed-form expressing of estimation error with respect to offloading delay is derived to indicate that adjusting offload delay on demand is necessary for estimation error reduction. Then, we propose an adaptive edge sensing scheme, aiming to minimize estimation error by jointly optimizing task offloading and sensor scheduling. The required optimization is formulated as a mixed-integer nonlinear programming problem and solved by the designed decomposition and approximation methods. Specifically, the maximum matching is used for sensor scheduling to assign the optimal edge estimator for each sensor. The task offloading algorithm is designed based on the inner approximation method to reduce the offloading delay. Finally, simulation results demonstrate that the proposed scheme has superiorities in reducing estimation error compared with centralized sensing and distributed sensing schemes. Moreover, we find an interesting result that estimation error is delay sensitive when the offloading delay is large.

As industrial plants embrace modern technologies, such as edge computing and wireless networks, industrial control systems have evolved into multitier cyber–physical systems. While traditional local controllers enjoy reliable connectivity to sensors/actuators, they suffer from the limited computation capacity of embedded devices. In contrast, edge servers introduce more computation resources connected to sensors/actuators through wireless networks. Offloading control functions to edge servers presents new opportunities to enhance control performance but also poses critical challenges. As wireless networks have limited bandwidth and varying reliability, it is important to optimize control performance by dynamically offloading a subset of the control functions to edge servers under the bandwidth constraint. Furthermore, the selection of offloaded control functions depends on both the cyber (wireless) and physical states of the wireless control systems. In this article, we tackle the problem of optimizing the control performance of multiple control loops through dynamic edge offloading. We establish a data-driven model to predict the control performance of each feedback control loop based on its cyber–physical states. We then develop a dynamic edge offloading approach to optimize the overall control performance of a system with multiple feedback control loops while guaranteeing their stability under fluctuating cyber–physical conditions. Finally, we demonstrate the efficacy of the data-driven model and offloading approach in case studies comprising simulations of up to 20 industrial robots.

Aircraft final assembly line (AFAL) is a typical complex manufacturing system with multiple installation and test processes operating simultaneously at each workstation. Lots of robots and sensors are connected and operated for heterogeneous processes by sharing limited communication and computing resources. How to manage devices and resources in a coordinated and efficient way is thus very challenging. Digital twin (DT) is a powerful technology for multiple objects management in the complex assembly system. It enables us to coordinate various devices and allocate communication and computing resources at workstations. In this article, two main processes, i.e., vision-assisted installation and flight control system test, are considered in the AFAL. We introduce a DT-enabled AFAL system and propose a DT-assisted heterogeneous processes coordinated (DT-HPC) framework to coordinate various devices and resources at each workstation. The wirelessly connected robots and sensors are applied for perception and information fusion. In order to minimize the total energy consumption and computing resources of all the wireless devices, joint design of the wireless channel allocation, transmission power, and computing resource allocation are proposed to satisfy the diverse Quality-of-Service (QoS) requirements. First, we propose a priority-aware channel assignment (PACA) algorithm to allocate channels for sensors and robots. Then, the optimal computing resource allocation strategy for two processes is derived while guaranteeing the processing latency requirements. Next, we derive the minimum transmission power of wireless sensors to guarantee the monitoring accuracy and calculate the transmission power of robots to obtain the satisfied transmission rate. Finally, we apply the DT-HPC framework in the DT-enabled AFAL system. The simulation results prove that our proposed algorithms can save energy while guaranteeing different QoS requirements.

Underwater communications are fundamental techniques for the Internet of Underwater Things (IoUT) to establish information links among underwater devices. Recently, orthogonal chirp division multiplexing (OCDM) has drawn great attention in underwater communications due to its advantages in dealing with burst interference in both time and frequency domains. However, the harsh underwater channel conditions (e.g., multipath propagation, temporal variations, and significant Doppler effects) have not been systematically considered in current underwater OCDM systems. In this article, two transmission block structures (Structure A and Structure B) based on OCDM are designed, and a unified OCDM receiver framework is proposed under a single scale multipath lag (SSML) channel model. In the receiver framework, the Doppler scaling factor and carrier frequency offset (CFO) are sequentially compensated, and receiver algorithms that apply the separate or superimposed transmission features (for the pilot and data) of Structure A and Structure B are proposed, respectively. To be specific, a multipeak Doppler scaling factor estimation algorithm and a closed-form CFO estimator are designed for the received signal of Structure A. Moreover, a null symbol-based CFO estimation algorithm is presented for the received signals of Structure B. The effectiveness and advantages of the proposed methods are analyzed and validated through simulations and channel data by comparisons with existing methods under different conditions.

Localization is an indispensable service for underwater sensor networks (USNs). Generally, the convex optimization method is adopted to solve the localization problem. However, the acoustic ray in water medium does not propagate along a straight line, which makes it difficult or impossible to transform the nonconvex optimization problem into a convex optimization problem. This article develops a broad learning (BL)-based localization solution for USNs with isogradient sound speed profile. We first employ the ray tracing model to compensate the range bias caused by straight-line propagation. On the basis of collected range information from anchor nodes, the localization optimization problem is transformed into supervised, unsupervised, and semisupervised learning frameworks. Correspondingly, three BL-based location estimators are developed to seek the position information of sensor nodes, where the incremental learning schemes are conducted for fast parameter tuning and remodeling. In addition, the Cramer–Rao lower bound (CRLB) of positioning error and the convergence to global optimality are both analyzed. Finally, simulation and experiment results are presented to show the effectiveness of our approach. It is demonstrated that the proposed solution in this article has the following nice features: 1) relax the dependence of convex relaxation over convex optimization-based location estimators and 2) reduce the training time and improve the localization efficiency over deep-learning-based location estimators.

The new generation of edge computing supported industrial cyber–physical system (ICPS) promotes the deep integration of sensing and control. The unknown model is one of the key challenges to characterize their interactions. In most existing works, many efforts have been devoted to overcoming the challenge for the single aspect of sensing and control. However, the industrial revolution puts forward the higher requirements of the overall production performance. To solve this problem, we propose a novel framework for learning-based edge sensing and control co-design. Specifically, the model learning error is first analyzed to bound the actual control performance. Then, the bound is further linked to the sensing design through the bridge of relaxed assumptions of the nonzero initial state and unknown order. Besides, the cloud-edge symphony (CES) algorithm is designed for the co-design problem solving considering the defects of the single edge computing unit (ECU). In the novel framework, the processes of sensing, control, and learning are comprehensively considered for global optimization. Finally, the proposed algorithm is applied to the personalized production of laminar cooling based on the semiphysical evaluation, and the effectiveness is verified by the results. Note to Practitioners—Edge computing supported ICPS deeply integrates the sensing and control processes. It is beneficial to realize the small-batch customized production for the individual demands in intelligent manufacturing. However, the inevitable problem of weak prior knowledge of system models motivates us to adopt appropriate learning methods to deal with the model inaccuracy and characterize the internal relationship between sensing, control, and model learning. In this article, we propose a novel framework to comprehensively consider the performance of different aspects for global optimization. Specifically, the relaxed assumptions of the nonzero initial state and unknown order are regarded as the bridge to combine edge sensing and control. The cloud-edge symphony (CES) algorithm is proposed to solve the co-design problem and applied to the laminar cooling process for evaluation. It is observed that better overall performance is achieved than previous methods. In the future, our framework can be further extended from the single edge computing unit (ECU) and collaboration with the industrial cloud platform to coordinate sensing and control between the multiple ECUs. Besides, the production requirements of specific applications can be further considered including the real-time response and the reuse of production experience.

In this article, we consider the power scheduling problem of the multihop transmission with limited power resources. For a discrete-time linear time-invariant process, we consider a more practical scenario where the forward-error-correcting (FEC) coding scheme is utilized. An approximate communication model is introduced to formulate the nonanalytical relationship between the consumption of power and the successful-decoding-probability. For the single-hop transmission, we propose an analytical method to figure out the optimal offline scheduling for the finite-time case and the optimal periodic schedule for the infinite-time case. We consider the process and terminal errors simultaneously, and explicitly discuss how different values of parameters affect the optimality. Moreover, we extend our conclusions to the multihop case. In order to deal with the difficulty and complexity brought by the multihop scenario, a novel method based on the equivalent-scheduling matrix (ESM) is proposed to describe the accumulated effects through the multihop transmission. Meanwhile, explicit solutions of the multihop case are provided for finite- and infinite-time cases, respectively. Numerical examples are provided to demonstrate the effectiveness of the proposed methods.

This article studies the bearing-based formation tracking control problem of multiple double-integrator agents. A leader-following structure, where the leader moves with the reference dynamics, is adopted. Different from the existing methods, which require complete information of the time-varying reference velocity, in this article, only the time-varying reference orientation information is known by part of the followers and the amplitude of the reference velocity is unknown. To solve the problem, this article proposes a velocity-estimation-based control scheme, which consists of an estimator for estimating the varying rate of the reference orientation, an adaptation law for estimating the amplitude of the reference velocity, and bearing-based control inputs for tracking the leader and achieving the bearing-based formation based on the estimations. Moreover, the scaling formation maneuver can be achieved by using an auxiliary distance measurement. It shows that both the estimation errors and control errors converge to zero under the connectivity of the topology and properties of bearing rigidity. The closed-loop system is analyzed to be semiglobally uniformly asymptotically stable based on the cascaded system theory. Numerical simulations are presented to demonstrate the effectiveness of our method.

For improving the performance of industrial cyber-physical systems (ICPS), the joint design of control and transmission has been demonstrated as an efficient mechanism. However, the existing metrics in recent joint design works lack completeness and accuracy, which leads to the challenge of improving the stability and network resource utilization of ICPS. This article proposes a full-loop age of information (FL-AoI)-based control and transmission joint design architecture for multisubsystem ICPS integrating multihop network. The FL-AoI depicts the timeliness of information by state delay, input delay, and event-triggered status in full-loop ICPS. To avoid the instability caused by the input delay, we design a linear-quadratic regulator (LQR)-based controller by the FL-AoI and derive a feasible region of the FL-AoI for guaranteeing the stability of control systems. To ensure the accuracy of FL-AoI, we propose a routing and scheduling policy based on time-sensitive networking (TSN) for the deterministic bound of delay. Finally, we propose an optimal event-triggered policy based on FL-AoI for minimizing the data transmission amount while ensuring the system stability. The evaluation results show that our strategy improves the nonlinear and nonscalar control systems' stability while reducing the network burden of TSN compared with the traditional joint design strategies.

In this article, the resilient coordination problem of networked Lagrangian systems with adversarial agents is considered. A novel algorithm called norm-based resilient decision algorithm is proposed to exclude the impact of adversarial agents. To ensure the coordination of networked Lagrangian systems, the maximum number of adversarial agents related to the robustness of the communication network is given. Under the proposed resilient consensus algorithm, secure coordination is guaranteed under adversarial agents. Then, the proposed resilient controller is extended to static formation scenarios. Finally, the effectiveness of the proposed method is demonstrated through case studies and experiments. Compared to the existing results, the proposed algorithm can reduce computing resources by designing auxiliary vectors and converting them into scalars to remove extreme values.

Object detection is crucial for surveillance in edge-enabled Industrial Internet-of-Things. Massive high-dimensional video streams without considering priority differences connect to edges via narrow and time-varying uplink channels, which should be analyzed efficiently for accurate and fast surveillance responses. However, time-varying network environments and constrained edge resources degrade surveillance's accuracy and real-time performance. This article proposes EdgeLeague for multiple video streams with different quality of service, which maintains high surveillance performance under edge resource limitations and uplink bandwidth dynamics by edge collaboration and camera network configuration. The EdgeLeague scheme is formulated by an NP-hard integer nonlinear problem to dynamically configure camera network resolutions and detection models on cooperative edges. To accelerate configuration responses, the formulated problem is decomposed into edge league grouping, video-league matching, and video configuration, solved by low-complexity algorithms. Theoretical analysis is provided for optimal video-league matching. Simulations show EdgeLeague achieves 0.312 s latency and 86.3% surveillance accuracy.

360$^\circ$video becomes increasingly attractive in smart factories due to its immersive experience for industrial surveillance. However, transmitting this kind of video requires a fairly large demand on the bandwidth due to its high-resolution and panoramic view. Moreover, low-latency responses of the video to users' head movements are required. This leads to the tradeoff between high video quality and low-latency response under limited bandwidth in factories. This article proposes a perception-sensitivity (PS)-based 360$^\circ$tiled video streaming method called PSTile for industrial surveillance. Specifically, a PS tiling strategy is constructed for valid tile grouping based on the designed PS index. Then, a tile bitrate adaptation problem is formulated to allocate bitrates to valid tiles. It jointly optimizes surveillance accuracy, end-to-end latency, and quality-of-experience of users under accuracy, latency, and bandwidth constraints. Simulations demonstrate that PSTile achieves at least 32.6% lower end-to-end latency and 19.3% higher average video bitrate than grid-tiling and clus-tiling methods.

Many computation-intensive industrial applications need to be run in the cloud, which relies on a lot of sharply varying data transmitted from the industrial field. To save the communication bandwidth and ensure data with required accuracy obtained by the cloud, we design a data transmission architecture based on dual prediction scheme and cloud-edge collaboration and a dual-mode algorithm based on deep learning. With the proposed architecture, a deep learning model is deployed and synchronized on the edge and cloud sides. Further, the proposed algorithm can help the cloud for computation with locally predicted data or real-time data from the edge, depending on whether the predicted data are adequately accurate. A physical validation platform including a sensor, an edge gateway, and a cloud server is built, and drastically changing real vibration data are collected to validate the proposed scheme. The results show that the proposed scheme can reduce 88.66% of data transmission while guaranteeing deviations less than 0.1.

The industrial visual sensing-actuation system is an implementation approach to construct the loop between the digital twin and physical systems, which is facing the following challenges. First, the cross-digital-physical information exchanges bring a high end-to-end delay that threatens the functional safety of industrial systems. Second, industrial scenarios are diverse, such as manufacturing, chemical engineering, etc., which makes the intelligent sensing strategies for one scenario inapplicable to others, especially for few-shot cases. Third, intelligent actuation strategies cannot allocate resources across digital and physical domains. We propose the delay-minimization-based intelligent digital twin approach to address the above challenges. The digital twin framework incorporates samples from the physical domain to train the learning models in the digital domain. The proposed scheme tailors and adapts transferable and reinforced learning models with end-to-end delay analysis to optimize the training process. The feasibility and efficiency of the scheme are validated by simulations.

Communication-aware motion planning of autonomous underwater vehicle (AUV) is regarded as an emergent requirement for marine intelligent transportation system. However, the fading acoustic channel and the complex underwater environment make it difficult to realize such task. This paper is concerned with a communication-aware motion planning issue for AUV in obstacle-dense environment. We first develop an intelligent AUV system, which includes binocular cameras for short-distance obstacle avoidance, sonars for long-distance detection, and modems for acoustic communication with buoys. For such system, the parallax angles from AUV to obstacles are utilized to construct an optimal motion planning problem by integrating our previously proposed channel estimation approach. In order to solve the above problem, a deep learning method called depth deterministic policy gradient (DDPG) is developed to minimize the cost function, such that a collision-free path can be planed for AUV while maintaining the communication quality. Note that the advantages of our solution are highlighted as: 1) balance the communication quality and motion stability over the disk model-based methods; 2) improve the collision-avoidance efficiency in path lengths and control efforts as compared with the distance-based methods. Finally, simulation and experimental studies are both provided to verify the effectiveness of our method.

Communication efficiency and flocking stability are two basic requirements for the application of autonomous underwater vehicles (AUVs) in maritime transportation systems. Although they are closely related, most existing flocking approaches focus on the control techniques and ignore the influence of communication efficiency. This paper presents a joint design solution to the channel estimation and flocking control for multi-AUV-based maritime transportation systems, with the consideration of path loss, shadow and multipath fading channels. A value iteration-based reinforcement learning (RL) estimator is first designed to predict the channel quality of AUVs in positions that have not yet visited. With the predicted channel quality, we construct an integrated optimization problem for the co-design of communication and flocking strategies. Along with this, a value iteration-based RL flocking controller is developed to achieve the co-design of channel estimation and flocking control for AUVs. It is worth mentioning that, the value iteration-based RL estimator in this paper can avoid local optimal in traditional least-squares methods, and meanwhile the flocking controller in this paper can make a balance between flocking stability and communication efficiency for AUVs. Finally, simulation and experimental results reveal that the proposed approach in this paper has superior performances by comparing with the other works. As such, our approach is more useful for marine engineer to understand and explore the maritime transportation system from the communication and control view points.

The security issue of mobile robots has attracted considerable attention in recent years. In this article, we propose an intelligent physical attack to trap mobile robots into a preset position by learning the obstacle-avoidance mechanism from external observation. The salient novelty of our work lies in revealing the possibility that physical-based attacks with intelligent and advanced design can present real threats while without prior knowledge of the system dynamics or access to the internal system. This kind of attack cannot be handled by countermeasures in traditional cyberspace security. To practice, the cornerstone of the proposed attack is to actively explore the complex interaction characteristic of the victim robot with the environment and learn the obstacle-avoidance knowledge exhibited in the limited observations of its behaviors. Then, we propose shortest-path and hands-off attack algorithms to find efficient attack paths from the tremendous motion space, achieving the driving-to-trap goal with low costs in terms of path length and activity period, respectively. The convergence of the algorithms is proved and the attack performance bounds are further derived. Extensive simulations and real-life experiments illustrate the effectiveness of the proposed attack, beckoning future investigation for the new physical threats and defense on robotic systems.

With the rapid development of smart manufacturing, edge computing-oriented microservice platforms are emerging as an important part of production control. In the containerized deployment of microservices, layer sharing can reduce the huge bandwidth consumption caused by image pulling, and chain sharing can reduce communication overhead caused by communication between microservices. The two sharing methods use the characteristics of each microservice to share resources during deployment. However, due to the limited resources of edge servers, it is difficult to meet the optimization goals of the two methods at the same time. Therefore, it is of critical importance to realize the improvement of service response efficiency by balancing the two sharing methods. This paper studies the optimal microservice deployment strategy that can balance layer sharing and chain sharing of microservices. We build a problem that minimizes microservice image pull delay and communication overhead and transform the problem into a linearly constrained integer quadratic programming problem through model reconstruction. A deployment strategy is obtained through the successive convex approximation (SCA) method. Experimental results show that the proposed deployment strategy can balance the two resource sharing methods. When the two sharing methods are equally considered, the average image pull delay can be reduced to 65% of the baseline, and the average communication overhead can be reduced to 30% of the baseline.

This article is concerned with a containment control issue for autonomous underwater vehicles (AUVs), subject to unavailable velocity signals in cyber side and stochastic environment disturbances in physical side. We first divide the environmental disturbances into deterministic and stochastic parts. Based on this, a terminal sliding mode observer is developed to estimate the velocities of AUVs in finite time. With the estimated velocities, a distributed containment controller is designed for each AUV to follow a convex hull spanned by trajectories of the leader AUVs. For the developed velocity observer, a double power reaching law is employed to reduce the chattering and improve the convergence rate. Besides that, an adaptive strategy is incorporated into the containment controller, such that the steady-state errors caused by stochastic environment disturbances can be compensated. Stability conditions for the velocity observer and containment controller are also provided. Finally, we conduct the simulation and experimental studies to verify the effectiveness.

Fast and smooth driving is a preferable consideration in platooning algorithm development for intelligent autonomous vehicular systems. It can improve traffic efficiency while guaranteeing passenger comfort. To this end, this work investigates the fast distributed platooning problem of connected vehicular systems. Taking the inaccurate velocity measurement into consideration, an extended state observer (ESO) based on a fractional order faster nonsingular terminal sliding mode (FNTSM) is proposed for velocity and disturbance estimation simultaneously. An FNTSM control algorithm based on the double power reaching law is developed to reach fast platooning while guaranteeing string stability of the connected vehicular systems regardless of zero or nonzero initial spacing error conditions. The salient features of the proposed platoon controller are that system convergence can be achieved in finite time and the time-varying external disturbances can be estimated accurately. Finally, simulation and experiment studies are conducted to demonstrate the effectiveness and efficiency of the proposed control algorithm.

Driven by the coexisted heterogeneous communication protocols and diverse quality of service (QoS) requirements of industrial applications, time-sensitive networking (TSN) has been proposed as a promising and unified standard to provide deterministic transmission. However, the traffic scheduling of the TSN network should be orchestrated in a coordinated manner, which is challenging to respond to dynamic applications rapidly. For this, network function virtualization (NFV) has been introduced to eliminate the tight coupling between functions and devices, thus enabling a more flexible network resource allocation. However, NFV cannot be simply applied in a TSN network due to the inherent complex characteristics of TSN. To address this issue, we develop SvTI, a service function chain (SFC) driven queue injection scheme in the NFV-enabled TSN network. The basic idea of SvTI is to jointly model virtual network functions (VNFs) embedding and the TSN multi-queue characteristic by constructing an ordered augmented topology according to the SFC. Thus, the VNF embedding and TSN queue injection problem can be transformed into a shortest path routing problem. Besides, considering the QoS requirements of applications, a TSN queue-based topology division mechanism is proposed to simplify the augmented topology further and improve the algorithm efficiency. Simulation results show that our SvTI obtains a smaller graph size than other works and is more scalable.

Traffic optimization strategies are imperative for improving the performance of transportation networks. Most traffic optimization strategies only depend on traffic states of congested road segments, where congestion propagation is neglected. Therefore, we propose a distributed traffic optimization strategy for urban freeways considering the potential congested road segments caused by congestion propagation, calledpotential-homogeneous-area(PHA). Utilizing the historical traffic density data, we first quantify the effect of congestion propagation and identify PHA by applying the proposed spatiotemporal lambda-connectedness method. Meanwhile, dynamic capacity constraints of PHA are determined and are integrated with the cell transmission model (CTM) in a centralized traffic optimization problem. To reduce computational complexity and improve scalability, we then propose a double-consensus-based alternating direction method of multipliers algorithm (DC-ADMM) to handle the neighbor coupling constraints and global coupling constraints for solving the problem in a fully distributed way. We prove that the proposed DC-ADMM algorithm converges to the optimal solution in the condition of a convex objective function. Finally, simulations based on real traffic density data, collected in Inner Ring Road, Shanghai, China, reveal the effectiveness of our proposed strategy.

With the popularity of mobile devices and development of computationally intensive applications, researchers are focusing on offloading computation to the mobile-edge computing (MEC) server due to its high computational efficiency and low communication delay. As the computing resources of an MEC server are limited, vehicles in the urban area who have abundant idle resources should be fully utilized. However, offloading computing tasks to vehicles faces many challenging issues. In this article, we introduce a vehicular fog-edge computing paradigm and formulate it as a multistage Stackelberg game to deal with these issues. Specifically, vehicles are not obligated to share resources, and let alone disclose their private information (e.g., stay time and the amount of resources). Therefore, in the first stage, we design a contract-based incentive mechanism to motivate vehicles to contribute their idle resources. Next, due to the complicated interactions among vehicles, roadside unit (RSU), MEC server, and mobile device users, it is challenging to coordinate the resources of all parties and design a transaction mechanism to make all entities benefit. In the second and third stages, based on the Stackelberg game, we develop pricing strategies that maximize the utilities of all parties. The analytical forms of optimal strategies for each stage are given. Simulation results demonstrate the effectiveness of our proposed incentive mechanism, reveal the trends of energy consumption and offloading decisions of users with various parameters, and present the performance comparison between our framework and existing MEC offloading paradigm in vehicular networks.

With the increasing underwater applications, underwater wireless sensor networks (UWSNs) have become a research hotspot. Routing protocols used to keep network connectivity and reliable transmission are essential in UWSNs. Due to the specific limitations in UWSNs, such as serious ocean interference, high propagation latency, and dynamic network topology, it is challenging to balance multiple performances, such as real timeness and energy efficiency in a routing protocol. To this end, this article proposes a localization-free routing scheme, termed energy-efficient guiding-network-based routing (EEGNBR) protocol, to provide a time saving and reliable routing for UWSNs, which is a good choice for applications characterized by intermittent connectivity. For reducing the network delay, EEGNBR cites the advantageous distance-vector mechanism and establishes a guiding network to provide underwater sensor nodes with the shortest route (minimum hop counts) toward the sinks. Moreover, EEGNBR innovatively replaces the waiting mechanism used in traditional opportunistic routing with a novel data forwarding mechanism named concurrent working mechanism, which could greatly reduce the forwarding delay while guaranteeing reliable routing. In order to ensure routing reliability as well as avoid duplicate transmission, the forwarding protection mechanism is adopted to save energy consumption and extend the service life of the network. Simulation results show that EEGNBR performs significantly better than some classical related protocols in terms of network delay while maintaining comparable or even better energy consumption and packet delivery ratio.

The Internet of Underwater Things (IoUT) plays a key role in maritime monitoring systems, but energy-efficient data-uploading has been a challenging task owing to energy-constrained and expensive facilities, such as buoys and underwater sensors. In this article, we present an energy-efficient data collection scheme for unmanned aerial vehicle (UAV)-aided ocean monitoring networks (OMNs), where underwater acoustic and aerial radio frequency (RF) links are considered collaboratively. Our goal is to maximize energy efficiency (EE) of the entire OMN by jointly optimizing the transmit power of buoys and sensors, scheduling their transmissions, as well as designing the UAV's trajectory; the objective function is constrained by minimum throughput thresholds, power consumption budgets, and the UAV's kinematic conditions. Furthermore, we introduce a tradeoff between the energy consumption of buoys and sensors to bridge the gap between acoustic and RF links. The formulated problem is decomposed into three subproblems and they are solved alternatively. In each iteration, we leverage Dinkelbach's method and successive convex approximation (SCA) technique to tackle the fractional program (FP) and transform an original subproblem into a convex form, respectively. Extensive simulations confirm the convergence of our proposed scheme, reveal the influence of the tradeoff on EE, and show that our scheme outweighs other benchmarks in different scenarios.

The emerging hybrid backscatter and energy harvesting (EH) devices have been regarded as a promising scheme for green Internet of Things (IoT). In the interwoven primary and secondary wireless-powered IoT networks, we develop a novel hybrid scheme that integrates the backscatter communication and the harvest-then-transmit (HTT) protocol. In order to mitigate the adverse effect on the primary user (PU), the secondary users (SUs) are classified into “Cooperation Scenario” and “Coexistence Scenario” based on their different levels of interference to the PU. In the Cooperation Scenario, we propose a cooperation protocol where the SUs operate in the backscatter mode so as to relay information to the PU. Therefore, the SUs are rewarded for harvesting energy and obtaining the spectrum access time from the primary system. Also, in the Coexistence Scenario, a coexistence strategy is developed to enable the SUs to operate in either ambient backscatter or EH mode during the channel busy time. When the primary channel becomes idle, the SUs are capable of active transmission by using the harvested energy. For each scenario, we investigate the sum-throughput maximization problem of the secondary system. Employing the Lambert W function and the block coordinate descent method, the optimal time allocation can be obtained via the Lagrangian dual method. Numerical results validate that the proposed hybrid backscatter and HTT scheme improves the performance of the secondary system evidently compared with benchmark methods.

Multiobject tracking (MOT) is a crucial technology for security surveillance, which is computationally intensive due to the requirement of processing a large number of video streams within low latency in practice. The input video streams of MOT are processed on a cloud computing center with abundant computational capability, posing heavy pressures on delivering video streams to the cloud. Recent advances in the Internet-of-Things (IoT) technology provide edge-computing-based solutions for video analytics at scale. However, the gap between MOT’s high computational capability demand and IoT devices’ resource-constrained nature remains significant. In this article, a resource-efficient MOT (REMOT) method is proposed for real-time surveillance on IoT embedded devices, including an affinity measurement based on an appearance model with angular triplet loss and a motion association that substitutes the time-consuming graph-based data association stage. Considering the tradeoff between latency and accuracy, we design an optimization strategy on the parallel processing of deep learning models’ layers to accelerate the inference speed with less accuracy loss. Besides, we employ a model compression strategy for model size reduction. Experiments on MOT16 and MOT17 benchmarks demonstrate that REMOT reduces 2.4$\times $latency compared with the original implementation and achieves a running speed of 81 frames per second (fps) on an embedded device with only a marginal accuracy loss (6%), which meets the requirements of real-time processing and low-latency response for surveillance.

In this article, the predefined-time cooperative platoon control problem with constrained communication range for uncertain networked aerial vehicles is considered. The uncertain networked aerial vehicles are subjected to external disturbance and parameter uncertainties. A novel adaptive sliding-mode disturbance observer is first designed for single uncertain networked aerial vehicle with an radial basis function of neural network estimator to guarantee the control performance. By embedding a dynamic control gain associated with the predefined convergence time, the proposed disturbance observer is proved to be uniformly ultimate boundedness stable by the time transformation approach. Then the dynamic gain technology is combined with the prescribed performance control to design the platoon controllers for uncertain networked aerial vehicles. With the proposed disturbance observer and the distribute controller, the platoon can be achieved within the predefined time. The proposed approach can simultaneously guarantee the system stabile without any initial conditions and system parameters. Finally, some simulation experiments are given to verify the effectiveness of the proposed protocols.

The deterministic and real-time communication is the indispensable requirement in Industrial Internet of Things (IIoT) application areas. Time-sensitive networking (TSN) is a promising technology for this kind of communication demands through designing proper scheduling and routing mechanisms. However, it is still challenging to design the mechanisms for large-scale instances due to high computational complexity. In order to guarantee schedulability and scalability, a learning-based scalable scheduling and routing co-design (LSSR) architecture is proposed in this article for TSN. A stream partition method combining classification and graph-based clustering is established to reduce interpartition conflicts to enhance schedulability based on the explored domain knowledge and the characterized stream data set for practical requirements. Integrated with the stream partition method, we construct the constraints of scheduling and routing co-design to guarantee the deterministic and real-time transmission. An iterative scheduling algorithm is proposed to reduce the computational complexity and thus, to enhance scalability. Simulations demonstrate the effectiveness and advantages of the proposed LSSR scheme.

Task offloading in edge computing is important for the Industrial Internet of Things (IIoT) to implement computation-intensive applications in real time. However, achieving efficient task offloading in IIoT is very challenging due to the limited computing resources of IIoT devices, the coupling of computing and communication resources, and the unreliability in multihop wireless transmission. In this article, we construct a link model by considering the influence of unreliable links in multihop transmission to reveal the relationship between reliability and transmission delay. Then, a nonconvex optimization problem that minimizes task processing delay is formulated, and task offloading is decided by considering transmission path selection, bandwidth allocation, and computational resource allocation. To solve this problem, an algorithm based on the alternating direction method of multipliers (ADMM) is designed using auxiliary variables and reformulation linearization technology (RLT). The simulation results show that our proposed algorithm can fully utilize the computing power of the edge server and reduce the task processing delay. Compared with the centralized algorithms, the performance of the proposed scheme is only 1% worse, but the calculation time can be reduced by 40%.

Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded surfaces, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods could not locate low-contrast text areas very well. In this paper, we propose a refined feature-attentive network (RFN) to solve the inaccurate localization problem. Specifically, we first design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a high-quality attention map. Then, an attentive proposal refinement module is developed by the attention map to rectify the location deviation of candidate boxes. Besides, a re-scoring mechanism is designed to select text boxes with the best rectified location. To promote the research towards industrial scene text detection, we contribute two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts. Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance. Both code and dataset are available at:https://github.com/TongkunGuan/RFN.

Distributed algorithms are gaining increasing research interests in the area of power system optimization and dispatch. Existing distributed power dispatch algorithms (DPDAs) usually assume that suppliers/consumers bid truthfully. However, this article shows the need for DPDAs to consider strategic players and to take account of their behavior deviation from what the DPDAs expect. To address this, we propose a distributed strategy update algorithm (DSUA) on top of a DPDA. The DSUA considers strategic suppliers who optimize their bids in a DPDA, using only the information accessible from a DPDA, that is, price. The DSUA also considers the cases when suppliers update bids alternately or simultaneously. Under both cases, we show the closeness of supplier bids to the Nash equilibrium via game-theoretic analysis as well as simulation.

This article is concerned with the fixed-time prescribed tracking control problem for the uncertain stochastic nonlinear systems subject to input quantization and unknown measurement sensitivity. Different from existing results, the sensitivity on the sensor for measuring the system state is considered as an unknown parameter instead of the known one. Due to unknown measurement sensitivity on the sensor, the real system state cannot be obtained by measurement; hence, we put forward a new feedback control algorithm by the use of the unreal measured value of the system state. Moreover, the fixed-time prescribed performance on the output tracking error is investigated by developing a novel performance function. By means of the backstepping method, an adaptive quantized controller is designed for the system. Based on the Lyapunov stability theory, it is proved that the controller can render the output tracking error that satisfies the fixed-time prescribed performance and all signals of the resulting closed-loop system are bounded in probability. Finally, simulation results are provided to illustrate the effectiveness of the proposed control algorithm.

The new generation of the industrial cyber-physical system (ICPS) supported by the edge computing technology facilitates the deep integration of sensing and control. System observability is the key factor to characterize the internal relationship of them. In most existing works, the observability is regarded as the assumption for subsequent sensing and control. But, in fact, with the gradually expanded network scale, this assumption is more difficult to directly satisfy sensing design. For this problem, we propose the observability guaranteed method (OGM) for edge sensing and control co-design. Specifically, the nonconvex observability condition is transformed into the convex range of key parameters of the sensing strategy based on the graph signal processing (GSP) technology. Then, we establish the relationship between these parameters and control performance. In OGM, except the previous design from sensing to control, we reversely adjust the sensing design for control demands to satisfy observability. Finally, our algorithm is applied into the hot rolling laminar cooling process based on the semiphysical evaluation. The effectiveness is verified by the results.

This article considers the distributed output-feedback consensus control problem for nonlinear multiagent systems subject to input delays. Different from the existing related works, the input delay of each agent is described as an unknown time-varying function and is different from each other in this article. To deal with this problem, for each follower, we first construct a novel distributed observer based on the relative output information to asymptotically estimate the state information of the leader, then we introduce a classical observer to asymptotically estimate the state information of the follower based on its output information. By means of two observers, the leader-following consensus problem is transformed into the stability problem of the nonlinear system with unknown input delays. Subsequently, the distributed controller independent of delays is proposed for each follower by the use of the truncated prediction method under some conditions. Based on the Lyapunov stability theory, it is strictly proved that the distributed controller can render all agents achieving consensus. Finally, the effectiveness of the theoretical results is illustrated on the basis of numerical simulations on a group of single-link manipulators.

This article investigates the stabilization control and stabilizing data-rate condition problems for networked control systems, which transmit signals from the sensor to the controller over the communication network with denial-of-service (DoS) attacks. Considering a class of DoS attacks that only constrain its frequency and duration, we aim to explore the constraint condition for stabilization and minimum stabilizing data rate of the networked control systems. The framework consists of two main parts. The first part considers the stabilizing control by the state-feedback approach under ideal bandwidth capacity. While the second part characterizes the average stabilizing data rate in terms of the eigenvalues of system matrix and DoS constraint functions to explicitly reveal the relationship between the attacks and the network bandwidth capacity. The stabilizing result is novel in the sense that the DoS-attack intensity, which is characterized by its frequency and duration, can vary for different time intervals. With this feature, the minimum average data-rate condition can vary for different time intervals according to the intensity of DoS attacks.

In this article, we investigate the observer-based event-triggered consensus problem of multiagent systems with disturbances. A reset observer consisting of a linear observer and reset element is proposed, the reset element endows the reset observer the ability to improve transient estimation performance compared with traditional linear observers. A hybrid dynamic event-triggering mechanism (ETM) is proposed, in which an internal timer variable is introduced to enforce a lower bound for the triggering intervals such that Zeno-free triggering can be guaranteed even in the presence of disturbances. Then, in order to describe the closed-loop system with both flow dynamics and jump dynamics, a hybrid model is constructed, based on which the Lyapunov-based consensus analysis and dynamic ETM design results are presented. In contrast with linear observer-based consensus protocols and the existing dynamic ETMs, the system performance can be improved and continuous communication between neighboring agents is not needed. Finally, a simulation example is provided to show the effectiveness of the proposed methods.

With the advent of Industry 4.0 and the popularization of smart terminal equipment, the interaction between industrial field information systems and production equipment has intensified. To meet the real-time transmission of time-triggered flow and the coordinated transmission of best effort flow, time-sensitive network-related technologies are used to implement flow queue forwarding by strictly following the gate control list. First, response-time analysis method is proposed to predict the upper bound of delay under a scheduling model following IEEE 802.1Qbv. Second, according to response-time analysis, a deadline monotonic scheduling algorithm with temporary priority expansion is proposed to divide the priority into more levels, which is not limited by the queue type, so as to ameliorate the transmission sequence of switch export flow. Finally, an adaptive priority adjustment scheduling algorithm with temporary priority expansion is designed to construct the optimal scheduling method further improving the scheduling success rate and reducing worst-case end-to-end delays. Compared with similar algorithms, the algorithm proposed improves the scheduling success rate by at least 30%, reduces the total delay by at least 21% and the TT flow delay by 11% in high network utilization conditions.

As an emerging communication technology, time-sensitive networking (TSN) promises the real time and deterministic interaction of massive data in Industrial Internet of Things. However, it is challenging to schedule the time-sensitive flows timely and superiorly through the mechanism analysis for current TSN scheduling models, especially in complex industrial scenarios. In this article, we propose an analysis approach of flow sequences based on divisibility theory to characterize the flow conflicts and dependencies, which derives the scheduling flexibility based on flow position diversity (PD) and the equivalent flow judgment conditions for slot occupancy. Integrating the abovementioned derivation, a parallel computing framework with the generalized slot length is established to lower the scheduling complexity. Within each computing unit, an incremental scheduling algorithm with the flow judgment conditions and PD-based search boundary is proposed. It reduces the scheduling complexity further while maintaining load balance for the mixed transmission of periodic and aperiodic flows. To achieve the optimality of runtime and load balance, two PD-based flow sorting strategies are designed, respectively. The evaluation results show that compared with the existing works, the runtime efficiency of scheduling at scale is increased by at least 1500 times in complex traffic scenarios while the load balance on the network links is also improved.

In this article, a coupled sliding mode control (CSMC) is developed for vehicular systems with nonlinear uncertainties by using the disturbance observer (DO) and multi power reaching law. The DO is designed to estimate the nonlinear uncertainties. It is worth mentioning for the DO that the uncertainties include not only parameter uncertainty but also external disturbance, and the bounds of the uncertainties are not required to be known. In addition, the multi power reaching law is constructed to avoid the chattering problem of the traditional sliding mode control (SMC) and to improve the convergence speed effectively. Firstly, the constant time headway policy (CTHP) based on multi power reaching law and SMC is proposed to achieve the string stability for vehicle platoons. Compared with constant spacing policy (CSP), CTHP is more feasible in practice, because the desired spacing between adjacent vehicles is dependent on vehicle speed. Then, a modified constant time headway policy (MCTHP) is proposed for the vehicular systems to decrease the intervehicle spacing and increase the traffic density effectively. Finally, the numerical simulation and experiment are performed to demonstrate the effectiveness and advantage of the developed strategy.

The main method of modern ironmaking is blast furnace ironmaking, which is a very complex nonlinear dynamic process with complex physical-chemical coupling. The hot metal is the final product of blast furnace, and its silicon content not only reflects the quality of hot metal but also characterizes the operation status of the blast furnace, so its accurate prediction is very important for the operation of the blast furnace. Given the bottleneck problem in the application of the existing prediction model of hot metal silicon content in the blast furnace, this article proposed a novel data-driven modeling method. First, a nonlinear Takagi–Sugeno (T–S) fuzzy model is constructed for the hot metal silicon content to completely capture the nonlinear dynamics of the blast furnace process. Then, considering the doubts of blast furnace operators about the predicted results of the model, the Bayesian method is used to identify the consequent parameters of the fuzzy model to obtain the probability output, to present the credibility of the predicted results. Furthermore, to improve the robustness of the fuzzy model to the initial fuzzy rules, the sparse priori is adopted to construct a compact fuzzy model with strong generalization performance, in which the key fuzzy rules were screened out. In addition, two optimization methods are derived for each of the above models. Finally, the validity of the proposed methods is verified by the test of actual blast furnace data.

In this article, the scheduling problem over a delayed network is investigated. Different from the existing literature, we propose a hybrid stochastic model which incorporates both delay and packet loss. Based on this proposed model, an energy-efficient co-design problem is considered under power-limited scenarios. A global optimal offline solution is presented explicitly. Aided by arriving information feedback, we propose an online schedule based on an absolute threshold. The optimality of our proposed schemes and thresholds selection is theoretically analyzed. Meanwhile, we have extended the conclusions of the single-system case to the multisystem case. Optimal strategies from both offline and online perspectives are provided with rigorous proof, respectively. The stability condition is given for the estimator to converge in an infinite-time horizon. The comparisons between offline and online strategies are proved the global superiority of the online strategy to the offline one. Numerical simulations have validated the correctness and superiority of our proposed algorithms.

Human-on-the-loop (HOTL) system is regarded as a promising technology to allow autonomous underwater vehicle (AUV) to track the most adequate target point as soon as possible. However, the unique characteristics of the underwater environment make it challenging to perform the tracking task. This article is concerned with a finite-time tracking control issue for AUV, subjected to unavailable velocity signals in the measurement side and uncertain model parameters in physical side. A HOTL system, including operator, buoys, AUV and sensors, is first provided to construct a cooperative tracking network. For such system, operator in surface control center decides the tracking mission based on all available data. Then, a buoy-assisted localization estimator is utilized by AUV to acquire its position, through which a fast terminal sliding mode observer is developed to estimate the velocity of AUV in finite time. With the estimated velocity information, an adaptive-nonsingular fast terminal sliding mode tracking controller is designed to drive AUV to the target point in finite time. For the proposed velocity observer and tracking controller, the signum and differential functions are employed together to improve the convergence speed and reduce the chattering. Besides that, the proposed solution can not only guarantee finite-time velocity observation, but also achieve finite-time tracking control. Finally, simulation and experimental results are both presented to verify the effectiveness.

This article studies the joint localization and tracking issue for the autonomous underwater vehicle (AUV), with the constraints of asynchronous time clock in cyberchannels and model uncertainty in physical channels. More specifically, we develop a reinforcement learning (RL)-based asynchronous localization algorithm to localize the position of AUV, where the time clock of AUV is not required to be well synchronized with the real time. Based on the estimated position, a scalable sampling strategy called multivariate probabilistic collocation method with orthogonal fractional factorial design (M-PCM-OFFD) is employed to evaluate the time-varying uncertain model parameters of AUV. After that, an RL-based tracking controller is designed to drive AUV to the desired target point. Besides that, the performance analyses for the integration solution are also presented. Of note, the advantages of our solution are highlighted as: 1) the RL-based localization algorithm can avoid local optimal in traditional least-square methods; 2) the M-PCM-OFFD-based sampling strategy can address the model uncertainty and reduce the computational cost; and 3) the integration design of localization and tracking can reduce the communication energy consumption. Finally, simulation and experiment demonstrate that the proposed localization algorithm can effectively eliminate the impact of asynchronous clock, and more importantly, the integration of M-PCM-OFFD in the RL-based tracking controller can find accurate optimization solutions with limited computational costs.

Information freshness is essential to industrial wireless networks (IWNs) and can be quantified by the age-of-information (AoI) metric. This paper addresses an AoI-aware spectrum sharing (AgeS) problem in IWNs, where multiple device-to-device (D2D) links opportunistically access the spectrum to satisfy their AoI constraints while maximizing primal links’ throughput. Particularly, we orchestrate the access of D2D links in a distributed manner. Since distributed scheduling results in incomplete observation, D2D links share the spectrum with uncertainty on the transmission environment. Therefore, we propose a distributed scheduling scheme, called D-age, to deal with the transmission uncertainty in the AgeS problem, where an adaptation of actor-critic method is adopted with AoI constraints tackled in the dual domain. To address the non-stationary environment and multi-agent credit assignment issue, cooperative multi-agent reinforcement learning (MARL) approach is developed, where multiple local actors are designed to guide D2D links to make real-time decisions via distributed scheduling policies, which are evaluated by an edge-assisted global critic with action-aware advantage functions. Integrated with graph attention networks (GATs), the critic selectively learns contextual information by assigning different importances to neighboring links, which enables the evaluation of scheduling policies in a scalable and computation-efficient manner. Theoretical guarantee of the time-averaged AoI constraints is provided and the effectiveness of D-age in terms of both AoI violation ratio and the capacity of primal links is demonstrated by simulation.

Multivariate-information and computation-intensive tasks play an important role in Wireless Sensor Network Systems (WSNSs), where information freshness has an important impact on the system performance of state analysis. Recently, the Age of Information (AoI) has been studied extensively as a promising metric to evaluate the freshness of state packets. However, most of the existing research focuses on optimizing the average AoI of a single information source, which can not be directly applied to the scenario with multi-source tasks. In this paper, we firstly present a novel definition of the Age of Task (AoT) for edge-assisted industrial WSNSs. Furthermore, the expressions of the sensing time and arrival time that determine the AoT tail distribution are given in detail. Then, we propose an AoT tail violation probability minimization problem to find the optimal sampling rate and give the feasible region of the sampling rate. Since it is difficult to obtain the exacted expression of the formulated problem, the Upper Bound Minimization Problem (UBMP) and the more tractable α-relaxed UBMP are proposed to obtain the near-optimal sampling rate. Finally, simulation results show that the sampling rate obtained by α-relaxed UBMP is nearly optimal for the AoT tail violation probability minimization problem.

Driven by the advantages of the ubiquitous connection of 5G and the determinacy of Time-Sensitive Networking (TSN), the integration of 5G and TSN is expected to provide flexible and deterministic communications for the industry. However, due to the diverse quality of service (QoS) demands of industrial applications, it is challenging to provide suitable QoS mapping across industrial 5G-TSN networks and offer dynamic services via heterogeneous infrastructures. To address this issue, we design a QoS-aware dynamic data injection scheme to realize the interconnection between 5G and TSN under edge-assisted 5G-TSN architecture. To break the tight coupling between applications and infrastructures, we focus on the virtual network function (VNF) mapping problems to facilitate the QoS provisioning for different applications leveraging the network function virtualization (NFV) technique. We first formulate it as a mixed integer linear programming (MILP) with time-sensitive constraints. Then we develop PVMS, a preemption-based two-stage heuristic algorithm for VNF mapping and scheduling in the 5G-TSN network. In particular, we dynamically map the VNFs of arrived applications by greedily searching the earliest available 5G-TSN resources. To further meet the low-latency requirements, we employ a preemption mechanism to provide no-wait transmission for higher priority applications at the cost of the preempted applications being postponed. Simulation results demonstrate that the proposed PVMS has better performance in terms of the acceptance ratio, average delay, and QoS guarantee.

Various wireless manufacturing sensors facilitate the monitoring and control process in industrial internet of things (IIoT) systems. Control and image data generated by control and vision-based monitoring systems are transmitted over shared wireless network to edge computing devices for further decisions. Due to limited spectrum resources and sensor transmit power, it is challenging to design efficient quality of service (QoS) aware transmission mechanism for heterogeneous data with diverse real-time and system performance requirements. In this paper, in order to guarantee control system performance and image clarity, the time slot allocation and transmission power for heterogeneous sensors are jointly optimized by formulating a mixed integer nonlinear programming (MINLP) problem. The MINLP problem is decomposed into multiple subproblems which are proved to have strong duality and can be solved in dual domain. Then, according to acknowledge (ACK) signals, we propose a priority-aware heuristic algorithm to solve each subproblem. Finally, the simulation results prove that our proposed mechanism can reduce the number of slots and energy consumption for transmission while guaranteeing performance requirements of heterogeneous data.

This article considers both the influence of relay power and load control period on the load tracking performance in smart grid communication. First, based on regulation errors caused by load control with imperfect channel state information (CSI), the load tracking cost model integrated of load control period and relay power is established in smart grid communication. Then, a coordination mechanism of cost-sharing contract (CSC) is presented. In the CSC, the utility companies select the preferred contractual terms offered by the telecom operator (TO) to reduce their costs and coordinate the whole network system simultaneously. Finally, the theoretical analysis and simulation demonstrate that the simultaneous consideration of the relay power and load control period can reduce the costs of the utility companies, increase the profit of the TO, and improve the social welfare. Besides, the proposed coordination mechanism of CSC can coordinate the whole network system.

The cyber security of large-scale smart grid against false data injection attack (FDIA) is concerned in this article. FDIA can modify the sensor data and make internal states cause bias without being detected by the bad data detection system. We propose a method for FDIA detection and localization in the smart grid in this article. First, a series of interval observers are designed by considering the bounds of internal states, modeling errors, and disturbances to estimate the interval states of the grid physical system. By using the interval residuals of interval observers, a detection scheme against FDIA is proposed. For FDIA localization, the measurement data of the corresponding sensor is used as the input of the interval observer. Therefore, each interval observer is responsible for FDIA detection and localization of the corresponding sensor. Furthermore, the logic localization judgment matrix is constructed for localizing the sensor in which FDIA is injected. Then, the detection and localization scheme against FDIA is proposed based on the interval observer and the logic localization judgment matrix. Finally, simulations on the IEEE 36-bus grid are performed to illustrate the effectiveness of the proposed interval observer-based FDIA detection and localization algorithm.

This article studies the data collection problem in an Internet-of-Things (IoT) network with multiple unmanned aerial vehicles (UAVs) where UAVs first power multiple IoT devices by wireless power transfer, and then IoT devices utilize the harvested energy to transmit data to UAVs. Different from most of the existing works that often assume the channel between the UAV and the IoT device is a simplified Line-of-Sight (LoS) channel, a more practical and accurate probabilistic LoS channel model is adopted, in which both the elevation angle and the distance between the UAV and the IoT device determine the channel gain. Our objective is to maximize the UAV's minimum data collection rate among all IoT devices by jointly optimizing time allocation and 3-D trajectory of UAVs within a limited time duration. This results in a nonconvex optimization problem, which is challenge to solve. To tackle this difficulty, we transform the nonconvex problem to a difference of convex (D.C.) optimization problem by subtly using several methods. To solve the D.C. optimization problem, an efficient iterative algorithm is designed via a successive convex approximation method. Numerical simulation results are provided to verify the performance of the proposed algorithm compared to two benchmark algorithms, the algorithm with simplified LoS model and that with 2-D trajectory optimization, under various conditions.

In smart ocean, unmanned surface vehicles (USVs) are deployed to monitor the marine environment in a coordinated manner. The ubiquitous situation awareness of marine environment can be achieved by state estimation with the sensory data collected by USVs. Therefore, the transmission performance in terms of packet loss and delay of sensory data plays an important role in the state estimation of marine IoT systems. However, it is challenging to achieve the high-reliable and low-latency transmission for sensory data due to the path loss, spectrum scarcity and transmit power limitation. In this article, we introduce the Age of Information (AoI) to mathematically characterize the impacts of packet loss and transmission delay on the state estimation error. We first explore the relationship between the state estimation error and the AoI of sensory data. We then investigate the co-design of state estimation and sensory data transmission for marine IoT systems. Specifically, a mother ship (MS)-assisted cooperative transmission scheme is proposed to mitigate the impact of limited resources and path loss on the estimation performance. Then, the MS location, channel allocation, and transmit power are jointly optimized to minimize the mean-square error of state estimation, which is achieved by formulating a constrained minimization problem and solving it with the decomposition method. Simulation results demonstrate that the proposed scheme has superiorities in reducing the estimation error and the power consumption.

In industrial wireless sensor networks (IWSNs), a lot of energy is wasted in the form of electromagnetic radiations. It can be effectively utilized with energy harvesting (EH), which absorbs part of the energy in the transmission signal but reduces the throughput and reliability of IWSNs. In this article, we study the throughput optimization of IWSNs with EH from the interference radio-frequency (RF) signal considering the reliability constraint of the industrial information transmission. Under the premise of limited energy supply of EH relays, the throughput maximization of IWSNs is formulated as a nonconvex optimization problem. In order to transform the nonconvex problem to a convex optimization problem, the successive convex approximation (SCA) approach is adopted. Furthermore, a power allocation algorithm is designed to maximize the total transmission rate of the network. Simulation results demonstrate that the proposed algorithm can maximize the throughput under the primise of SINR reliability.

A mass of data generated by the widespread smart devices is transmitted through wireless communication networks for estimation and control in the Industrial-Internet-of-Things (IIoT) systems. The frequent data transmission needs to meet the high reliability and real-time demand of IIoT applications because the freshness of status updates influences the system performance. In this work, we use the Age of Information (AoI) to characterize the information freshness since it is a powerful metric to capture the randomness of state updates. Meanwhile, AoI is very useful in the control and communication co-design to improve the control performance considering communication disturbance. In order to analyze the control cost, we first derive the specific expression average AoI under the packet loss with finite retransmission times. Then, we investigate the influence of average AoI on control performance and obtain the joint cost combining communication energy consumption and control cost. According to the certainty equivalent principle, we design the optimal control law separately. Besides, we prove that the optimal joint infinite horizon cost is bounded by the linear function of average AoI. The communication policy, including the data interarrival rate and code length, is designed by optimizing the mixed-integer nonlinear programming (MINP) problem. Finally, simulation results reveal that, by employing the optimal data interarrival rate and code length, the joint control and communication cost is significantly reduced.

Privacy-preserving localization for Internet of Underwater Things (IoUT) plays a fundamental role in the sensing, communication and control of ocean environments. However, the unique characteristics of underwater environment make it much more difficult to achieve such a task. In this article, we are concerned with a privacy-preserving localization issue for IoUT, subjected to asynchronous clock, stratification effect and forging attack in cyber channels. In order to eliminate the influence of asynchronous clock and hide the private position information, we develop a privacy-preserving asynchronous transmission protocol, where a received signal strength (RSS)-based detection strategy is given to detect the malicious anchor nodes. Based on this, a least squares estimator is designed to estimate the position information of target. Particularly, a ray compensation strategy is incorporated into the localization estimator, such that the localization bias from assuming the straight-line transmission can be avoided. It is worth mentioning that, the proposed localization solution in this article can not only hide the private position information, but also eliminate the influences of asynchronous clock, stratification effect and forging attack. Finally, simulation and experiment results are conducted to reveal that the proposed localization solution outperforms the other existing works in terms of localization accuracy and effectiveness.

Tracking an autonomous underwater vehicle (AUV) has been regarded as one of the most key applications for Internet of Underwater Things (IoUT). However, the strong mobility of AUV as well as asynchronous clock, stratification effect, and high energy consumption of acoustic communication make it challenging to achieve such a task. To handle the above issues, this article develops a ubiquitous tracking scheme for AUV. The tracking scheme is divided into two stages, i.e.: 1) motion prediction and 2) persistent tracking. In the first stage, an unscented transform-based localization estimator is utilized by sensor nodes to acquire the initial position of AUV, through which a terminal sliding-mode velocity observer is designed to predict the mobility trajectory of AUV. With the predicted mobility trajectory, a minimum rigid-graph-based tracking strategy is developed in the second stage to enable ubiquitously tracking. For the designed tracking strategy, the posterior Cramer-Rao lower bound is selected as the benchmark to optimize the network topology, such that a minimum rigid graph can be generated to balance the tradeoff between tracking accuracy and energy consumption. Particularly, the duty-cycle mechanism and the unscented Kalman filtering are jointly adopted to prolong the network lifetime and improve the tracking accuracy. Finally, simulation and experimental results are presented to show the effectiveness of our approach.

Orthogonal chirp division multiplexing (OCDM) is a recently proposed multiplexing scheme and has drawn increasing attention in underwater communications. In an OCDM system, a series of orthogonal chirp signals carry the data information. However, the time variation feature of underwater acoustic (UWA) channels is rarely considered in the existing OCDM systems. In this paper, a particular transmission structure is proposed for the OCDM system to process the signal block-by-block at the receiver. Moreover, a low complexity channel estimation algorithm and the corresponding equalizers are derived. The proposed structure has a better ability to track time-varying channels. The simulation results show that the proposed method achieve promising performance under time-varying channels.

The dissipative stability problem for a class of Takagi-Sugeno (T-S) fuzzy systems with variable sampling control is the focus of this paper. The controller signals are assumed to transmit with a constant delay. Our aim is to design the sampled-data controller such that the T-S fuzzy system is globally asymptotically stable with a (Q,S,R) - γ -dissipative performance index. The stability is analyzed by using a novel piecewise Lyapunov-Krasovskii functional (LKF) together with a looped-functional and free-matrix-based (FMB) inequality method. First, several useful linear matrix inequality (LMI) conditions are derived to verify the dissipative stability of the T-S fuzzy system and then the controller gains matrices are expressed by resorting the LMI approach with the maximal-allowable upper bound (MAUB) of sampling periods. The proposed LMI conditions can be easily solved by using the MATLAB tool box. Finally, the numerical example of a truck-trailer system is considered and analyzed by the proposed scheme to illustrate the benefit and superiority.

This paper addresses the output feedback distributed containment control problem for a class of nonlinear stochastic multiagent systems under a fixed directed graph. Existing works usually design the containment control protocol using backstepping design method based on a conservative Lipschitz condition on nonlinear functions, which has a tedious control design procedure. In this paper, a new output feedback distributed containment control algorithm is proposed based on a novel dynamic compensator, which can not only simplify the control design procedure but also relax the condition on nonlinear terms. The proposed distributed containment protocol for each agent depends only on the agent output and the relative outputs of its neighbor agents, and can reduce the communication burden between the agents. Based on the Lyapunov stability theory, it is proved that the outputs of the followers are driven into the convex hull spanned by the outputs of the leaders with the proposed linear controller. Finally, the effectiveness of the theoretical results is illustrated by simulation examples.

This article studies the finite-time observer-based leader-following consensus problem for a class of nonlinear multiagent systems with nonuniform time-varying input delays. Existing works usually assume that input delays are the same constants and the input of the leader is available for each follower. In this article, we propose a new distributed consensus algorithm to relax the conservative condition. A novel finite-time distributed observer is designed for each follower, which can accurately estimate the state information of the leader in a setting time. By means of the observer, the distributed controller is proposed for each follower, and it depends only on the state information of the follower and the estimated-state information of its neighbor agents. Based on the Lyapunov stability theory, it is strictly proved that all agents can achieve a consensus. Finally, the effectiveness of the theoretical results is verified by numerical simulation on a group of single-link manipulators.

In the existing literature, reset control has been shown to have great potential to improve transient performance of linear systems, but reset-induced jump dynamics bring difficulties for stability analysis, especially under the network environment. In this article, we apply reset control to the consensus of multiagent systems (MASs) with event-triggered communication, and a novel reset mechanism (RM) and a hybrid event-triggering mechanism (ETM) are proposed with guaranteed Zeno-freeness. The state space is decomposed into multiple flow sets and jump sets according to the RM and ETM, and a hybrid model of the MASs is constructed such that the reset induced and event-trigger induced jump dynamics can be handled in a unified framework. Based on the hybrid model, a novel hybrid systems framework is presented for consensus analysis and co-design of RM and ETM. Finally, the proposed design is verified with a simulation example.

For complex and difficult-to-control blast furnace systems with hour-level delay, accurate prediction of molten iron quality plays a very important role in guaranteeing the stable and smooth operation. Recently, some data-driven multi-input multi-output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature, silicon content ([Si]), phosphorus content ([P]), and sulfur content ([S]). However, those data-driven MIMO models ignore the interindicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. Moreover, the above methods do not pay attention to the molten iron quality indicators missing issue, which often occurs on blast furnace. To address the above two issues, this article proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model by utilizing an output transfer matrix. In the novel method, the interindicator correlation was explicitly modeled by a low-rank learning of the correlation matrix that overcame the great challenge of jointly determining the fuzzy rules of the MIMO T-S model and the interindicator correlation. Moreover, a new complete complementary matrix can be obtained by the output transfer from the original incomplete matrix resulting from molten iron quality indicators missing issues. For the corresponding optimization problem, an effective alternating optimization algorithm is presented, and the convergence of the optimization algorithm is also rigorously proved. The validity of the proposed method is verified by comparison with some related methods on real blast furnace data.

This article investigates the dynamic event-triggeredH∞control problem for nonlinear networked control systems with unreliable communication channel, variable communication delays, and limited bandwidth. The nonlinear plant is represented by discrete-time polynomial fuzzy model. First, a decentralized dynamic event-triggered mechanism is proposed to determine whether the measured data are transmitted or not, and in order to exclude data collision caused by limited bandwidth, novel try-once-discard and flexible round-robin scheduling protocols are proposed to assign communication channel to certain sensor node. Then, Bernoulli distribution is employed to model the unreliable communication channel, and a new random sequence is developed to model the received data sequence under the effect of data losses and scheduling protocols. Furthermore, a discrete-time stochastic system model with both state and error delays is constructed, and sufficient conditions in the form of sum-of-squares are developed for the design ofH∞controllers such that the closed-loop system is stochastically stable and preserves guaranteedH∞performance. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed results.

Underwater sensor networks (USNs) are envisioned to enable a large variety of marine applications. Such applications require accurate position information of sensor nodes. However, the openness and inhomogeneity characteristics of underwater medium make it much more challenging to solve the localization issue. This paper is concerned with a privacy-preserving localization issue for USNs in inhomogeneous underwater medium. An honest-but-curious model is considered to develop a privacy-preserving localization protocol. Based on this, a localization problem is constructed for sensor nodes to minimize the sum of all measurement errors, where a ray compensation strategy is incorporated to remove the localization bias from assuming the straight-line transmission. To make the above problem tractable, we consider the unsupervised, supervised and semisupervised scenarios, through which deep reinforcement learning (DRL) based localization estimators are utilized to estimate the positions of sensor nodes. It is noted that, the proposed localization solution in this paper can hide the private position information of USNs, and more importantly, it is robust to local optimum for nonconvex and nonsmooth localization problem in inhomogeneous underwater medium. Finally, simulation studies are given to show the position privacy can be preserved, while the localization accuracy can be enhanced as compared with the other existing works.

Industrial wireless networks (IWNs) have attracted significant attention for providing time-critical delivery services, which can benefit from device-to-device (D2D) communication for low transmission delay. In this article, a distributed scheduling problem is investigated for D2D-enabled IWNs, where D2D links have various age-of-information (AoI) constraints for information freshness. This problem is formulated as a constrained optimization problem to optimize D2D packet delivery over limited spectrum resources, which is intractable since D2D users have no prior knowledge of the operating environment. To tackle this problem, in this article, an AoI-aware scheduling scheme is proposed based on primal-dual optimization and actor--critic reinforcement learning. In specific, multiple local actors for D2D devices learn AoI-aware scheduling policies to make on-site decisions with their stochastic AoI constraints addressed in the dual domain. An edge-based critic estimates the performance of all actors' decision-making policies from a global view, which can effectively address the nonstationary environment caused by concurrent learning of multiple local actors. Theoretical analysis on the convergence of learning is provided and simulation results demonstrate the effectiveness of the proposed scheme.

Edge computing is emerged as a promising solution to cope with huge volumes of data generated by smart devices and low latency demand for mission-critical applications in industrial cyber-physical systems. Data processing and estimation are shifted to the edge computing side. Nevertheless, the WCN between field devices and edge computing side is exposed to malicious attackers because of its openness. Therefore, in this article, we focus on the transmission path selection strategy design to guarantee the secure state estimation on the edge side against dynamic denial-of-service attacks. First, we present a novel learning-based secure routing algorithm (LSRA) to learn the attack rule and predict the attacker's next conduct with the use of both historical and online data. With lower computational complexity, the proposed learning algorithm could track the attack rule in real time whenever new data comes. Meanwhile, we derive the analytical relationship between the probability upper bound of learning error and the learning time. Based on the predicted attacker's behavior obtained by the learning algorithm, we flexibly select the secure routing path to avoid being attacked and, thus, improve successful transmission probability. Furthermore, this secure routing path selection method improves the performance of the state estimation system. The theoretical analysis of estimator stability is given. Finally, simulation results reveal the effectiveness of LSRA and the path selection scheme.

In this article, we study the tracking problem for networked nonlinear discrete systems with actuator faults and dual-direction data dropouts. A novel adaptive fault-tolerant iterative learning model-free control strategy is designed. First, by utilizing the method called compact form dynamic linearization, the original nonlinear system model is transformed into an equivalent data-driven model, and the data model contains only one unknown parameter. Both the actuator fault and the system dynamics information are included in this parameter. Then, to model the physical processes of data dropout, a new mathematical relationship is constructed. Furthermore, an adaptive fault-tolerant iterative learning tracking control scheme is developed with only randomly received input/output data. Noting that the high learning rate or convergence rate is required in actual applications, a new varying parameter approach is designed to improve such rate. Finally, it is rigorously proved that the closed loop is stable in the sense of uniform ultimate boundedness, and numerical simulation results are conducted to validate the effectiveness of the designed control strategy.

In this paper, we address the consensus tracking problem for the multiagent system (MAS) based on a nonfragile memory sampled-data controller. Considering the effect of controller gain fluctuation and communication delay, a novel sampled-data control scheme with variable sampling interval is designed for each agent. By developing some new terms, an improved piecewise Lyapunov-Krasovskii functional (LKF) is constructed to take full advantage of characteristic about real sampling pattern. Furthermore, some relaxed matrices constructed in the LKF are not necessarily positive definite. Making full use of the LKF and free-matrix-based integral inequality, some sufficient criteria are developed to ensure the consistency of the MAS. Then, by solving a group of linear matrix inequalities with the maximal sampling interval, the desired sampled-data control gain matrix is obtained. Finally, the numerical example of a 5-agent system is given to illustrate the effectiveness of the proposed approach in this paper.

A new event-triggered iterative learning control method is proposed for handling the distributed containment control problem of model-free multiagent systems under a fixed directed graph. The designed controller merely uses the input and output signals, controlled model information is not required. At first, the unknown dynamic is transformed into the linearization model upon the base of pseudo partial derivative. Secondly, the novel distributed containment controller is proposed for each follower by use of iterative learning algorithm. Moreover, a new trigger mechanism is designed to save energy of the systems, such that the updating number of the proposed controller can be reduced greatly. Mathematical deduction shows that the controller can render the outputs of the followers converge to a convex hull formed by the outputs of leaders. Finally, simulation examples are given for verifying the significance of proposed method.

Most studies so far on trajectory tracking control of autonomous underwater vehicle (AUV) have assumed that the Euler angles are exactly known. However, the AUV inevitably suffers from external environmental disturbances which are driven by wind, density, and temperature gradients. The attitude transducers cannot derive accurate attitude information of the AUV. Additionally, the uncertain hydrodynamic parameters affect the stability of the system. Consequently, it is unknown whether tracking performance of the AUV can be guaranteed. In order to overcome these drawbacks, in this paper, a finite-time controller is developed by using the nonsingular fast terminal sliding mode control technique. A robust differentiator is proposed to estimate the external disturbances and uncertain parts. Simulations are performed to show that with the proposed control laws, the AUV converges to the desired trajectory even in the presence of external disturbances and system uncertainty.

This article investigates the consensus problem of multiagent systems (MASs) with the reset control approach. Reset control has been shown to possess great potential to improve transient system performance, but reset-induced state jump dynamics bring difficulties for consensus analysis, especially under the network environment. In this article, a novel reset element is developed, and time regularization is utilized to exclude Zeno behavior. The case with continuous communication is first considered, a distributed proportional integral + reset consensus protocol is proposed, which consists of the proportional-integral controller and the reset mechanism. The results are further generalized to the case with asynchronous sampling, which relaxes the requirement for continuous communication and clock synchronization. A hybrid model of the MAS is constructed to describe the system dynamics, and a hybrid systems approach is proposed to handle the reset- and sampling-induced jump dynamics in a unified framework. Moreover, a novel Lyapunov function is proposed and LMI-based stability conditions are given. Finally, an example is provided to show the effectiveness of the proposed methods.

In recent years, cervical cancer has been one of the most common diseases in women's cancer. The advanced diagnosis of cervical precancerous lesions is essential for preventing cervical cancer. Its effectiveness and efficiency can be greatly improved by computer aided diagnosis, while challenged by the imprecise conclusions and uninterpretable process of diagnosis. To solve this problem, we propose a novel deep learning-based interpretable diagnosis system for pathology images, consisting of three interrelated models: an image model, an attention model and a conclusion model. Computer aided diagnosis improves the effectiveness and efficiency of the proposed image model uses a convolutional neural network (CNN) to ex-tract semantic features. Combining the model with the semantic attribute attention model, it aims to capture the discriminant relationship between se-mantic attributes by predicting the conclusion label through long-term and short-term memory (LSTM). The network is trained in an end-to-end manner, with different weights for each model. Experimental results on cervical intraepithelial neoplasia images, diagnostic reports and label datasets show that the proposed method achieves a significant improvement over traditional methods with a better interpretability.

In smart ocean, multiple unmanned surface vehicles (USVs) are deployed, which generally perform multiple monitoring missions with different requirements of transmission performance. For the monitoring mission, the transmission latency is quite important for marine IoT systems to achieve the ubiquitous situation awareness. However, it is quite challenging due to the location-depended path loss and battery-powered sensors. To address this issue, this paper adopts the Age of Information (AoI) to mathematically express the impact of transmission delay on state estimation, and proposes a mothership assisted cooperative transmission scheme to enhance the estimation performance with limited energy. Moreover, the locations of mother-ships is optimized to minimize the mean squared error of state estimation, which is achieved by formulating a constrained minimization problem and solving it with the decomposition method. Simulation results demonstrate that the proposed scheme could achieve smaller the estimation error.

Due to the ever-increasing time-sensitive industrial applications, critical-machine type communication (C-MTC) is a promising technique for timely delivery services in industrial wireless networks (IWNs), where vicinal devices can benefit from device-to-device (D2D) communication for low power consumption and latency. For real-time applications, Age of Information (AoI) is an essential metric that represents the freshness of data from the perspective of destinations. Thus, an AoI orchestration agent for link scheduling in D2D-enabled IWNs is needed. Most existing works on AoI deal with this scheduling in a centralized manner, which cannot afford timely packet delivery requirements for numerous D2D devices. Different from the existing works, a learning-based autonomous AoI and power orchestration agent, namely, L-AoI, is proposed for D2D-enabled IWNs in this article, where D2D devices adaptively compete for wireless resources in a distributed manner. As a result, the global channel state information as well as the actions of other D2D devices are unknown. Hence, D2D devices deal with this uncertainty under the guidance of L-AoI so that AoI constraints can be respected. By leveraging from the belief-based Bayesian reinforcement learning, L-AoI learns the scheduling action profile with strategies of other D2D devices considered so that the spectrum sharing coalitions can be intelligently formed. Both theoretical analysis and simulation are provided to validate the performance of L-AoI in terms of AoI stability and violation ratio.

The noncooperative target tracking is an important issue for the Internet of Underwater Things (IoUT). Autonomous underwater vehicles (AUVs) are preferred options to achieve the target tracking especially in anchor-free environments, where no equipments with known positions, named anchors, are deployed. The self-organized mobile network of multiple AUVs can localize and continuously monitor the target. Thus, in this article, we investigate the problem of the noncooperative target tracking using multiple AUVs in anchor-free environments. In the target tracking, AUVs play as references and their positions need to be estimated first. We propose a multi-AUV cooperative localization and target tracking (MCLTT) framework based on belief propagation (BP). Under MCLTT, BP-based underwater cooperative localization (BPUCL) and noncooperative mobile target tracking (NcMTT) algorithms are designed. Gaussian approximations are used to reduce communication costs among AUVs. The designed BPUCL alleviates the impact of the accumulated errors in the inertial measurements of AUVs and slows down the growth of the localization error. In NcMTT, model-free position prediction processes are proposed and a novel form of the particle-based BP message is designed using time-difference-of-arrival (TDOA) measurements. The simulation results validate the proposed algorithms by comparing with state-of-the-art methods.

In this article, we study an interference management and power allocation problem when electrical power communication (EPC) networks are densely deployed in the coverage of licensed networks. The purpose is to reduce the electricity cost and improve the licensed operator's profit subject to the quality-of-service (QoS) of licensed users (LUs). First, the electricity cost is modeled based on the Taguchi loss function, which links the cost to the communication errors in the EPC networks. The operator's profit is formulated by introducing a bonus-penalty mechanism, and a rational interference threshold (IT) of the licensed base station (LBS) is set to ensure the QoS of the LU. Second, we formulate the interference management and power allocation problem as a Stackelberg game, and a successive convex approximation algorithm is used to solve this problem to achieve the optimal IT and relay power. The simulation results indicate that the cost to the utility company is reduced and the profit of the LBS increases.

This article investigates the detection and isolation of false data injection (FDI) attacks in a smart grid based on the unknown input (UI) interval observer. Recent studies have shown that the FDI attacks can bypass the traditional bad data detection methods by using the vulnerability of state estimation. For this reason, the emergency of FDI attacks brings enormous risk to the security of smart grid. To solve this crucial problem, an UI interval observer-based detection and the isolation scheme against FDI attacks are proposed. We first design the UI interval observers to obtain interval state estimation accurately, based on the constructed physical dynamics grid model. Through the capabilities of the designed UI interval observers, the accurate interval estimation state can be decoupled from unknown disturbances. Based on the characteristics of the interval residuals, a UI interval observer-based global detection algorithm was proposed. Particularly, the interval residual-based detection criteria can address the limitation of the precomputed threshold in traditional bad data detection methods. On this basis, we further consider the detection and isolation of FDI attacks under structure vulnerability. Namely, there exist undetectable FDI attacks in the grid system. Taking the attack undetectability problem into account, a logic judgment matrix-based local detection and isolation algorithm against FDI attacks are developed. Based on the combinations of observable sensor cases, local control centers can further detect and isolate the attack set under structure vulnerability. Finally, the effectiveness of the developed detection and isolation algorithms against FDI attacks is demonstrated on the IEEE 8-bus and IEEE 118-bus smart grid system, respectively.

Localization is a critical issue for many location-based applications in the Internet of Underwater Things (IoUT). Nevertheless, the asynchronous time clock, stratification effect, and mobility properties of the underwater environment make it much more challenging to solve the localization issue. This article is concerned with an autonomous underwater vehicle (AUV)-aided localization issue for IoUT. We first provide a hybrid network architecture that includes surface buoys, AUVs, and active and passive sensor nodes. On the basis of this architecture, an asynchronous localization protocol is designed, through which the localization problem is provided to minimize the sum of all measurement errors. In order to make this problem tractable, a reinforcement-learning (RL)-based localization algorithm is developed to estimate the locations of AUVs, and active and passive sensor nodes, where an online value iteration procedure is performed to seek the optimization locations. It is worth mentioning that the proposed localization algorithm adopts two neural networks to approximate the increment policy and value function, and more importantly, it is much preferable for the nonsmooth and nonconvex underwater localization problem due to its insensitivity to the local optimal. Performance analyses for the RL-based localization algorithm are also provided. Finally, simulation and experimental results reveal that the localization performance in this article can be significantly improved as compared with the other works.

Target tracking has been considered as one of the most important applications of underwater acoustic sensor networks. However, the long propagation delay, high-energy consumption, and strong noise properties of the underwater environment make target tracking more challenging as compared with terrestrial sensor networks. This article is concerned with an energy-efficient tracking issue for underwater targets, subject to an asynchronous clock, power restriction, and noise measurement constraints. The tracking process can be divided into two phases, i.e., position acquisition and persistent tracking. In the first phase, we establish the relationship between propagation delay and position, through which an asynchronous localization algorithm is developed for sensor nodes to estimate the position of target. Based on the estimated position, a consensus-based Bayesian filter is designed for sensor nodes in the second phase to enable persistent tracking. In particular, the consensus fusion strategy and duty-cycle mechanism are jointly adopted to improve the tracking accuracy and prolong the network lifetime. Moreover, the convergence analyses for the proposed approach are also presented. Finally, simulation and experimental results reveal that the proposed tracking approach can reduce the influence of malicious measurements, while the energy efficiency can be significantly improved as compared with the other works.

In this paper, the distributed output feedback leader-following control is investigated for high-order nonlinear multiagent systems (MASs) using the dynamic gain method. The linear-like distributed output feedback controller is designed without using the recursive method to overcome the "explosion of complexity" problem and further relax the conditions on the nonlinear functions of the MASs. First, the distributed reduced order dynamic gain observer is constructed for the i th agent to estimate its unmeasured state variables, in which the output information of its neighbors are used. Second, the linear-like output feedback controller is designed such that the outputs of the followers track the leader's output, and the tracking error could be arbitrarily small. Finally, the simulation examples are given to illustrate the effectiveness of the proposed method.

This paper studies the problem of decentralized adaptive output feedback fault detection and control for a class of uncertain nonlinear interconnected systems. The K-filters are designed to estimate the unmeasured state variables of the system. Moreover, the built-in noise dampening filters are introduced to attenuate the influence caused by the measurement noises. Then the fault detection scheme is proposed by designing the residual and threshold signals. Subsequently, by using the backstepping design method, the decentralized switched control strategies are proposed with the help of the neural network approximation technique. Based on the Lyapunov stability theory, it is proved strictly that all signals of the resulting closed-loop system are bounded. Finally, a simulation example is presented to verify the effectiveness of the theoretical result.

This paper investigates the leader-following consensus problem for multiagent systems (MASs) with communication delays. A novel hybrid event-triggered control scheme is developed and a hybrid system approach is proposed to design the event-triggering condition. Meanwhile, by means of temporal regularization, a strictly positive lower bound on the interevent times can be guaranteed, that is, Zeno-freeness can be guaranteed. The MASs are first described as a closed-loop system with both flow dynamics and jump dynamics, where the jump dynamics is induced from the triggering events and communication delays. Then, a hybrid model of the MASs is constructed under a hybrid systems framework. Based on this hybrid model, how to construct the Lyapunov function is given and the event-triggering condition design is also developed, such that the asymptotic consensus is achieved. Finally, an example is provided to show the effectiveness of the proposed approach.

This paper investigates the sampled-data stabilization problem for a Takagi-Sugeno (T-S) fuzzy system with time delay. By taking the information of states within the intervals from tkto t and t to tk+1into account, a new two-side delay-dependent looped-functional is introduced which can not only relax the monotonic constraint of Lyapunov-Krasovskii functional (LKF), but also make better use of the actual sampling pattern. Furthermore the sampled-data fuzzy controller is designed to contain both the present and delayed state information, thereby enhancing the control performance and design flexibility. Based on the novel augmented LKF and improved bounding technique, less conservative stability criteria are derived in the form of linear matrix inequalities. The superiority of proposed results is shown by two simulation examples.

Sensing data and control commands are frequently exchanged over communication networks for remote data acquisition and distributed control in industrial network control systems. The system performance relies on the design of sensing, transmission, and control. Due to the harsh environment in industrial field and the limited network resources, it is very challenging to meet the high requirement on transmission reliability for remote feedback control. In order to enhance the transmission ability for this kind of systems, an edge-assisted system architecture is proposed for the sensing and control processes. The parameter estimation for the sensing process is executed in the so-called edge estimator, and the controller is designed in a remote control center. Under this architecture, in this article an on-demand transmission scheme is designed by characterizing the overall effects of transmission reliability on the estimation and control performance. The overall system is optimized by formulating a revenue-cost maximization problem subject to the constraints of system stability, estimation convergence, spectrum utilization, and energy budget. The formulated mixed-integer nonlinear programming problem can be effectively solved with the block coordinate descent method. It is decomposed into two subproblems over disjoint variable sets. Simulation results demonstrate the advantages on both network-wide revenue and control-transmission cost.

Blockchain-based femtocell networks aim to build decentralized frameworks which enable easy deployment and low power consumption, thus they have been seen promising technologies to make up the coverage of cellular networks in the next generation communication system. This article aims to employ power control to support quality-of-service provisioning, especially the guarantee for the transmission rate of a macrocell user (MUE) and the time delay of femtocell users (FUEs) in two-tier femtocell networks, where the MUE and FUEs share the same communication channel. We formulate the interactions among the macrocell base station and FUEs as a Stackelberg game to maximize the utilities of MUE and FUEs by obtaining the optimal power allocation and pricing strategy. Considering the uncertainty of channel gain which is expressed as a function of transmission distance, we propose a worst-case method to transform the uncertain optimization problem into a deterministic one. We then design two algorithms by considering the dynamics of FUEs, i.e., FUEs may join and leave femtocells. Numerical results verify the convergence and superior performance of our proposed algorithms.

Owning to the recorded light ray distributions, light field contains much richer information and provides possibilities of some enlightening applications, and it has becoming more and more popular. To facilitate the relevant applications, many light field processing techniques have been proposed recently. These operations also bring the loss of visual quality, and thus there is need of a light field quality metric to quantify the visual quality loss. To reduce the processing complexity and resource consumption, light fields are generally sparsely sampled, compressed, and finally reconstructed and displayed to the users. We consider the distortions introduced in this typical light field processing chain, and propose a full-reference light field quality metric. Specifically, we measure the light field quality from three aspects: global spatial quality based on view structure matching, local spatial quality based on near-edge mean square error, and angular quality based on multi-view quality analysis. These three aspects have captured the most common distortions introduced in light field processing, including global distortions like blur and blocking, local geometric distortions like ghosting and stretching, and angular distortions like flickering and sampling. Experimental results show that the proposed method can estimate light field quality accurately, and it outperforms the state-of-the-art quality metrics which may be effective for light field.

Audio information has been bypassed by most of current visual attention prediction studies. However, sound could have influence on visual attention and such influence has been widely investigated and proofed by many psychological studies. In this paper, we propose a novel multi-modal saliency (MMS) model for videos containing scenes with high audio-visual correspondence. In such scenes, humans tend to be attracted by the sound sources and it is also possible to localize the sound sources via cross-modal analysis. Specifically, we first detect the spatial and temporal saliency maps from the visual modality by using a novel free energy principle. Then we propose to detect the audio saliency map from both audio and visual modalities by localizing the moving-sounding objects using cross-modal kernel canonical correlation analysis, which is first of its kind in the literature. Finally we propose a new two-stage adaptive audiovisual saliency fusion method to integrate the spatial, temporal and audio saliency maps to our audio-visual saliency map. The proposed MMS model has captured the influence of audio, which is not considered in the latest deep learning based saliency models. To take advantages of both deep saliency modeling and audio-visual saliency modeling, we propose to combine deep saliency models and the MMS model via a later fusion, and we find that an average of 5% performance gain is obtained. Experimental results on audio-visual attention databases show that the introduced models incorporating audio cues have significant superiority over state-of-the-art image and video saliency models which utilize a single visual modality.

Hybrid electric storage system (HESS) is a promising power supply for electric vehicles (EVs) to prolong battery cycling life. Battery longevity is affected by the magnitude and fluctuation of the charging/discharging power profiles. While vehicle driving, unexpected high power demand can cause battery degradation and should be supplied by the supercapacitor (SC) in the HESS. However, the limited capacity of SCs restricts the HESS benefit. Thus, one of the crucial while challenging issues for an HESS is how to effectively manage the power splitting between batteries and SCs to satisfy the vehicle's driving demand as well as reducing battery degradation rate. In this paper, a proactive power management scheme is proposed to extend the EVs battery life with the HESS. First, we exploit a time-series forecasting method to predict the short-term vehicle velocity and calculate the future power demand based on prediction results. Next, due to the nonlinear dynamics of the HESS, the T-S fuzzy modeling method is adopted to approximate system nonlinearity and develop an empirical model. Finally, a model predictive control (MPC) based power management problem is formulated. Prediction errors are considered in MPC formulation to improve system robustness. Based on driving profile tests, simulation results demonstrate that the magnitude and fluctuation of battery current are both reduced and the battery life is prolonged by 17.81% compared with the existing methods.

Cyber-physical systems (CPSs) are becoming common in the industrial automation domain. In industrial CPS, distributed programmable logic controllers collaborate to control manufacturing plants. Design and analysis of such systems require a system model that covers control, computation, and communication with physical plant dynamics. This paper focuses on execution semantics for industrial CPS with the aim of providing a deterministic and platform-independent execution environment. A discrete-event-based execution semantics augmented with timestamp mechanism is proposed for IEC 61499 to provide deterministic behavior and guarantee compliance with real-time constraints for industrial CPS. The timestamped discrete-event-based execution semantics is implemented in an IEC 61499 runtime with service-enabled features. A case study of building automation system is used to prove the proposed semantics.

This paper investigates the problem of the distributed adaptive output feedback leader-following consensus control for a class of high-order nonlinear multiagent systems with unknown parameters and nonlinear terms. First, the reduced order dynamic gain k-filters are built to estimate the unmeasured state variables. The bounds of the unknown parameters are estimated in order to avoid the over-estimation problem. Then, the dynamic surface control technique is used to design the distributed controller, and the computation load of the system is greatly reduced. Finally, a numerical example is shown to verify the effectiveness of the designed controller.

When dealing with complex nonlinear signals in intelligent system, by defining the inner product of two data vectors in the feature space, the kernel function can reflect the nonlinear mapping relationship between reproducing kernel Hilbert space (RKHS) and original data space. Therefore, by implementing classical linear adaptive filtering in RKHS space, the filtering operation can be expressed as a special relation of inner product with kernel function, which is referred to as “kernel trick.” As long as these algorithms can be expressed in the form of inner product, not only the convex least squares problem can be solved iteratively, but also the nonlinear adaptive filtering algorithms can be obtained, which have both general approximation characteristics and convexity. Therefore, the combination of kernel method and adaptive filtering algorithm is realized. On the other hand, since the Gram matrix is used, the dimension of kernel adaptive algorithm is determined by the number of data samples. When the number of observation sample point is increasing, the size of the state space increases exponentially with the growth of the dimension. Thereby, the kernel adaptive algorithm should solve the problem of online sparsification to avoid “curse of dimensionality.” As part II, based on online sparse kernel learning and the classical adaptive filtering algorithm, the kernel adaptive algorithms and online sparse algorithms are investigated in this paper. The main works of this paper have two aspects. First, combined with classical adaptive algorithm and kernel feature mapping, this paper investigates the basic concept of kernel adaptive algorithm and the realization mechanism of four kernel adaptive algorithm intensively: 1) kernel least mean squares; 2) kernel recursive least squares; 3) kernel affine projection algorithm; and 4) kernel principal component analysis. Second, in order to reduce the computational complexity, this paper studies some online sparse algorithms which include novel criterion, approximate linear dependency, sliding window, coherence criterion, surprise criterion, and so on. Finally, this paper summarizes the essence of existing conclusions of above algorithms and perspectives the future research direction.

To promote the revolution of Industrial Internet of Things, the next generation communication system is expected to provide latency critical services in industry. However, for the traditional downlink-centric cellular systems, the timely delivery of packets cannot be guaranteed by the default dynamic access scheme due to complex signaling procedure. A promising solution to low-latency access is the resource pre-allocation scheme based on the semi-persistent scheduling (SPS) technique, however at the expense of low spectrum utilization. Aiming to make those pre-allocated resources more rewarding, a so-called DPre, a predictive pre-allocation scheme based on learning for low-latency uplink access in industrial wireless networks, is proposed in this paper. It intelligently explores the correlation of devices' access behavior and device utility diversity through sequential learning. Thus, flexible and judicious per-allocation decisions in both time and frequency domains can be made in an on-demand manner. Moreover, with the proposed temporal-spatial utility metric, DPre is guaranteed to reserve for more informative devices. Both theoretical analysis and simulation validate its high spectrum utilization through accurate prediction and the potential to pre-allocate for valuable packets.

State estimation plays an important role for the situation monitoring in industrial network systems, where multiple sensors observe a dynamical process and deliver state information to the remote center over wireless channels. However, the lossy wireless channels make the state information received by remote center be intermittent. Moreover, the scarcity of radio resources makes it challenging to simultaneously schedule a large number of sensors. In practice, different sensors usually have distinct contributions on state estimation, thus this paper firstly characterizes the integrated impact of sensing ability and transmission capacity on the state estimation performance, based on which a sensing aware opportunistic transmission scheme is then proposed. At each discrete time instant, the remote center determines which sensors to schedule based on the estimation demand and radio resources, and each sensor decides whether to participate the data transmission according to its residual energy. In order to further enhance the estimation performance and resource efficiency, the transmission scheduling and the sensor participation are jointly optimized by formulating a network-wide revenue maximization problem. This mix-integer nonlinear programming problem is effectively solved with the Dinkelbach method and heuristic algorithm. Finally, numerical simulation results verify the scheme efficiency.

Traffic monitoring is instrumental to a number of applications such as traffic engineering, QoS routing, anomaly detection and so on. With an accurate global view, software defined networking (SDN) has the capability to offer flexible, non-intrusive flow measurement by using wildcard matching in both direct per-flow and indirect aggregated manners. As a result, the complete and fine-grained traffic matrix (TM) monitoring, which is a challenge in traditional large-scale sensor networks, becomes more accessible. However, exiting SDN monitoring solutions have a poor trade-off between the resource-hungry nature of full sampling and limited accuracy of TM inference. Thus, in this paper, we aim to address this issue by developing CFlow, a lightweight compressive flow statistics collection (FSC) scheme for SDNs. By taking advantage of the low-rank and short-term stability features of real-world TMs, CFlow selectively samples flow statistics through custom-tailored wildcard rules, with which the final TMs are recovered via the matrix completion technique. Moreover, since the accurate measurement of large flows can improve the overall TM estimation performance, CFlow successively learns these informative flows with additional observation from both per-flow statistics collection and the latest recovered TMs. Simulation results based on real TMs demonstrate that CFlow can not only provide fine-grain visibility into network traffic but also avoid considerable monitoring overhead.

As point cloud is a typical and significant type of geometric 3D data, deep learning on the classification and segmentation of point cloud has received widely interests recently. However, the critical problems to process the irregularity of point cloud and feature extraction of shape pattern have not yet been fully explored. In this paper, a geometric deep learning architecture based on our PointDoN module is presented. Inspired by the Difference of Normals (DoN) in traditional point clouds processing, our PointDoN module is a feature aggregation module combining DoN shape pattern descriptor with both 3D coordinates and extra features (such as RGB colors). Our PointDoN-based architecture can be flexibly applied to multiple point cloud processing tasks such as 3D shape classification and scene semantic segmentation. Experiments demonstrate that PointDoN model achieves state-of-the-art results on multiple types of challenging benchmark datasets.

With the development of smart vehicles and computation-intensive vehicular applications, it is a challenge to maintain high performance for vehicles with scarce local computational resources. Mobile Edge Computing (MEC) is a computing paradigm with high potential to improve vehicular services by offloading computation-intensive tasks to the MEC servers. However, as the computational resources of MEC servers are limited, parking lots (PLs) having abundant idle computational resources should be utilized. We introduce a new computing paradigm, named by Vehicular Parked-Edge Computing (VPEC). We formulate a three-stage contract-stackelberg offloading incentive mechanism to describe this problem. The PLs are classified into different types according to their idle computational resources, and parking lot agent (PLA) offers different contracts to different types of PLs. The optimal problem is designed to maximize the utilities of vehicles, operator and PLA. We use backward induction method to solve this three-stage problem, and give the closed-form expressions of the optimal strategies for each stage. Simulation results demonstrate the feasibility of the proposed incentive mechanism and reveal the changing trend of optimal strategies in each stage when traffic density changes.

In this paper, to optimize the Wireless Charging Lane (WCL) deployment in urban areas, we focus on installation cost reduction while achieving regional balance of energy supply and demand, as well as vehicle continuous operability issues. To explore the characteristics of energy demand, we first analyze the daily trajectory of taxis in different regions and find different fluctuating features of daily energy demand. Then, we establish the WCL power supply model to obtain the wireless charging supply situation in line with the real urban traffic condition, which is the first work considering the influence of traffic lights on charging situation. To ensure minimum deployment cost and to coordinate the contradiction between regional energy supply-demand balance and overall supply-demand matching, we formulate optimization problems ensuring the charge-energy consumption ratio of vehicles. In addition, we rank the priority of WCL efficiency to reduce the complexity of solution and solve the Mixed Integer NonLinear Programming (MINLP) problem to determine deployment plan. Compared with the baseline, the proposed method has significantly improved the effect.

Femtocell network is regarded as the potential and effective technique to improve the capacity and coverage of traditional cellular networks. One of the challenges is how to access the network and manage the interference among different users. Compared with other access strategies, hybrid access strategy allows femtocell base stations (FBSs) to provide preferential access to femtocell users (FUEs) while other users can access nearby FBS with specific restrictions. In this paper, a robust downlink power control scheme is studied in two‐tier femtocell networks, where femtocells share the same frequency with macrocell. A hierarchical game framework that takes the different users' requirements into consideration is constructed. In addition, as the link gains are actually uncertain in dynamic environment, probabilistic constraints are used to describe the uncertainty. Then two sub‐problems are obtained to maximise the sum rate of macrocell and femtocells, respectively and guarantee the quality of service (QoS) of different users. To tackle the nonlinear and nonconvex optimization problem, successive convex approximation is introduced. And the practical iterative power allocation algorithm is provided. Finally, numerical results show that the proposed scheme is effective in aspect of energy saving and QoS guarantee under dynamic environment.

The secure state estimation (SSE) problem is investigated for a kind of power systems where the smart meters' measurements are transmitted to a remote estimator. In this paper, we mainly focus on the transmission via wireless networks. Taking consideration of the possible increase in transmission failure rate due to risk events, such as jamming attacks, a so-called risk-averse transmission path selection (RaTPS) method is proposed to improve SSE robustness. Based on the idea of reinforcement learning, the transmission acknowledgments are applied as the reinforcement signals to reward the source node (smart meter) for choosing more reliable paths. The multipath traffic allocation can adaptively performed according to the transmission failure rate of each path. The theoretical analysis about convergence of RaTPS and SSE is given with the technique of Markov chain, and it is illustrated in the simulation that the robustness of SSE can be improved by using RaTPS.

The frequent stop-and-go operations require high and fast burst driving power, which accelerates the electric vehicle batteries degradation. Hybrid electric storage system (HESS) is a promising solution, which supplements the battery with supercapacitor for rapid charging/discharging. If future power demand is available, effective power management can be done by fully exploiting the HESS benefits. Recent advances in the Internet of Things (IoT) have made the future information prediction practical, since surroundings information is obtainable. In this paper, a proactive energy management strategy is developed for the HESS with the IoT support. By analyzing the traffic data, a probabilistic graphical model, i.e., the conditional linear Gaussian (CLG), is designed for future driving information prediction. Since, the CLG prediction results are probability distributions, a scenario-tree method is developed to approximate the future power demand by sampling the possible future velocity profiles from the results. A stochastic model predictive control problem is established by incorporating the sampled trajectories. A fast dual proximal gradient method is proposed to solve the problem and facilitate real-time implementation. Simulation results demonstrate that the magnitude and fluctuation of the battery discharging power are reduced by 46.4% and 27.7%, respectively, compared with the battery only case.

The growing popularity of industrial wireless networks (IWNs) is driven by various applications with stringent timeliness requests. However, the ossification, deep-rooted in the one-application one-network architecture of traditional IWNs, impedes the evolution of IWNs toward smart factory. As a solution, the slice-based network virtualization (NV) breaks the tight coupling between applications and network infrastructure, and thus provides a more flexible and scalable IWN architecture. The application of NV relies on the algorithms that instantiate multiple virtual networks (VNs) on a substrate infrastructure, known as VN embedding (VNE). However, existing VNE algorithms are not necessarily optimal for IWNs due to the absence of QoS-compliant capacity. To this end, so called iVNE, an intelligent latency-aware VNE scheme, is proposed to provide deadline guarantee for various industrial VNs (IVNs), which involves both static embedding and dynamic forwarding. In the static stage, an anypath embedding algorithm is introduced for the new arrival of IVNs so that their resource demands and deadlines can be satisfied with coarse grain. Then, a dynamic anypath forwarding method is incorporated into iVNE to offer intelligent latency sensing via deep Q-learning, and thus forwarding adjustments can be made timely to address the dynamic changes of link quality and network workload. The simulation results are provided to demonstrate the learning efficiency as well as the ability of load-balancing through responsive forwarding under dynamic environment.

The detection and isolation problem of false data injection (FDI) attacks in large-scale smart grid systems, is investigated in this paper. The FDI attacks can bypass the traditional bad data detection techniques, by falsifying the process of state estimation. For this reason, the emergency of FDI attacks brings great risk to the security of smart grids. To address this crucial problem, a novel detection and isolation scheme against the FDI attacks for the large-scale smart grid system is proposed. We first design an interval observer to estimate the interval state of internally physical system accurately, based on the constructed physical dynamics of grid systems. Taking the bounds of internal state and external disturbance into account, the detection criterion that an alarm is generated when the interval residuals does not include the zero value is proposed. To address the limitation of precomputed threshold, we use the interval residuals regarded as a nature detection threshold to replace the evaluation function and detection threshold used in traditional attack detection methods. Furthermore, an attack signature logical judgment matrix-based isolation algorithm is further proposed to isolate the sensors, in which the FDI attacks may be injected into the attacked subarea. Finally, the effectiveness of the developed detection and isolation scheme is demonstrated by using detailed case studies on the IEEE 128-bus smart grid system.

Directional antenna-based aerial networking (DAAN) is referred as a promising technology to meet the dynamic data demands for unmanned aerial vehicles (UAVs). However, the narrow radiation pattern of directional antennas and the mobility of UAVs make it challenging to form a robust DAAN. This paper presents a heading control strategy for UAV-carried directional antennas to establish a robust long-range aerial communication channel. The heading control process is mainly divided into two phases, i.e., position estimation and angle adjustment. In the first phase, a proportional-derivative-based tracking controller is designed for each UAV to ensure the consistency of heights, pitch, and roll angles. Particularly, the received signal strength indicator is adopted as an auxiliary measuring component, and then a consensus-based unscented Kalman filtering algorithm is developed to estimate the position of UAVs. With the estimated position information, a feedback-based heading controller is designed for directional antenna in the second phase to enable robust long-range aerial communication channel. Moreover, the convergence conditions and Cramér-Rao lower bounds are also provided. Finally, simulation results are presented to demonstrate the effectiveness of the proposed strategy. It is shown that the influence of malicious measurements can be reduced, and the signal strength can be significantly improved as compared with the omni-directional antenna-based works.

This letter is concerned with an asynchronous localization issue for underwater acoustic sensor networks (UASNs), subject to asynchronous clocks and stratification effects in physical channels. A novel unscented transform-based localization algorithm is proposed to estimate the positions of sensor nodes. Instead of linearizing the measurement equations, the proposed algorithm employs the unscented transform to compute the Jacobian matrix to reduce the linearization errors. Particularly, the ray-tracing approach is adopted to model the stratification effect. Moreover, the convergence analysis and Cramér-Rao lower bound for the algorithm are also provided. Simulation results show that the proposed algorithm can effectively improve the estimation accuracy as compared with the existing works.

The adaptive neural network tracking control problem is investigated for a class of interconnected switched systems. The considered systems are with unmodeled dynamics, some of which do not satisfy the input-to-state stable (ISS) condition. By utilizing the neural network to approximate the composite unknown nonlinear functions, the corresponding decentralized tracking controller is designed for each subsystem with the help of dynamic surface control method. Some subsystems are stable with the designed controller, while other subsystems may not be stable because of non-ISS unmodeled dynamics, but they have some special properties with the designed controller. Then, a novel switching signal scheme is established such that the interconnected switched system is stable in the sense of semi-global boundedness, and the tracking errors can converge to predefined residual sets with prescribed performance index. Moreover, the switching scheme allows the number of switches to grow faster than traditional average dwell time method. Finally, a numerical example is provided to demonstrate the effectiveness of the presented results.

In this paper, a pricing mechanism is proposed for the electricity supply chain, which is consisting of one generation company (GC), multiple consumers, and competing utility companies (UCs). The UC participates in electricity supply chain management by a revenue sharing contract (RSC). In the electricity supply chain, the electricity real-time balance has an important role in the stable operation of the power system. Therefore, we introduce the demand response into the electricity supply chain to match supply with demand under forecast errors. Hence, we formulate a noncooperative game to characterize the interactions among the multiple competing UCs, which set the retail prices to maximize their profits. Besides, the UCs select their preferred contractual terms offered by the GC to maximize its profits and coordinate the electricity supply chain simultaneously. The existence and uniqueness of the Nash equilibrium (NE) are examined, and an iterative algorithm is developed to obtain the NE. Furthermore, we analyze the RSC that can coordinate the electricity supply chain and align the NE with the cooperative optimum under the RSC. Finally, numerical results demonstrate the superiority of the proposed model and the influence of market demand disruptions on the profits of the UCs, GC, and supply chain.

Most research so far in teleoperation control has assumed that all information is transmitted continuously. Unfortunately, the damaged and electromagnetic interfered line cause communication link failure. In addition, the unreliable link further leads to port data congestion. The data packet will be discarded when the buffer overflows. Consequently, it is unknown whether stability of the teleoperator could be guaranteed in the presence of intermittent communications. In order to overcome these drawbacks, in this paper, we provide a solution to the formation control problem of a single-master-multislave teleoperator in the situation where each robot is allowed to communicate with its neighbors only at some irregular discrete time instants. The relationship among control gains, topology, and maximum-allowable connected interval is presented. Simulations are performed to show the validity of our proposed approach.

This paper investigates the event-based leader-following consensus problem for high-order nonlinear multiagent systems whose dynamics are in strict feedback forms and satisfy Lipschitz condition. By using self-triggered control scheme and dynamic output feedback control method in combination, a new class of distributed self-triggered consensus protocols is proposed based only on the relative output measurements of neighboring agents. It is noted that the proposed protocols only require the output information of neighboring agents to be shared and the designed self-triggered algorithm can avoid continuous communication among neighboring agents, thus the communication cost is reduced significantly. Sufficient conditions in terms of matrix inequalities are derived to guarantee the exponential leader-following consensus. The effectiveness of the theoretical results is illustrated through a simulation example.

In this paper, we investigate the secure state estimation (SSE) problem in power systems, where the physical system is measured by meters and mainly focuses on the measurements sent to a remote estimator via wireless networks faced with jamming attacks. Malicious attacks on the transmission paths block the data transmission and deteriorate the performance of estimation. Various works have been proposed to cope with the transmission failure. Few of them have considered the case that a smart attacker could adjust strategies according to the defensive methods with advanced communication techniques. We propose the antijamming game framework for SSE. Under this framework, defensive path selection (DPS) is proposed based on multiagent reinforcement learning to make optimal path selection against the intelligent attacker and improve the transmission performance for SSE. The effectiveness of the proposed method is theoretically proved to improve the robustness of estimation and the capability of DPS is analyzed.

Vision-based intelligent systems like automatic driving or driving assistance can be improved by enhancing the visibility of the scenes captured in bad weather conditions. In particular, many image dehazing algorithms (DHAs) have been proposed to facilitate such applications in hazy weather. Contrary to the substantial progress of DHA developing, the quality evaluation of DHAs falls behind. Generally, DHAs can be evaluated qualitatively by human subjects or quantitatively by objective quality measures. Compared with the subjective evaluation which is time consuming and difficult to apply, objective measures with quantitative results are more needed in practical systems. But in the literature, very few measures are widely utilized, and even less measures correlate well with the overall dehazing quality (DHQ). In this paper, we study the DHQ evaluation using real hazy images systematically. We first construct a DHQ database, which is the largest of its kind so far and includes 1750 dehazed images generated from 250 real hazy images of various haze densities using seven representative DHAs. A subjective quality evaluation study is subsequently conducted on the DHQ database. Then, we propose an objective DHQ index (DHQI) by extracting and fusing three groups of features, including: 1) haze-removing features; 2) structure-preserving features; and 3) over-enhancement features, which have captured the most key aspects of dehazing. DHQI can be utilized to evaluate DHAs or optimize practical dehazing systems. Validations on the constructed DHQ database and three other databases with synthetic haze have verified the effectiveness of DHQI. Finally, we give an overview of the current DHA quality evaluation strategies, discuss their merits and demerits, and give some suggestions on systematic DHA quality evaluation. The DHQ database and the code of DHQI will be released to facilitate further research.

To enhance the visibility and usability of images captured in hazy conditions, many image dehazing algorithms (DHAs) have been proposed. With so many image DHAs, there is a need to evaluate and compare these DHAs. Due to the lack of the reference haze-free images, DHAs are generally evaluated qualitatively using real hazy images. But it is possible to perform quantitative evaluation using synthetic hazy images since the reference haze-free images are available and full-reference (FR) image quality assessment (IQA) measures can be utilized. In this paper, we follow this strategy and study DHA evaluation using synthetic hazy images systematically. We first build a synthetic haze removing quality (SHRQ) database. It consists of two subsets: regular and aerial image subsets, which include 360 and 240 dehazed images created from 45 and 30 synthetic hazy images using 8 DHAs, respectively. Since aerial imaging is an important application area of dehazing, we create an aerial image subset specifically. We then carry out subjective quality evaluation study on these two subsets. We observe that taking DHA evaluation as an exact FR IQA process is questionable, and the state-of-the-art FR IQA measures are not effective for DHA evaluation. Thus, we propose a DHA quality evaluation method by integrating some dehazing-relevant features, including image structure recovering, color rendition, and over-enhancement of low-contrast areas. The proposed method works for both types of images, but we further improve it for aerial images by incorporating its specific characteristics. Experimental results on two subsets of the SHRQ database validate the effectiveness of the proposed measures.

This paper investigates the cooperative stabilization problem for a class of linear switched systems under asynchronous switching. Based on a novel class of switching signals which prevail over the traditional average dwell time scheme, a sufficient condition of global asymptotic stability is proposed for the considered system. Unlike the existing results, the provided condition permits the Lyapunov-like function to increase not only in the period of mode-identifying process but also in normal-working period with matched controller. Compensating the increment by an increased decrement, the proposed approach can guarantee the decrease of the Lyapunov-like function from a whole perspective. Moreover, the solvability condition is established as well to obtain the gains for corresponding controllers. Finally, a simulation example is provided to verify the validity of the presented method.

This paper studies the distributed adaptive fuzzy containment tracking control for a class of high-order stochastic pure-feedback nonlinear multiagent systems with multiple dynamic leaders and performance constraint requirement. The control inputs are quantized by hysteresis quantizers. Mean value theorems are used to transfer the nonaffine systems into affine forms and a nonlinear decomposition is employed to solve the quantized input control problem. With a novel structure barrier Lyapunov function, the distributed control strategy is developed. It is strictly proved that the outputs of the followers converge to the convex hull spanned by the multiple dynamic leaders, the containment tracking errors satisfy the performance constraint requirement and the resulting leader-following multiagent system is stable in probability based on Lyapunov stability theory. At last, simulation is provided to show the validity and the advantages of the proposed techniques.

In industrial automation, monitoring information is critical and expected to be received with ultra- high reliability and low latency. On the other hand, different industrial monitoring applications usually have diverse requirements on the transmission quality. This paper investigates the deadline aware reliable transmission in mission-critical machine-type communications (MTCs) to satisfy the service requirements for different monitoring applications in terms of reliability and latency. Specifically, a hybrid non-orthogonal multiple access (NOMA) framework is firstly introduced for improving spectrum utilization as well as meeting the diverse requirements of different applications. Under this framework, a NOMA-assisted small-packet transmission scheme is proposed for mission- critical MTCs, and the related performance is then mathematically formulated as a constrained optimization problem with the objective to maximize the network-wide revenue. The formulated non- trivial problem is effectively solved by considering the diverse requirements of different applications. Simulation results are provided to demonstrate both the impact of small packet size and the superiority of NOMA technique on the network-wide revenue improvement.

Clock synchronization is required by most time-based localization methods in wireless sensor networks (WSNs). However, synchronization is often coupled with localization. Furthermore, the accuracy of anchor positions depends on several factors, and uncertainties may exist in the observed anchor positions. Thus, we propose a joint time and location estimation of target sensors using a single mobile anchor to reduce the deployment cost for WSNs. Taking anchor position uncertainties into account, we develop an expectation maximization (EM)-type method to solve the joint estimation problem. The simulation results verify the performance of the proposed EM method is superior than conventional methods, such as least squares (LS), weighted least squares (WLS) and generalized total least squares (GTLS) estimators.

This paper considers the multi-loop wireless control system (WCS), where control command is delivered from the remote controller to multiple actuators over shared wireless channels. However, different system dynamics of multiple loops make each loop usually have different demands on the success probability of receiving control commands. Thus, the control performance of overall system is affected by both the transmission reliability and the dynamics of each loop. In this paper, we propose a demand-driven and energy-efficient transmission strategy to adaptive to wireless channels and system dynamics. In order to improve the control performance without burdening the scarce spectrum resources, the remote controller is equipped with multiple antennas, and the transmit beamforming design with power control is adopted to improve the success probability of control commands. In particular, we firstly characterize the control performance of each loop with a pre-defined Lyapunov function, which would decrease exponentially in expectation if the packet loss rate meets the stability condition of each loop. Then, a control stability constrained optimization problem is formulated to minimize the overall cost including energy consumption and linear quadratic Gaussian control cost. The non-trivial probabilistic constraint is effectively handled with the differential accumulation and difference-convex methods. Finally, simulation results verify that the proposed strategy has superiority on reducing control cost and energy consumption without considerations of system dynamics or joint design of transmit beamforming and power control.

A typical method for privacy-preserving data publishing mechanism is to add random noise to the original data for publishing. No matter what kind of noise is added, there is a chance that the original state can be estimated in a certain accuracy. The probability of the original data inferred by the malicious receiver in a given interval is measured by (α, β) -data-privacy. With random noise added to the original data, the utility of the published data will decrease. In this paper, we investigate the tradeoff between data privacy and data utility under (α,β) -data-privacy, aiming to seek an optimal noise distribution. To maximize the weighted sum of privacy and utility we prove that when the added noise is symmetric and the data utility is measured by l1- or l2-norm function, the optimal noise follows the uniform distribution. Then we further investigate the optimal noise to maximize data utility with a certain privacy guarantee and we derive that the optimal noise is a group of impulse functions. Finally, we compare (α, β) -data-privacy with differential privacy and obtain the inequality relationship between the two privacy parameters. Simulations are conducted to validate the correctness of the obtained results.

Driven by mission-critical applications in modern industrial systems, the 5th generation (5G) communication system is expected to provide ultra-reliable low-latency communications (URLLC) services to meet the quality of service (QoS) demands of industrial applications. However, these stringent requirements cannot be guaranteed by its conventional dynamic access scheme due to the complex signaling procedure. A promising solution to reduce the access delay is the pre-allocation scheme based on the semi-persistent scheduling (SPS) technique, which however may lead to low spectrum utilization if the allocated resource blocks (RBs) are not used. In this paper, we aim to address this issue by developing DPre, a predictive pre-allocation framework for uplink access scheduling of delay-sensitive applications in industrial process automation. The basic idea of DPre is to explore and exploit the correlation of data acquisition and access behavior between nodes through static and dynamic learning mechanisms in order to make judicious resource per-allocation decisions. We evaluate the effectiveness of DPre based on several monitoring applications in a steel rolling production process. Simulation results demonstrate that DPre achieves better performance in terms of the prediction accuracy, which can effectively increase the rewards of those reserved resources.

The wide application of Internet of Things (IoT) in industrial automation encourages the emergence of a new paradigm of industrial IoT systems, wireless control system (WCS), where the system and/or control information is delivered over wireless channels. In practical systems, WCSs would consist of multiple control-loops in general, the resource competition among which would seriously increase mutual interferences and transmission collisions, making it is difficult to provide the required transmission reliability for the control strategy. To address this issue, we design the control strategy together with the hybrid cooperative transmission scheme for multiloop WCSs in a proactive way. We first define the overall system cost function to explore the impacts of standard linear quadratic regulator control cost and wireless transmission reliability on the control performance. In order to further minimize the overall system cost while guaranteeing the control stability, we then propose a control performance aware cooperative transmission scheme, which is formulated as a constrained optimization problem. Decomposition method and heuristic algorithms are designed based on the feature of network structure to solve the formulated mixed integer nonlinear programming problem efficiently. Finally, simulation results demonstrate that by using the proposed strategy, the overall system cost is significantly reduced, decreasing by 78% and 82% compared to the cases without considerations of system dynamics and without cooperative transmission, respectively.

This paper focuses on the problem of decentralized event-based dynamic output feedback adaptive fuzzy control for a class of interconnected stochastic nonlinear systems. In order to relax the Lipschitz condition for the nonlinearity, a novel dynamic gain observer is constructed to estimate the unmeasured state variables. The funnel-like control technique is proposed to ensure that the output of each subsystem satisfies the prescribed performance requirement. To save energy in signal transmission, the controller and its triggered mechanism are codesigned based on backstepping method. By using the approximation theory of fuzzy logic systems, an unknown continuous function is approximated, and the difficulty caused by unmodeled dynamics is removed with the aid of changing supply function idea. By applying the Lyapunov stability theory, it is proved that all the signals of the resulting closed-loop system with the designed controller are bounded in probability. Finally, simulation results are given to verify the effectiveness of the theoretical results.

This paper considers the adaptive fuzzy output feedback tracking control problem for a class of uncertain nonlinear switched systems with time delay and unmodeled dynamics. Based on a kind of switched K-filters, a prescribed performance control scheme is proposed to guarantee the tracking performance and restrain the fluctuation caused by switches between submodes as well. In addition, fuzzy logic systems (FLSs) are use to approximate unknown nonlinear functions and dynamic surface control (DSC) method is employed to eliminate the explosion of complexity problem inherent in traditional backstepping method. The proposed controllers of corresponding subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time (ADT). A numerical simulation is performed to illustrate the effectiveness of the proposed approach.

Downtime is a key performance index for industrial automation systems. An industrial automation system achieves maximum productivity when its downtime is reduced to the minimum. One approach to minimize downtime is to predict system faults and recover from them automatically. A cloud-based decision support system is proposed for rapid problem identifications and to assist the self-management processes. By running multiple parallel simulations of control software with real-time inputs ahead of system time, faults could be detected and corrected automatically using autonomous industrial software agents. Fault trees, as well as control algorithms, are modeled using IEC 61499 function blocks that can be directly executed on both physical controllers and cloud services. A case study of water heating process is used to demonstrate the self-healing process supported by the cloud-based decision support system.

Since the hot metal silicon content simultaneously reflects the product quality and the thermal state of the blast furnace, accurately predicting the development tendency of hot metal silicon content has the immensely guiding role for blast furnace operators. This paper focuses on fuzzy classifier design for the development tendency of hot metal silicon content based on blast furnace operation data. The cross characteristic of binary classification problem was found via embedding high-dimensional blast furnace data into a two-dimensional space. Then, presented a nonparallel hyperplanes based fuzzy classifier, which conquered the cross classification still holding the interpretability advantage as fuzzy classifier. The proposed method was tested on No.2 blast furnace of Liuzhou Steel in China, that demonstrated the excellent performance compared with some other classifier algorithms.

In industrial automation, the state of process control could be monitored by spatially distributed sensors and 5G machine-type communication (MTC) enabled industrial Internet of things (IIoT). Thus, ultra-reliable MTC and high-accurate state estimation play important roles for ensuing system stabilization. However, it is challenging due to complex industrial wireless environments and limited communication resources. To address this issue, this paper first presents a transmission-estimation codesign framework to lay down the foundation for guaranteeing the prescribed estimation accuracy with limited communication resources. Under this framework, a hierarchical transmission-estimation approach is proposed to improve the transmission reliability and estimation accuracy according to system dynamics. The proposed approach is then optimized by formulating a constrained minimization problem, which is mixed integer nonlinear programming and solved efficiently with a block-coordinate-descent-based decomposition method. Finally, simulation results demonstrate that the proposed approach has superiorities in improving both the estimation accuracy and the energy efficiency.

Network systems often rely on distributed algorithms to achieve a global computation goal with iterative local information exchanges between neighbor nodes. To preserve data privacy, a node may add a random noise to its original data for information exchange at each iteration. Nevertheless, an eavesdropping node can estimate other's original data based on the information it received. The estimation accuracy and data privacy can be measured in terms of (E, δ)-data-privacy, defined as the probability of E-accurate estimate (the difference of an estimation and the original data is within E) is no larger than δ (the disclosure probability). How to optimize the estimation and analyze data privacy is a critical and open issue. In this paper, a theoretical framework is developed to investigate how to optimize the estimation of neighbor's original data using the local information received, named optimal distributed estimation. Then, we study the disclosure probability under the optimal estimation for data privacy analysis. We further apply the developed framework to analyze the data privacy of the privacy-preserving average consensus algorithm and identify the optimal noises for the algorithm.

This paper presents a joint design framework of fronthaul and access links in cloud radio access networks, wherein the fronthaul data delivery between the central processor (CP) and small-cell base stations (SBSs) is carried over wireless links, which is more cost effective and flexible than the conventional wired fronthaul solutions. In this framework, the coordinated beamforming scheme is adopted by the SBSs to serve the users cooperatively, which not only reduces the bandwidth requirement over the fronthaul links, but also eliminates the inter-cell interference in the access links. To further exploit the spatial diversity over the fronthaul links, the multiuser beamforming scheme is adopted by the CP to deliver the data of all users to their serving SBSs simultaneously. This non-convex and combinatorial optimization problem is reformulated to a unified beamformer design problem using the ℓ0/ℓ1-norm framework, which is still difficult to obtain the optimal solutions. Therefore, we first propose an algorithm based on the difference of convex (DC) programming scheme to find the suboptimal solutions, whereby the formulated problem is transformed to the standard DC programming problem and solved iteratively using the convex-concave procedure algorithm. We then propose another algorithm based on the successive convex approximation and weighted minimum-mean-squarederror approaches, and solve the transformed problem using the block coordinate update scheme. The pros and cons of these two algorithms are discussed, and simulation results are provided to demonstrate the performance gain of our schemes over other benchmark schemes.

The wide application of Internet of Things (IoT) in industrial automation leads to the emergence of a new paradigm of industrial IoT systems, namely wireless control system, where control commands are transmitted from the remote controller to multiple actuators over shared wireless channels. Considering system stability, distinct subsystems usually have different requirements on the transmission quality of control commands due to different system dynamics. In this paper, we aim to simultaneously guarantee the stability of all subsystems and minimize the weighted sum of control cost and transmission cost. To this end, the maximum tolerated packet loss rate of each subsystem is first characterized by a pre-defined Lyapunov function. Then, based on channel conditions and system dynamics, a beamforming-assisted hierarchical coordinated transmission strategy is proposed to alleviate the impact of unreliable transmission on the control performance. The control performance and energy efficiency are further optimized by formulating an overall cost minimization problem constrained by the system stability. Both the differential accumulation and the difference-convex methods are employed to effectively deal with the constraint that is expressed in an implicit probabilistic form. Finally, simulation results demonstrate that the proposed strategy has the advantages of reducing control cost and energy consumption.

In the performance analysis and control of wireless control systems, the high accuracy state estimate is a necessary prerequisite for feedback control. However, it is challenging due to the server interference wireless environment as well as limited spectrum and energy resources. To address this issue, this paper firstly presents a hierarchical framework to lay the foundation to guarantee the prescribed estimation accuracy for WCSs with minimal resource consumption. Under this framework, a resource-efficient hierarchical transmission-estimation co-design approach is proposed to adapt to the system dynamics and communication resources for improving the transmission reliability and reducing the energy consumption. Then, a joint optimization problem subject to constraints of estimation convergence, transmission reliability and resource limitation is formulated to minimize the overall estimation-energy cost for WCSs in terms of estimation error and energy consumption. The solution of mixed integer nonlinear programming problem is got efficiently and effectively with decomposition methods. Finally, simulation results demonstrate that the developed hierarchical transmission-estimation co- design approach has superiorities on improving the estimation accuracy and the resource efficiency. The estimation-energy cost caused with the proposed approach is about 51% and 77% of that with two compared ones without awareness of wireless environment variations and system dynamics, respectively.

The evolution of industrial wireless sensor networks (IWSNs) is driven by various factory automation applications with strict demands on latency and reliability, which requires flexible network resource allocation to support diverse QoS requirements of different applications. To this end, we propose an application-driven virtual network embedding (AVNE) scheme to facilitate the QoS provisioning for different applications leveraging the network virtualization (NV) technique. AVNE employs a novel anypath link mapping approach based on the anypath routing scheme, which greatly improves the efficiency of the embedded path by exploiting the unique features of wireless channels. Our simulations demonstrate that the proposed AVNE scheme significantly improves the revenue and admission ratio while minimizing the cost in virtualized IWSNs. AVNE also achieves better load-balance and thus is less prone to cause bottleneck nodes and links.

Process parameter estimation, to a large extent, determines the quality of the industrial production. Traditionally, limited sensors are deployed in production field by elaborate wiring, which cannot provide the accurate estimate in the hostile industrial environment. Recently, industrial wireless sensor network (IWSN) has been considered as one promising technology to improve the process parameter estimation by deploying more sensors flexibly and making them work collaboratively. In this paper, a sequential IWSN (Seq-IWSN) approach is provided for the temperature estimation of the steel slab during the hot strip milling process. In Seq-IWSN, the network deployment and scheduling strategies coupling with the process parameter estimation algorithm are involved. Simulation results based on NS3 network simulator show that Seq-IWSN can help to reduce the estimation error to less than 3°C, although the covariance of the sampling noise is as large as 100.

Industrial Cyber-physical systems (ICPSs) are expected to provide effective solutions for improving the operation of many existing industrial manufacturing systems. Wireless sensor networks in the industrial field is classified as low-power and lossy network due to energy constrained devices, the dynamic environment and a high packet loss rate. Energy efficiency and delivery reliability need to be achieved to cope with limited resources and the dynamic environment. In this paper, we propose a cross-layer design CDER to achieve energy efficiency and ensure the reliability of transmission based on 6LoWPAN, considering the routing stability. For purpose of achieving energy conservation, we propose a packet reassembly algorithm to decrease the number of forward packets. Moreover, for stable route establishment to deal with the dynamic environment, an energy balance algorithm considering routing stability is proposed. Extensive simulations show that our algorithm CDER improves the network performance, while achieving energy efficiency and improving routing stability.

Received Signal Strength (RSS) estimation for networks in urban transportation systems can be carried out by Vehicular Sensor Networks (VSN). All moving vehicles on roads with signal-sensing applications can act as mobile sensors, collecting RSS along their driving routes and uploading data to data center at the end of every drive. However, there must be inconsistencies among RSS values achieved at same locations owing to different types and brands for vehicles. In this paper, we propose a RSS estimation algorithm to improve the credibility of RSS information at certain locations. This algorithm is based on Bayesian learning mechanism and calibrated by the values gained by high-precision equipment PXI experimental platform. We evaluate the algorithm based on the real-world data collected by our Android application. The results demonstrate the effectiveness and superiority of the method compared with typical algorithms.

In an increasingly dense and heterogeneous wireless network with various access points, the users are likely to lie in the overlapping coverage areas of multiple radio access technologies, which motivates the boost of user throughput and quality of service via small cell dual-connectivity. In this paper, with the objective of improving network throughput and considering fairness among users, we formulate a small cell selection problem to maximize the network utility reflecting proportional fairness, which is actually the sum of the logarithm of long-term rate of users. However, the formulated problem turns out to be non-convex and combinatorial, and is difficult to be transformed into a convex problem and traditional game-theoretic methods cannot be used. In this context, we introduce a many-to-one matching game with externalities, and develop a distributed algorithm that converges to a stable matching. To further enhance network throughput by mitigating co- channel interference among cellular small cells, a joint cell selection and power control algorithm is developed by adopting an iterative approach to determine them separately and sequentially. Lastly, compared with the nearest distance-based cell selection scheme and the cell selection scheme maximizing the total long-term rate of users, numerical results show the convergence of the matching-based cell selection algorithm and the joint cell selection and power control algorithm, as well as their significant improvement on both total long-term rate of users and user fairness.

In this study, a power allocation scheme for two‐tier femtocell networks is proposed to maximise the user utilities constrained with satisfactory quality of service, where femtocell users share the same frequency with macrocell users (MUEs). Since the environment changes and the channel gains cannot be assumed to be constants, a worst‐case method is used to address the uncertainty of the power allocation problem. A non‐cooperative game model is developed to maximise the utilities of femtocell users by letting the users competing the utilities with others. As the robustness can be affected by the changing gains of communication links, the robust Stackelberg game is proposed to model this hierarchical competition where the MUEs and femtocell users act as leaders and followers, respectively. Two effective pricing schemes are applied to maximise the utilities, when different user demands are required, where the uniqueness of Nash equilibrium is proved in the two schemes. Numerical results show the convergence of the Stackelberg game with uncertainty and also the results demonstrate the effectiveness of the power allocation algorithm.

In smart grid, the frequency regulation can be provided by both the automatic generation control (AGC) and the demand-side regulation, and the regulation errors increase the electricity costs to the utility company. The demand-side regulation adopts a hierarchical communication architecture, and the data aggregator unit (DAU) may suffer from congestions which consequently increase the costs to the utility company for more AGC service except for the demand-side regulation. In this paper, we employed the base stations as relays and formulated the electricity costs-based upon the regulation errors and the packets loss model. Specifically, the utility company decides the relaying bandwidth to minimize its electricity costs, and the relay selects the base price to maximize its profits. The novelty of this paper is twofold. First, we formulate the interactions between the utility company and the relay as a bargaining problem. Second, we utilize the Nash bargaining solution (NBS) and Raiffa-Kalai-Smorodinsky (RBS) bargaining solution to achieve the Pareto-optimal outcome. Furthermore, we extended the results to the case with multiple DAUs and multiple relays. The numerical results demonstrate the cost reduction of the utility company and the profit increase of the relay under the NBS or RBS strategy. In addition, the NBS strategy can bring about more profits for the relay than the RBS strategy, while the RBS strategy can provide a fairer payoff allocation and lower costs to the utility company than the NBS strategy.

This paper considers the distributed consensus tracking problem for a class of high-order stochastic multiagent systems with uncertain nonlinear functions under a fixed undirected graph. Through the recursive method, the novel nonlinear distributed controllers are designed. By constructing a kind of special form for the virtual controller in the first step of recursive design, we realize that the state variables of every agent are separated except the outputs of the adjacency agents. The designed controller of each agent only depends on its own state variables and the outputs of the adjacent multiagents. With the proposed method, it is not required any more that the orders of the agents are same. This makes the designed controller be easier to be implemented and the proposed method be applicable for a wider class of multiagent systems. The efficiency of the design approach is illustrated by a simulation example.

The leader-following consensus problem is investigated for second-order time-varying nonlinear multiagent systems with unmodeled dynamics and unknown parameters over directed communication topology. Under the assumption that the unknown nonlinearities satisfy Lipschitz conditions with time-varying gains, a local adaptive law is introduced for the design of consensus protocol that enable all followers' state variables to consensus with that of leader asymptotically. The proposed protocols are independent of system parameters and only require the relative state information of its neighbors, and hence they are fully distributed. Simulation examples are given to illustrate the effectiveness of the theoretical results.

This paper studies the problem of distributed output tracking consensus control for a class of high-order stochastic nonlinear multiagent systems with unknown nonlinear dead-zone under a directed graph topology. The adaptive neural networks are used to approximate the unknown nonlinear functions and a new inequality is used to deal with the completely unknown dead-zone input. Then, we design the controllers based on backstepping method and the dynamic surface control technique. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of semiglobally uniform ultimate boundedness and the tracking errors between the leader and the followers approach to a small residual set based on Lyapunov stability theory. Finally, two simulation examples are presented to show the effectiveness and the advantages of the proposed techniques.

In this paper, we study the problem of output feedback distributed containment control for a class of high-order nonlinear multiagent systems under a fixed undirected graph and a fixed directed graph, respectively. Only the output signals of the systems can be measured. The novel reduced order dynamic gain observer is constructed to estimate the unmeasured state variables of the system with the less conservative condition on nonlinear terms than traditional Lipschitz one. Via the backstepping method, output feedback distributed nonlinear controllers for the followers are designed. By means of the novel first virtual controllers, we separate the estimated state variables of different agents from each other. Consequently, the designed controllers show independence on the estimated state variables of neighbors except outputs information, and the dynamics of each agent can be greatly different, which make the design method have a wider class of applications. Finally, a numerical simulation is presented to illustrate the effectiveness of the proposed method.

An increasingly important goal of industrial automation systems is to continuously optimize physical resource utilization such as materials. Distributed automation is seen as one enabling technology for achieving this goal, in which networking controller nodes collaborate in a peer-to-peer way to form a new paradigm, namely industrial cyber-physical systems (iCPS). In order to achieve rapid response to changes from both high-level control systems and plant environment, the proposed self-manageable agent relies on the use of the service-oriented architecture (SOA) that improves flexibility and interoperability. It is enhanced by the autonomic service management (ASM) to implement software modifications in a fully automatic manner, thus achieving self-manageable and adaptive iCPS. The architecture design of the ASM is provided and integration with SOA-based execution environment is illustrated. Preliminary tests on self-management are completed using a case study of an airport baggage handling system.

The cloud radio access network (C-RAN) has been deemed a cost-effective architecture for exploiting the capacity benefit of densely deployed radio access points. The low-latency fronthaul data transmission from the central processor to small-cell base stations (SBSs) is a key requirement in C-RANs for which conventional wired fronthaul links will be cost-prohibitive and also inconvenient. Therefore, scalable and low-cost wireless fronthaul solutions have drawn much attention in both industry and academia. In this paper, we propose adopting the multicast beamforming strategy over fronthaul links to deliver each user's message to a cluster of SBSs selected according to the user-centric clustering scheme, which then adopts the joint beamforming technique to cooperatively transmit the signal to the target users. Some approximate techniques are applied to obtain a tractable formulation for this mixed integer nonlinear programming problem, and an iterative algorithm based on the block coordinate update method is proposed accordingly. Then, a binary search based algorithm is developed to preserve the sparsity of beamformers due to the relaxation of the discrete clustering function with the continuous exponential function. Extensive simulation results are provided to show the performance of the proposed algorithms in terms of convergence, power consumption, and weighted sum rate.

Traffic safety and efficiency are the most important motivations for the rapid development of vehicular ad hoc network (VANET) technology. VANET is considered to provide the unified platform and solutions for the intelligent transportation system (ITS). In this paper, a novel method named spectrum penetration is proposed to improve the throughput of multi-channel MAC for VANET. Meanwhile, it can overcome the drawback of multi-channel hidden terminal problem by performing cross channel data exchange. The proposed spectrum penetration based multi-channel MAC (SPBM) protocol makes full use of the seven channels and idle subcarriers specified in the IEEE 802.11p to ensure the timely safety message broadcast and improve the throughput of non-safety messages. The simulation results demonstrate that our proposed SPBM protocol has a high channel utilization and strong adaptability to different traffic densities.

In industrial cyber-physical systems (ICPSs), state estimation provides the best possible approximation for the unmeasurable system state based on the received measurements from sensors via lossy wireless channels. As a result, the estimation performance heavily depends on the transmission reliability. In this paper, a cognitive radio assisted cooperative transmission scheme is proposed to improve the accuracy of state estimation by delivering necessary redundant measurements to the remote estimator. The relationship between the accuracy of multi- sensor state estimation and the arrival rate of measurements is explored. Based on this, an optimization problem is formulated to minimize the state estimation error by jointly allocating the harvested licensed channels and the ISM channels with power control and admission control. A sub-optimal decomposition scheme is proposed to solve this intractable problem efficiently. Numerical results demonstrate that the proposed scheme significantly outperforms existing schemes by reducing more than 73% packet loss rate and 56% estimation errors.

In this paper, we consider a simple model of distributed sensor fusion problem in sensor networks with asymmetric links, where the common goal is linear parameter estimation. For the realistic scenario of bandwidth-constrained networks, we propose a least square approach, based on distributed quantized consensus algorithms, to compute the ideal centralized sample mean estimate. Analytical results show that the proposed approach is effective in smearing out the quantization errors, and outperforms the centralized approaches with respect to the estimation performance. Simulation results are provided to validate the analytical results.

The cloud radio access network (Cloud-RAN) is an emerging network architecture for the next generation mobile wireless networks. However, connecting a large number of small base stations (SBSs) through wired backhaul links is inconvenient and cost-prohibitive. Therefore, wireless backhaul has become a promising solution for Cloud-RAN. In this paper, we propose to apply the multicast beamforming technique in wireless backhaul, which is efficient to share each user's message to a cluster of SBSs for collaboratively serving the user through joint beamforming. The joint optimization of multicast beamforming in backhaul links and user-centric clustering in access links is formulated as a weighted sum rate maximization problem. To tackle this intractable problem, several approximation and transformation techniques are introduced based on the semidefinite relaxation method and successive convex approximation approach. A block coordinate descent algorithm is developed to solve the approximate optimization problem iteratively by exploiting its special structure. Simulation results show that the proposed algorithm is guaranteed to converge to the stationary point, and the performance outperforms other wireless backhaul schemes under various network scenes.

With the explosive growth of Internet of Vehicles (IoV), it is undoubted that vehicular demands for real-time Internet access would get a surge in the near future. Therefore, it is foreseeable that the cars within the IoV will generate enormous data. On the one hand, the huge volume of data mean we could get much information (e.g., vehicle's condition and real-time traffic distribution) through the big data analysis. On the other hand, the huge volume of data will overload the cellular network since the cellular infrastructure still represents the dominant access methods for ubiquitous connections. The vehicular ad hoc network (VANET) offloading is a promising solution to alleviate the conflict between the limited capacity of cellular network and big data collection. In a vehicular heterogeneous network formed by cellular network and VANET, an efficient network selection is crucial to ensure vehicles' quality of service. To address this issue, we develop an intelligent network recommendation system supported by traffic big data analysis. Firstly, the traffic model for network recommendation is built through big data analysis. Secondly, vehicles are recommended to access an appropriate network by employing the analytic framework which takes traffic status, user preferences, service applications and network conditions into account. Furthermore an Android application is developed, which enables individual vehicle to access network automatically based on the access recommender. Finally, extensive simulation results show that our proposal can effectively select the optimum network for vehicles, and network resource is fully utilized at the same time.

Traffic management, road sensing and multimedia delivery in vehicular ad-hoc network (VANET) are application domains whose performance depend on network throughput. Rate adaptation is the key method to maximize the throughput by estimating the current channel qualities and deciding the best bitrate for the next frames. In VANET, rate adaptation is more challenging due to the rapid variation of channel qualities caused by the high speed and density of vehicles. Fortunately, vehicles are subject to certain recurring patterns particularly when vehicles communicate with the road side units (RSU). In this paper, we design and implement a location-related rate adaptation algorithm (LRRA) which combines the historical information stored in database and current channel conditions to jointly maximize the throughput. We evaluate LRRA with outdoor experiments and ns-3 simulations. The results show that LRRA is superior to most current rate adaptation algorithms.

In the urban transportation system, the unbalanced relationship between taxi demand and the number of running taxis reduces the drivers' income and the levels of passengers' satisfaction. With the help of vehicular global positioning system (GPS) data, the taxi demand distribution of city can be analyzed to provide advice for drivers. A clustering algorithm called Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is suitable for discovering demand hotspots. However, the execution efficiency is still a big challenge when DBSCAN is applied on big databases. In this paper, we propose an improved density-based clustering algorithm called Grid and Kd-tree for DBSCAN (GD-DBSCAN), which integrates partitioning method with kd-tree structure to improve the computational performance of DBSCAN. Furthermore, this algorithm can take advantages of multi cores and shared memory to parallelize related functions. The experiment shows GD- DBSCAN is efficient, it has an improvement of at least 10% in performance compared with DBSCAN.

Indoor localization has been actively researched in recent years due to the increasing demand for location-awareness services. However, to balance localization accuracy and system cost is always a challenge for indoor localization systems. Radio frequency identification (RFID) is a promising technology to achieve both goals, because of its reasonable cost and reliability. In this paper, we propose a novel RFID indoor localization system based on angle of arrival (AoA) and phase of arrival (PoA) methods. This system leverages RFID's two experimental signal diffusion characteristics to estimate AoA. One is that the interrogation zone is constrained in a lobe, and only in this area the tag can be queried. The second is that there exits a stable pattern of received signal strength (RSS) on angle changes. We use the two features to find a general area and to pinpoint the AoA consecutively. This effectively narrows the sampling zone (where signal needs to be sampled), and helps to reduce computational complexity. In addition, we reduce the multipath effect on range estimation by determining the AoA and rotating the reader into the direction of the target. Moreover, we exploit two signals with a slightly different frequency to eliminate the phase ambiguity issue. Our system takes only one reader and achieves mean accuracy of 23 cm. The simplicity and effectiveness of our system make it convenient to be used in practice.

In this study, the authors study the power control for a two‐tier network system which is comprised of a central macrocell and several femtocells. In practice, the communication environment is fairly complex and dynamic which leads to the imperfect channel state information (CSI). To enhance the robustness of the two‐tier network system, the imperfect CSI in both signal links and interference links are considered and the uncertainties of the CSI are uniformly distributed in an ellipsoid uncertainty set. Then, a probability‐constrained optimisation problem is formulated to deal with the uncertainties and protect the quality‐of‐service of all users. A novel method is provided to convert probability constraints into deterministic ones. Based on them, they propose an iterative algorithm and an admission control algorithm to enhance the network efficiency. They also investigate the case when the distributions of the uncertainties of the CSI are unknown. Finally, numerical results are given to illustrate the effectiveness of the authors’ power control scheme.

Traditional compressive sensing (CS) methods assume the data sparsity to be constant over time, which holds well in many long‐term scenarios. However, the authors’ recent study on electrocardiogram (ECG) monitoring reveals that data sparsity varies dramatically for real‐time monitoring systems where the data latency must be bounded, due to limited data collected within the delay bound. The variation of data sparsity makes the reconstruction error (RE) unstable. Furthermore, the variation of wireless channel quality also impacts the reconstruction quality. To accommodate both variations, this study proposes a novel adaptive feedback architecture for real‐time wireless ECG monitoring based on the CS technique, which can bound the REs in the presence of the variations of data sparsity and wireless channel. An experiment testbed has been built to evaluate the performance of the proposed system. The results show that the data latency can be limited to <300 ms and the RE can be controlled to 9%.

Industrial wireless sensor networks (IWSNs) are committed to bring the industry automation into the era of Industry 4.0 by providing the ubiquitous perception to improve the production efficiency. However, the proliferation of wireless devices in industrial applications makes the spectrum sharing in limited industrial, scientific, and medical (ISM) band a challenging problem. In this paper, it is concerned with the intrinsic impact of the evenness of spectrum usage on the spectrum sharing performance in terms of channel accessing probability, spectrum utilization, and fairness of spectrum usage. In order to explore the explicit relationship between the evenness and spectrum sharing performance, a new concept of equilibrium is first defined to represent the achievable best evenness of spectrum usage. Then, a set of rules called local equilibrium-guided autonomous channel switching (LEQ-AutoCS) is devised, with which each accessed sensor autonomously equalizes the local channel occupations within its range of spectrum sensing without overhead on exchanging the sensors' spectrum sensing reports. It is further proved that the equilibrium can be achieved by this concessive manner. Theoretical analysis and experimental results demonstrate that the proposed LEQ-AutoCS rules provide higher utilization and fairness of spectrum usage comparing to the existing spectrum access approaches. Moreover, it is shown that LEQ-AutoCS rules assist the system to reduce the spectrum access delay to 1/2 of CSMA-based systems and 1/50 of TDMA-based systems, respectively.

For data collection in large-scale wireless sensor networks (WSNs), dynamic clustering provides a scalable and energy-efficient solution, which uses cluster head (CH) rotation and cluster range assignment algorithms to balance the energy consumption. Nevertheless, most existing works consider the clustering and routing as two isolated issues, which is harmful to the connectivity and energy efficiency of the network. In this paper, we provide a detailed analysis on the relations between clustering and routing, and then propose a joint clustering and routing (JCR) protocol for reliable and efficient data collection in large-scale WSN. JCR adopts the backoff timer and gradient routing to generate connected and efficient intercluster topology with the constraint of maximum transmission range. The relations between clustering and routing in JCR are further exploited by theoretical and numerical analyses. The results show that the multihop routing in JCR may lead to the unbalanced CH selection. Then, the solution is provided to optimize the network lifetime by considering the gradient of one-hop neighbor nodes in the setting of backoff timer. Theoretical analysis and simulation results prove the connectivity and efficiency of the network topology generated by JCR.

Due to the cognitive limitations of the human operator and lack of complete information about the remote environment, the work performance of such teleoperation systems cannot be guaranteed in most cases. However, some practical tasks conducted by the teleoperation system require high performances, such as tele-surgery needs satisfactory high speed and more precision control results to guarantee patient' health status. To obtain some satisfactory performances, the error constrained control is employed by applying the barrier Lyapunov function (BLF). With the constrained synchronization errors, some high performances, such as, high convergence speed, small overshoot, and an arbitrarily predefined small residual constrained synchronization error can be achieved simultaneously. Nevertheless, like many classical control schemes only the asymptotic/exponential convergence, i.e., the synchronization errors converge to zero as time goes infinity can be achieved with the error constrained control. It is clear that finite time convergence is more desirable. To obtain a finite-time synchronization performance, the terminal sliding mode (TSM)-based finite time control method is developed for teleoperation system with position error constrained in this paper. First, a new nonsingular fast terminal sliding mode (NFTSM) surface with new transformed synchronization errors is proposed. Second, adaptive neural network system is applied for dealing with the system uncertainties and the external disturbances. Third, the BLF is applied to prove the stability and the nonviolation of the synchronization errors constraints. Finally, some comparisons are conducted in simulation and experiment results are also presented to show the effectiveness of the proposed method.

Severe pollution induced by traditional fossil fuels arouses great attention on the usage of plug-in electric vehicles (PEVs) and renewable energy. However, large-scale penetration of PEVs combined with other kinds of appliances tends to cause excessive or even disastrous burden on the power grid, especially during peak hours. This paper focuses on the scheduling of PEVs charging process among different charging stations and each station can be supplied by both renewable energy generators and a distribution network. The distribution network also powers some uncontrollable loads. In order to minimize the on-grid energy cost with local renewable energy and non-ideal storage while avoiding the overload risk of the distribution network, an online algorithm consisting of scheduling the charging of PEVs and energy management of charging stations is developed based on Lyapunov optimization and Lagrange dual decomposition techniques. The algorithm can satisfy the random charging requests from PEVs with provable performance. Simulation results with real data demonstrate that the proposed algorithm can decrease the time-average cost of stations while avoiding overload in the distribution network in the presence of random uncontrollable loads.

In this correspondence paper, an exact method is developed to guarantee asymptotic stability of a bilateral teleoperation system that is subjected for a time-delayed communication. This extends the prior art of searching for the maximum upper bound of time delay. In order to improve the flexibility in controller design and obtain better performance, a fractional-order PDαcontroller is proposed. The exactly stable regions of delays are explored for both integral-order and fractional-order controllers. Compared with conditions in most previous works which are deduced by the Lyapunov-Krasovskii functional and rely on the solution of some linear matrix inequalities, the stability conditions proposed in this paper are established from the frequency domain point of view, and thus, the results are not only sufficient but also necessary. To illustrate accuracy of the conditions, they are simulated on a delayed teleoperation system composed of a pair of robots.

State estimation, which computes the best possible approximation for the system state based on the perceived information transmitted from sensors to the estimators, is vital for control system performance in industrial cyber-physical systems (ICPSs) with the integrated techniques of control, communication and computing. Thus the performance of state estimation relies on the communication reliability. In order to improve the reliability, redundant channels/slots are reserved for the data transmission in industrial wireless techniques, such as WirelessHART. However, the redundancy scheme burdens the increasingly over-crowded ISM spectrum band due to the envisioned emerging ubiquitous industrial wireless monitoring in the architecture of ICPS in the near future. The cognitive radio (CR) technology can intelligently explore the available spectrum opportunities on licensed channels, and it motivates this paper to consider the redundant transmission through the opportunistically available licensed channels to guarantee the transmission reliability for state estimation. Unfortunately, spectrum sensing takes extra energy consumption, thus it is necessary to take into account the energy efficiency for the battery-powered IWSN. Then a CR enabled energy- efficiency maximization problem is formulated by regarding the convergence of state estimation as a constraint of the resource allocation problem. In order to solve the non-convex and mixed integer programming, the Dinkelbach and Lagrangian relaxation techniques are adopted to transform the problem into a convex programming and furthermore reduce the computational complexity. Numerical results demonstrate that the CR technology can significantly release the spectrum for the redundancy design from the ISM band while guarantee the reliability for the effective state estimation.

In this paper, we investigate the resource allocation for orthogonal frequency division multiple access relay networks, where the relay does not have embedded energy supply and needs to first harvest energy from the received signals from the source before forwarding transmission. The relay uses time switching scheme for wireless information and power transfer. We aim to maximize the weighted sum rate under several constraints by varying the source transmission power, the relay transmission power, and the time switching ratio. We formulate the joint resource allocation problem as an optimization problem, which is non-convex. Although it is difficult to solve the non-convex problem, we derive its closed-form solution by exploiting its special structure. We also prove that the closed- form solution is a partial optimum. Finally, simulations verify the proposed closed-form solution is superior to the equal power solution.

In this paper, we consider the relay selection and power allocation problem in an amplify-and-forward relaying network, the objective is to minimize the outage probability given that only mean channel gain information is known. We firstly show that relay selection is important in achieving the global optimal solution in addition to the power allocation. Based on this argument, we then propose to decompose the problem into two parts: relay selection and power allocation. For the relay selection problem, a novel scheme is designed to incrementally select relays according to their ordering of mean channel gain. Then for the given set of relays, the optimal power allocation for the source and relays are obtained by exploiting the special structure of the problem. Simulation results show that the proposed scheme outperforms existing schemes without relay selection, which is also very efficient since the results obtained by this scheme are very close to the optimal results achieved by exhaustive search.

With the popularity of electric vehicles, numerous cities have adopted electric vehicles as a part of taxis system. Compared with traditional fuel taxis, electric taxis (ETs) have to rely on charging stations (CSs) to charge frequently, so that it is possible to use charging behavior to control the actions of ETs. This paper considers the problem of optimizing dispatch of electric taxis and charging stations' prices making. Specifically, based on the electricity price control strategy, electric taxis are guided to suitable charing stations deliberately to match a desired dispatch which could improve service quality or operating efficiency of taxis system. In this paper, a Stackelberg (leader-followers) game model is proposed to describe the optimal dispatch and price-making problems. The existence of Nash equilibrium of this game is analyzed, and a low computational complexity algorithm that is suitable for large scale problem is designed to solve this game. In addition, a practical situation is simulated and the impacts of several parameters are presented.

In this demo, an efficient and reliable wireless link is designed for mobile video surveillance systems. In the link, the idea of cognitive radio is utilized and an adaptive channel switching mechanism is employed to avoid unpredictable interferences. Packets pipelining and accumulative acknowledgement (ACK) is proposed based on the stop-and-wait ARQ protocol to improve the communication efficiency of the link. Moreover, an integration design of the ACK packet is used to piggyback different kinds of control messages. With the efficient and reliable wireless link, a cognitive radio prototype is developed for video transmission between the telerobot and teleoperator. The video information from the telerobot can be transmitted back to the teleoperator quickly and reliably even under channel interferences. The telerobot can also be controlled timely and accurately. A video demo shows the whole story and performance.

This study presents a solution to the interference management scheme and resource allocation strategy for the two‐tier femtocell networks, where the femtocell users (FUEs) share the same frequency band with the existing macrocell users. It is assumed that the FUEs compete for the available spectrum to fulfil their own communication. And the macrocell base station protects itself by pricing the interference from the FUEs, which formulates the Stackelberg game. In this study, two effective pricing schemes, uniform pricing scheme and non‐uniform pricing scheme, combining with admission control are proposed to maximise the revenues and protect the quality of service requirements. The Stackelberg equilibriums for the proposed games are investigated. Besides, a novel distributed interference pricing algorithm is provided for the uniform pricing case. Numerical results show that, in two‐tier femtocell networks with shared spectrum, the proposed pricing schemes are effective in resource allocation and performance protection.

This paper investigates the output consensus problem of heterogeneous discrete-time multiagent systems with individual agents subject to structural uncertainties and different disturbances. A novel distributed control law based on internal reference models is first presented for output consensus of heterogeneous discrete-time multiagent systems without structural uncertainties, where internal reference models embedded in controllers are designed with the objective of reducing communication costs. Then based on the distributed internal reference models and the well-known internal model principle, a distributed control law is further presented for output consensus of heterogeneous discrete-time multiagent systems with structural uncertainties. It is shown in both cases that the consensus trajectory of the internal reference models determines the output trajectories of agents. Finally, numerical simulation results are provided to illustrate the effectiveness of the proposed control schemes.

The asymptotical synchronization problem is investigated for two identical chaotic Lur'e systems with time delays. The sampled-data control method is employed for the system design. A new synchronization condition is proposed in the form of linear matrix inequalities. The error system is shown to be asymptotically stable with the constructed new piecewise differentiable Lyapunov-Krasovskii functional (LKF). Different from the existing work, the new LKF makes full use of the information in the nonlinear part of the system. The obtained stability condition is less conservative than some of the existing ones. A longer sampling period is achieved with the new method. The numerical examples are given and the simulations are performed on Chua's circuit. The results show the superiorities and effectiveness of the proposed control method.

This paper studies the dynamic output feedback tracking control problem for stochastic interconnected time-delay systems with the prescribed performance. The subsystems are in the form of triangular structure. First, we design a reduced-order observer independent of time delay to estimate the unmeasured state variables online instead of the traditional full-order observer. Then, a new state transformation is proposed in consideration of the prescribed performance requirement. Using neural network to approximate the composite unknown nonlinear function, the corresponding decentralized output tracking controller is designed. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of uniformly ultimately boundedness and that both transient-state and steady-state performances are preserved. Finally, a simulation example is given, and the result shows the effectiveness of the proposed control design method.

This paper considers multiaccess control for the uplink in orthogonal frequency division multiple access wireless networks. To avoid the extensive information exchange associated with centralized approaches, we formulate the decentralized access control problem with the contention power constraint as a Bayesian game, mapping time-varying channel state information into contention strategies. By exploiting the problem structure, a strategy where users access the channels with probability one if the observed channel gain is above a predetermined threshold is shown to be optimal. It is also shown that the energy consumption of the threshold strategy will not exceed that of randomized strategies. The game is then equivalently reformulated as one of finding the threshold value in a distributed manner, and the existence and uniqueness of Bayesian Nash equilibria is established. A distributed algorithm based on Lagrange duality is proposed to approach the unique equilibrium, and the algorithm is shown to be globally stable. In a homogeneous system, the performance loss of the proposed scheme is proved to be bounded compared with a centralized channel allocation scheme. Contrary to other proposals, our method allows for heterogeneous channel state information and achieves a comparable throughput with reduced power. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract Cognitive radio (CR) networks comprise a number of spectrum agile nodes with the capability of spectrum detection. Applying techniques of spectrum sharing in CR networks can achieve the efficient utilization of network resources. Usually, data rates of user sessions are time varied because of the dynamic behaviors of CR networks. It is expected that the occurrence of link outage should be avoided and incorporated into the routing design under conditions of increasingly crowded spectrum. This paper proposes an integral framework, which considers these two correlated schemes (resource reconfiguration and robust routing) simultaneously. For that, the resource reconfiguration scheme is developed for the efficient usage of network resources and aims at reducing the occupancy of licensed bands. The link outage, resulting from random session rate, is confined within an acceptable range by using strategy of virtual ‘network portfolio’. A robust optimization approach is proposed to guarantee reliable data transmission among possible interfering links. Both these two items (resource reconfiguration and robust routing) are formulated in a framework of cross‐layer optimization. The evolutionary process of CR network states is provided in simulations, where the results show that the joint design proposal can achieve the least interferences among different licensed users while realizing robust routing. Copyright © 2013 John Wiley & Sons, Ltd.

Spectrum sharing is a promising technique responsible for providing efficient and fair spectrum allocation. Considering the unevenness phenomenon of spectrum usage in industrial wireless networks, a novel spectrum sharing scheme, in the framework of industrial cognitive radio network (ICRN), is proposed in this paper via an autonomous switching technique to equalize the spectrum usage and increase the spectrum access possibility of new requests. The autonomous switching technique borrows the idea from the fact that specific collective motion can be achieved by local actions of individuals in many biological systems. The accessed nodes sense the limited spectrum range around their central frequency and then make the decision of channel switching autonomously. Several sensing report based rules are presented to facilitate the switching decision in order to equalize the channel usage among the sensing range of each node. It is demonstrated that by using these rules the spectrum usage becomes more even, and thus the spectrum utilization and fairness are both improved. Numerical examples are given to show the effectiveness of the proposed spectrum sharing scheme.

Vehicular Ad Hoc Network (VANET) plays a crucial role in enabling important safety applications such as emergency messages dissemination for alleviating traffic accidents and congestion in urban transportation system. Warning information dissemination is of increasing significance in urban highways due to their special topology structure. Hovering Information is a new paradigm for sharing location-aware warning information. It utilizes the vehicles to relay the messages to other vehicles in an anchor area, e.g. the highway entrances. However, one of the challenges lies on how to keep the messages alive at the expense of light network load. In this article, we propose a Hovering-Based Information Dissemination (HBID) approach. In this scheme, a vehicle adjusts the broadcast interval according to the positions and speeds of all neighboring vehicles and then choose the next relay vehicle. Through this, we can reduce the network load and keep the messages in the anchor area. Numerical simulation results demonstrate the effectiveness of the proposed method.

Parameter estimation problem is considered for a class of dual-rate Wiener systems whose input-output data are measured by two different sampling rate. Firstly, a polynomial transformation technique is used to derive a mathematical model for such dual-rate Wiener systems. Then, directly based on the dual-rate sampled data, a dual-rate Wiener systems stochastic gradient algorithm (DRW-SG) is presented. In order to improve the algorithm convergence rate, a dual-rate Wiener systems stochastic gradient algorithm with a forgetting factor algorithm (DRW-FF-SG) is presented. For making full use of the forgetting factor, a dual-rate Wiener systems stochastic gradient algorithm with an increasing forgetting factor algorithm (DRW-IFF-SG) is presented which performs excellently. Finally, an example is provided to test and illustrate the proposed algorithms.

UAV can work in places that are dangerous, or not easy to reach for humans. However, due to active control and operating difficulties, it is still a challenge to develop fully autonomous flight in complex environments. This paper applies a novel heuristic dynamic programming for the UAV heading optimal tracking controller design, using kernel-based heuristic dynamic programming (KHDP). Kernel-based HDP is developed by integrating kernel methods and approximately linear dependence (ALD) analysis with the critic learning of HDP algorithm. Compared with conventional HDP where neural networks are widely used and their features were manually designed, the proposed algorithm can obtain better generalization capability and learning efficiency through applying the sparse kernel machine into the critic learning process of HDP algorithm. Simulation and experimental results of UAV heading optimal tracking control problems demonstrate the effectiveness of the proposed kernel-based HDP algorithm.

As a highly complex multi-input and multi-output system, blast furnace plays an important role in industrial development. Although much research has been done in the past few decades, there still exist many problems, such as the modeling and control problems. In view of these reasons, this paper is concerned with developing a Wiener model to predict the silicon content of blast furnace. Unlike traditional Wiener model, this paper avoids the optimization of high number of model parameters. The Wiener model here is composed of a basis filter filter expansion named Laguerre filter and a linear programming support vector regression (LP-SVR). They are used to represent the linear dynamic component and the nonlinear static element. Take the advantages that Laguerre filter can approximate linear systems with a lower model and order and LP-SVR can achieve a sparse solution, the proposed Wiener model not only improves the prediction accuracy but also reduces the computation complexity. Simulation results show that this Wiener model is suitable for the prediction of blast furnace silicon content.

There are currently several ways of wireless access to support vehicular communication, including vehicular ad-hoc networks (VANETs) and cellular networks (off-the-shelf 3G and LTE). It is necessary to make a seamless handover decision to guarantee quality of service (QoS) of communications for a vehicle moving in the regions covered by more than one access networks. In this paper, we provide a performance guaranteed optimized handover decision algorithm. With this algorithm, the communication of vehicles can handover through heterogeneous wireless access networks not only to reach overall load balance among all access points, but also to maximize the data rate of the whole networks as well as the vehicles' fairness. In addition, in the process of decision making, the data rate of handover vehicles are estimated. Simulations are performed to demonstrate the efficiency of the proposed algorithm.

This study is focused on the multiple relay selection problem in cooperative communications. The objective is to select the minimum set of relays to minimise the spectrum cost while preserving the signal‐to‐noise ratio (SNR) requirement at the destination. A probabilistic constraint is adopted to characterise the SNR outage requirement, which turns out to be intractable in general. As a solution, a tractable bound is derived for the outage probability constraint, with which the multiple relay selection problem can be formulated as a mixed integer optimisation problem with the on‐and‐off power mode at the relays, which is NP‐hard. By utilising the intersections of the proposed bound for different relays, the authors propose an outage probability guaranteed relay selection algorithm which can find the optimal solution with significantly reduced complexity. A heuristic algorithm is also proposed for comparison. Extensive simulation results are provided to show the effectiveness of the proposed schemes.

High accuracy and low cost are challenging requirements for localization in wireless sensor networks (WSNs). The radio interferometric positioning system (RIPS) proposed inaims to meet both requirements at the same time. However, it is vulnerable to channel fading, and suffers from the noise aggravation due to the square operation. In this paper, we propose a dual-tone radio interferometric positioning system (DRIPS) using undersampling techniques, named uDRIPS. Our proposed methodology is immune to flat fading effects, and avoids the amplification of measurement noise by directly undersampling the received signal. Furthermore, the time-of-arrival (TOA) information is extracted from the phases of the received dual-tone signals in the uDRIPS. As a result, it is able to localize an asynchronous target with the help of synchronous anchors (nodes with known positions). Moreover, we investigate the integer ambiguity problem due to phase wrapping, and develop a localization algorithm to estimate the unknowns alternatively. Simulation results corroborate the efficiency of our proposed algorithm.

The master-slave control design problem is considered for the networked teleoperation system with friction and external disturbances. A new finite-time synchronization control method is proposed with the help of adaptive fuzzy approximation. We develop a new nonsingular fast terminal sliding mode (NFTSM) to provide faster convergence and higher precision than the linear hyperplane-sliding mode and the classic terminal-sliding mode (TSM). Then, the adaptive fuzzy-logic system is employed to approximate the system uncertainties, and the corresponding adaptive fuzzy NFTSM controller is designed. By constructing Lyapunov function, the stability and finite-time synchronization performance are proved with the new controller in the presence of system uncertainties and external disturbances. Compared with the traditional teleoperation design method, the new control scheme achieves better transient-state performance and steady-state performance. Finally, the simulations are performed and the comparisons are shown among the proposed method, the P+d method, the PD+d method, the DFF method, and the classic TSM FTSM. The simulation results further demonstrate the effectiveness of the proposed method.

This brief is concerned with the problem of asymptotic stability of neural networks with time-varying delays. The activation functions are monotone nondecreasing with known lower and upper bounds. Novel stability criteria are derived by employing new Lyapunov-Krasovskii functional and the integral inequality. The developed stability criteria have delay dependencies and the results are characterized by linear matrix inequalities. New and less conservative solutions to the global stability problem are provided in terms of feasibility testing. Numerical examples are finally given to demonstrate the effectiveness of the proposed method.

In the conventional dual-hop cooperative communications, relays with imbalanced channel condition to the source or the destination may become the bottleneck of the overall cooperation. Therefore, in this paper, a Three-Stage Relaying (TSR) framework is proposed for clustered networks to extend the dual-hop cooperation to three stages by dividing relays into two groups. The long-haul communication between two groups form a virtual multi-input multi-output (MIMO) link with which the bottleneck between the relay and the source (or the destination) can be removed. We focus on the power allocation problem based on this framework, with the objective of minimizing the outage probability at the destination under the total power constraint. To address the computational complexity, the problem is decomposed into two subproblems, one deals with the power allocation of the source and the first-hop relays, the other deals with the power allocation of the second-hop relays. We design the algorithms for each subproblem by exploiting their special structure, and then develop a master procedure to handle the power allocation across these subproblems. The performance of the proposed scheme is evaluated through simulation study, which shows that the TSR framework achieves significant improvement on the outage probability compared with the dual-hop cooperation scheme, and the power consumption is more fairly distributed across relays.

We address the problem of subchannel and transmission power allocation in orthogonal frequency division multiple access relay networks with an aim to maximize the sum rate and maintain proportional rate fairness among users. Because the formulated problem is a mixed-integer nonlinear optimization problem with an extremely high computational complexity, we propose a low-complexity suboptimal algorithm, which is a two-step separated subchannel and power allocation algorithm. In the first step, subchannels are allocated to each user, whereas in the second step, the optimal power allocation is carried out on the basis of the given subchannel allocation and the nonlinear interval Gauss–Seidel method. Simulation results have demonstrated that the proposed algorithm can achieve a good trade-off between the efficiency and the fairness compared with two other existing relevant algorithms. In particular, the proposed algorithm can always achieve 100% fairness under various conditions. Copyright © 2012 John Wiley & Sons, Ltd.

Traffic information is important for drivers and the traffic monitoring center (TMC) to avoid traffic congestions. VANET based systems use probe vehicles (PVs) to sense and upload the traffic information. However, due to the uneven distribution of the PVs, only a small part of the real-time traffic data can be gathered. Also, it is not efficient to estimate the traffic conditions at TMC and then publish to vehicles. We consider a service oriented algorithm for both PVs and TMC to estimate the missing traffic conditions, and propose a Matrix Completion (MC) based algorithm, HaTTEM, to estimate the missing traffic data in the traffic condition matrix (TCM). Temporal continuity and the bounds of the traffic data are introduced to reduce the error of estimation. To reduce the computational time, we propose the L0.5-based Iterative MC Algorithm so that the TCM can be fast estimated by the PVs and TMC. The simulations with real world data show the effectiveness and efficiency of HaTTEM even when the PVs can only sense 20% of the traffic data.

Despite the fact that wireless sensor networks (WSNs) have been applied to structural health monitoring (SHM) systems of civil infrastructure, the reliable data transmission is very challenging for structures far away from data center due to long transmission distance. In this paper, we present a novel architecture that high-speed train takes the role of a mobile sink to assist the data collection. In order to guarantee the recovery of monitoring data with high probability under unreliable transmission, a new data packing and reconstruction method called PackTrix is then proposed with Matrix Completion technique. It is proved that the proposed PackTrix algorithm is one of the optimal packing methods rendering the Matrix Completion technique effectiveness for data reconstruction. Theoretical analyses as well as simulations with real world data show that PackTrix can recover lost data with low relative error based on only very small percents of the original data.

In the conventional dual-hop cooperative communications, the relays with imbalanced channels to the source and the destination become the bottleneck of the overall cooperation. In this paper, a Three-Stage Relaying (TSR) scheme is presented to extend the dual-hop cooperation to three stages by dividing relays into two groups. The relay-to-relay cooperation is introduced with which the bottleneck link between the relay and the source (or the destination) can be efficiently broken. We focus on the power allocation based on this framework, with the objective of minimizing the outage probability at the destination under the total power constraint. To address the computational complexity, the problem is decomposed into two subproblems, one deals with the power allocation of the source and the first-hop relays, the other deals with the power allocation of the second-hop relays. We design the algorithms for each subproblems by exploiting the special structure of the problems, and develop a master procedure to handle the power allocation across these subproblems. The performance of the proposed scheme is evaluated through simulation study, which shows that the TSR framework achieves significant improvement on the outage probability compared with the dual-hop protocol, and the power consumption is more balanced across relays.

Dynamic spectrum auction offers the flexibility and capability for bidders to request and acquire unoccupied channels from spectrum license holders. Compared with the conventional auction, spectrum auction allows various buyers to utilize the same channel simultaneously based on their locations, which is denoted as spectrum reusability. In this paper, we consider a novel kind of attack, which could compromise location privacy of bidders by observing the bid items as well as bid price. To thwart this attack, we introduce a new Location Privacy Preserving Dynamic Spectrum Auction (LPPA) scheme which consists of two components: Privacy Preserving Bid Submission protocol (PPBS) and Private Spectrum Distribution protocol (PSD). Based on the prefix membership verification scheme, PPBS allows the auctioneer to construct the conflict relationship between different users and obtain the maximum value of bids on various channels without leaking users' location information. Furthermore, PSD is proposed to efficiently distribute the spectrum among bidders and securely charge the winners with the help of periodically available TTP (Trusted Third Party). To demonstrate the effectivenss of the proposed scheme, we implement our attack and scheme on data extracted from Google Earth Coverage Maps released by FCC. The experiment results show the efficacy and efficiency of our approach.

Collaborative spectrum sensing has been recognized as a promising approach to improve the sensing performance via exploiting the spatial diversity of the secondary users. In this study, a new selfishness issue is identified, that selfish users sense no spectrum in collaborative sensing. For easier presentation, it's denoted as entropy selfishness. This selfish behavior is difficult to distinguish, making existing detection based incentive schemes fail to work. To thwart entropy selfishness in distributed collaborative sensing, we propose YouSense, a One-Time Pad (OTP) based incentive design that could naturally isolate entropy selfish users from the honest users without selfish node detection. The basic idea of YouSense is to construct a trapdoor onetime pad for each sensing report by combining the original report and a random key. Such a one-time pad based encryption could prevent entropy selfish users from accessing the original sensing report while enabling the honest users to recover the report. Different from traditional cryptography based OTP which requires the key delivery, YouSense allows an honest user to recover the pad (or key) by exploiting a unique characteristic of collaborative sensing that different secondary users share some common observations on the same radio spectrum. We further extend YouSense to improve the recovery successful rate by reducing the cardinality of set of the possible pads. By extensive USRP based experiments, we show that YouSense can successfully thwart entropy selfishness with low system overhead.

An Orthogonal Frequency Division Multiple Access (OFDMA) downlink system with secure transmissions and delay constraints is investigated, in which a base station (BS) transmits both open data with delay limitations and private data with secrecy demands to each user. The time-varying channel is modeled as slow-fading and all channel state information (CSI) is assumed to be known to BS. An online cross-layer scheduling algorithm composed of flow control and resource allocation is proposed in this paper to maximize the downlink throughput with delay and power constraint. Furthermore, the algorithm makes decisions on current CSI instead of channel statistics. In addition, virtual queues of delay and power are constructed to track them so that those time-average constraints are fulfilled. In the end, it is proven by means of Lyapunov optimization technique that our algorithms will obtain a performance which can be extremely close to optimality and reduce operation complexity significantly.

This paper is concerned with the problem of filter design for target tracking over sensor networks. Different from most existing works on sensor networks, we consider the heterogeneous sensor networks with two types of sensors different on processing abilities (denoted as type-I and type-II sensors, respectively). However, questions of how to deal with the heterogeneity of sensors and how to design a filter for target tracking over such kind of networks remain largely unexplored.We propose in this paper a novel distributed consensus filter to solve the target tracking problem. Two criteria, namely, unbiasedness and optimality, are imposed for the filter design. The so-called sequential design scheme is then presented to tackle the heterogeneity of sensors. The minimum principle of Pontryagin is adopted for type-I sensors to optimize the estimation errors. As for type-II sensors, the Lagrange multiplier method coupled with the generalized inverse of matrices is then used for filter optimization. Furthermore, it is proven that convergence property is guaranteed for the proposed consensus filter in the presence of process and measurement noise. Simulation results have validated the performance of the proposed filter. It is also demonstrated that the heterogeneous sensor networks with the proposed filter outperform the homogenous counterparts in light of reduction in the network cost, with slight degradation of estimation performance.

Distributed collaborative spectrum sensing is a promising method to improve the precision and efficiency of primary user detection in cognitive radio networks. Despite its performance advantages, it introduces new security issues that malicious or selfish nodes may manipulate false sensing data to degrade or even covert the sensing result of the whole network. Existing research often utilizes a threshold to distinguish honest users and malicious ones. However, determining such a threshold is difficult due to the dynamic characteristic of cognitive radio networks, and it is likely to misjudge an honest node with a relatively large deviation to be malicious. In this paper, we propose an Adaptive Deviation-tolerant Secure Scheme (ADS) for distributed collaborative spectrum sensing, which aims to mitigate the misbehaviors of inside malicious nodes and, at the same time, tolerant the large deviation introduced by honest users. ADS achieves the trade off of sensing security and deviation tolerance by assigning a dynamic weight to each sensing node and utilizes an adaptive threshold to minimize the negative effect on honest users. We evaluate the performance of the scheme through both analytical and simulation based study.

In this paper, we study the power allocation strategy in a drive-thru scenario, where an access point (AP) is installed along the highway to provide Internet services to vehicles within its coverage range. We consider single-hop vehicle-to-roadside (V2R) communications for a vehicle that aims to upload data within the coverage range of the AP, where the bandwidth allocated to it is time-varying, and the size of the data is known upon it enters the area. The data bits received over a time slot are correctly received if the instantaneous channel capacity rtis greater than or equal to a threshold Rt, and corrupted otherwise. The vehicle has to pay an amount for data transmission according to the power consumption at each time slot whether the data bits are correctly received or not. The target is to complete the transmission of the traffic demand volume with the minimal cost. First, we consider the optimal power allocation strategy with a single AP and random vehicular traffic arrivals. We formulate it as a finite-horizon sequential power allocation problem. Then we solve the problem using dynamic programming and find the optimal power allocation strategy. The proof of the existing of the optimal value of the cost-to-go function is given after. Simulation results show that our proposed strategy achieves less cost than another heuristic strategy. The impacts of different traffic demand volumes, traffic densities, and outage probabilities on total cost are also analyzed.

In this paper, we study the relay selection problem for cooperative communications in aircraft approach. We propose to incorporate the airborne trace prediction scheme with the Rician fading channel model to provide a good estimation of channel condition for aircrafts with high speed mobility. Based on this model, the relay selection problem is formulated and transformed to a nonlinear integer programming (NLIP) problem, and two heuristic algorithms are proposed since this problem is NP-hard. We provide simulation results to show that the proposed scheme can guarantee the reliability of transmission with high probability.

Collaborative spectrum sensing has been proposed recently to improve the sensing performance in Cognitive Radio networks. However, cooperative sensing will also introduce extra cost to the collaborator, such as the cooperative time and energy consumption. In reality, whether the rational secondary users have incentive to join the collaboration depends upon whether the benefit of the collaboration could outweigh the cost. In this paper, we model it as the Cooperative Spectrum Sensing Game (CSSG). In this game, every secondary user could choose to collaborate or not in each time slot, and the payoff is measured in terms of data throughput. Since the effectiveness of collaboration is proportional to the number of the collaborators, secondary users' decisions are based on how many users will choose to collaborate. Thus, CSSG could be modeled as the classic game: the Stag Hunt Game. In addition, to avoid the cooperation failure, we propose Cooperative Communication Incentive Scheme (CCIS) to enhance the collaborative sensing. At last, the numerical analysis about CSSG as well as the proposed scheme CCIS is given.

Collaborative spectrum sensing has been regarded as a promising approach to enable secondary users to detect primary users by exploiting spatial diversity. In this paper, we consider a converse question: could space diversity be exploited by a malicious entity, e.g., an external attacker or an untrusted Fusion Center (FC), to achieve involuntary geolocation of a secondary user by linking his location-dependent sensing report to his physical position. We answer this question by identifying a new security threat in collaborative sensing from testbed implementation, and it is shown that the attackers could geo-locate a secondary user from its sensing report with a successful rate of above 90% even in the presence of data aggregation. We then introduce a novel location privacy definition to quantify the location privacy leaking in collaborative sensing. We propose a Privacy Preserving collaborative Spectrum Sensing (PPSS) scheme, which includes two primitive protocols: Privacy Preserving Sensing Report Aggregation protocol (PPSRA) and Distributed Dummy Report Injection Protocol (DDRI). Specifically, PPSRA scheme utilizes applied cryptographic techniques to allow the FC to obtain the aggregated result from various secondary users without learning each individual's values while DDRI algorithm can provide differential location privacy for secondary users by introducing a novel sensing data randomization technique. We implement and evaluate the PPSS scheme in a real-world testbed. The evaluation results show that PPSS can significantly improve the secondary user's location privacy with a reasonable security overhead in collaborative sensing.

In this paper, we study the relay selection problem in cooperative communications in aircraft approach, whereby aircrafts in the holding stack can be selected as relays to enhance the ground-to-air communications between the control tower and the approaching aircraft. We derive the upper bound of the outage probability in cooperative communications under three different channel conditions: Rayleigh, Rician and Nakagami-m. A relay selection algorithm OPRS is proposed accordingly that attempts to find the minimum number of relays to meet the outage probability requirement. We then consider the case that the bound of the channel condition is given but the distribution is unknown. In this case, the relay selection problem is formulated as a robust optimization problem. We provide simulation results to show the effectiveness of the proposed schemes.