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.

This article focuses on nonconvex federated composite optimization (FCO) problem, where the loss function is nonconvex and contains a nonsmooth regularizer. To resolve this problem, we propose FedRREF, a novel federated learning algorithm that integrates error feedback (EF) with the efficient random reshuffling (RR) technique, resulting in lower computation and communication costs. To the best of our knowledge, FedRREF is the first algorithm to consider RR and biased compression simultaneously in federated learning, especially in nonsmooth and nonconvex settings, and it is shown to have a $\mathcal {O}(1/\sqrt {T})$ convergence rate, where $T$ is the number of communication rounds. Finally, the numerical experiments illustrate the validity of the proposed algorithm.

Distributed computing faces a persistent multi agent trust dilemma. In the computation process, participants may maliciously attack the system for personal gain by providing false data. Blockchain provides a possible solution for this problem with its immutability and multi-party consensus. However, existing blockchain data throughput has long been queried owing to its exorbitant time and energy costs by consensus mechanisms. This paper proposes a light blockchain structure in distributed computing scenarios. A No-Proof consensus (NPC) mechanism is designed for distributed computing problems with no extra proving process such as Proof-of-Work or Proof-of-Stake. This consensus mechanism notices that the distributed computing result has proven to be valid in the computation process automatically, which does not need to be verified again in the consensus mechanism. Further, the single-threaded data processing ability of the blockchain structure certainly leads to low efficiency when applied to distributed computation problems. An NPC-based blockchain is constructed in this paper to solve this problem. In this structure, the distributed computing is done off chain, and an oracle is designed to upload the computing results to the blockchain asynchronously. Upon the contribution in this paper, a distributed energy trading model is provided as a case study to verify the superiority of the designed blockchain in contrast with other similar structures.

This article investigates the model predictive control (MPC) problem for discrete-time uncertain Markovian jump systems (MJSs) via an improved optimizing prediction dynamics (IOPD) approach. To address immeasurable system states, an observer-based output feedback controller is designed within the MPC framework, accompanied by a novel dual-mode control strategy that optimizes the tradeoff among initial feasibility, control performance, and computational efficiency. The first control mode, associated with the terminal constraint set, is derived from an off-line infinite-horizon optimization problem. The second mode, which steers the system state toward the terminal set within a prescribed time, is determined via online optimization, where dynamically structured perturbations expand the initial feasible region of system state, reduce computational burden, and improve closed-loop performance. The challenges posed by immeasurable states and nonlinear variable coupling are systematically resolved using matrix decomposition and parameter transformation techniques. Sufficient conditions are established to guarantee the recursive feasibility of the IOPD-MPC algorithm and the mean-square stability of the closed-loop system. Numerical simulations on a macroeconomic system validate the efficacy of the proposed method.

Ensuring end-to-end delays meet deadlines is a key challenge in Industrial Internet of Things (IIoT), as it directly affects control performance. To reduce end-to-end delays, traditional methods focus on message scheduling under the Time Division Multiple Access (TDMA) protocol. However, with the increasing adoption of complex control algorithms in industry, neglecting task scheduling on the controller can lead to missed deadlines for control applications. This paper investigates the co-scheduling problem of messages and tasks, aiming to satisfy the end-to-end deadline constraints of all control applications in IIoT. Since the preemptive controller introduces an unknown number of task scheduling decision variables, the Early Deadline First (EDF) strategy is employed to generate task schedules. To generate message schedules aligned with task schedules, we reformulate the co-scheduling problem, making it solvable by numerical solvers. Moreover, we propose a heuristic algorithm, SECDS, for efficient scheduling in large-scale IIoT. Simulation results show the superior applicability and performance of the proposed methods compared to baseline algorithms.

With the deepening application of cyber-physical systems in Industry 4.0, industrial edge computing faces significant challenges in coordinating sensing, computing, and control tasks with heterogeneous resource requirements and strict real-time constraints. This paper investigates the real-time scheduling problem for container-based deployment of such tasks, which are modeled as precise/imprecise real-time tasks to support computational elasticity. To enhance energy efficiency and quality-of-service guarantees, while ensuring real-time requirements, a hierarchical real-time scheduling model that integrates multi-level task-container-node allocation is proposed. To overcome computational bottlenecks of existing centralized solvers, a bilevel distributed optimization framework is designed: the upper level solves task-container allocation and container resource interface decisions through primal decomposition, while the lower level reformulates original problem into convex optimization to derive task computation ratios and CPU frequency, achieving optimal solutions through iterative coordination. Hardware-In-the-loop simulations, implemented using the Ray distributed computing framework validate the effectiveness of the proposed method.

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.

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.

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.

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.

This article is concerned with the co-design of privacy-preserving and resilient consensus protocol for a class of multiagent networks (MANs), where the information exchanges over communication networks among the agents suffer from eavesdropping and Sybil attacks. First, we introduce a new attack model in which an adversarial agent could launch a Sybil attack, generating a large number of spurious entities in the network, thereby gaining disproportionate influence. In this communication framework, a whole-process privacy-preserving mechanism is designed that is capable of protecting both initial and current states of agents. Then, instead of existing methods requiring identifying and mitigating Sybil nodes, a degree-based mean-subsequence-reduced (D-MSR) resilient strategy is implemented, showcasing its significant properties: 1) ensuring the effectiveness of aforementioned designed privacy protection strategy; 2) allowing the network to contain Sybil nodes without elimination; and 3) reaching consensus among the normal agents. Finally, several numerical simulations are provided to validate the effectiveness of the proposed results.

Optimization problems arise in various domains, ranging from power systems to resource allocation and large network management. An effective solution to these problems in a distributed manner has become a crucial research area due to the increasing scale and complexity of modern systems. In this article, we propose a novel local projection global tracking (LPGT) decentralized algorithm based on the Alternating Direction Method of Multipliers for general optimization problems. Unlike existing distributed methods that require problem-specific adaptations or centralized coordination, LPGT provides tailored solutions for handling generic local equality and inequality constraints, as well as global coupled equality and inequality constraints. Moreover, a projection-based analytical scheme is designed to handle generic local equality and inequality constraints without iterative subproblem solvers, and a fully decentralized deviation tracking mechanism is constructed to enforce both global coupled equalities and inequalities constraints via agent communications, eliminating the need for a central coordinator. Case studies for a local energy trading model are proposed to verify the feasibility and applicability of the algorithm.

This paper presents a compound scaling dynamic network architecture designed for real-time surface defect detection in industrial production lines, addressing the challenges posed by varying time constraints and computational resource allocation. We develope a lightweight UNet-based dynamic network using a ResNet-18 backbone, incorporating optional convolutional blocks at the end of each network stage to simultaneously scale network depth and width. To efficiently train this dynamic network, an incremental training strategy is proposed to minimize interference between different paths. The network is trained and tested on the Severstal Steel Defect Detection dataset, demonstrating that the compound scaling approach significantly improves accuracy at the same Floating Point Operations (FLOPs) cost compared to single-dimension scaling methods. Additionally, a scheduling algorithm is designed to dynamically allocate computational resources and adjust network levels, ensuring real-time requirements are met in multitask environments. Experimental results validate the robustness of this scheduling algorithm in handling both periodic and aperiodic tasks, as well as a increasing number of tasks. Under varying time constraints, our compound scaling approach shows higher defect detection accuracy by at most 4.0% and 4.6% respectively, compared to network depth-only scaling method and resolution-only scaling method, while mamtaining a quite low deadline miss rate under 0.3%.

In industrial sites, multiple real-time control systems often share computing resources. However, burst computing tasks may lead to the random dropping of control computing subtasks, resulting in control failures and potential hazards. To address this issue, we propose an energy-efficient safety-aware task scheduling scheme based on mixed-integer programming. When burst computing tasks are triggered, this scheduling scheme adaptively releases resources from low-criticality control computing sub tasks and adjusts the processor speed based on the dynamic voltage and frequency scaling technology (DVFS), ensuring the timely completion of burst computing tasks and keeping the control system state deviation within a safe threshold. To achieve the scheduling scheme, we propose an efficient algorithm called EESASA. Simulation results show that EESASA can minimize the overall cost of control and processor energy while ensuring the safety of the control system and the timely completion of burst computing tasks.

As very important controllers in automated facto-ries, the correctness and safety of Programmable Logic Controller (PLC) and their programs determine the stability of the production process. Structured Text (ST) is one of the most widely used PLC programming languages. However, there are not enough formal semantics and different venders may have their own implementations. In this work, a visual executable semantic model ST-Petri for ST has been proposed. The model uses Petri nets to formally represent ST semantics while using graphs to visualise program control flow. To validate the correctness of the semantic model, we use programs from Github and mutations of existing source programs as a test set to execute and compare the results with the open source OpenPLC platform. Experimental results show that ST-Petri has significant advantages in terms of compilation pass rate and error indication as a semantic model and a ST program compiler.

In the era of Industry 4.0, the execution of intelligent industrial applications relies on collaborations among multi-type devices, such as sensing, computing, and control devices. Al-though a universal information model allows for integrated device management, the model-building complexity rises dramatically with increasing system functions. To address this challenge, an integrated information model with hierarchical architecture is designed. By decoupling device functions, a separated rule is adopted for model construction such that the information model of each device only contains function-related nodes and common attributes for describing device features, reducing the complexity of information model construction. Then, an aggregation server is constructed, enabling application-oriented views. Based on this, efficient management is facilitated by displaying management information of corresponding applications in the user interface, reducing the complexity of integration of sensing, computing, and control devices. Finally, experimental results demonstrate the effectiveness of the proposed scheme. Compared to the standard OPC UA information model, in the aggregation server, the number of model nodes of each type of devices in the application-oriented view is reduced by 79.8% on average.

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.

The wireless edge-cloud industrial Internet of Things (IIoT) has many advantages over the legacy cloud-wired IIoT. In this paper, we propose WiSDEC-Net, a targeted wireless edge-cloud IIoT environment for remote control. A prioritized Universal Max-weight algorithm is implemented to provide basic optimal throughput. Fault-resilient mechanisms, including a fault protection strategy and a firm transmission strategy, are designed respectively for the control channel and the application channel in an industrial environment. The proposed WiSDEC-Net is evaluated on a simulated edge-cloud IIoT structure with remote control. Experimental results, compared to other baseline schemes including the model without a fault protection strategy and spanning tree protocol, show superior robustness and higher throughput of WiSDEC-Net in the presence of common wireless link faults including single-link failure and packet loss.

Segment Anything Model (SAM), an advanced universal image segmentation model trained on an expansive visual dataset, has set a new benchmark in image segmentation and computer vision. However, it faced challenges when it came to distinguishing between shadows and their backgrounds. To address this, we proposed ShadClips, which consists of SAM-optimizer and SONet. It has dramatically enhanced SAM’s ability to segment shadow images, differentiating between the background and both soft and hard shadows adeptly. Due to its dependence on pixel point inputs, the SAM-Optimizer interface could do better. This method presents challenges, especially when dealing with long, extended shadows. To make the user experience more intuitive and effective, we incorporated the capabilities of CLIPs. Therefore, simple text descriptions like “A photo of a shadow” can be used to guide the SAM-Optimizer, allowing it to select the most relevant shadow mask from SAM’s comprehensive category list. Meanwhile, we introduce SONet to shadow removal. A large number of experiments on ISTD/SRD prove that the proposed method is effective and satisfactory. The source code of the ShadClips can be accessed from https://github.com/zhangbaijin/SAM-helps-Shadow .

The vigorous development of electric vehicles (EVs) is an important means of reducing carbon emissions and mitigating environmental problems such as the greenhouse effect. Battery swapping stations (BSSs) can both provide battery swapping services for large-scale EVs and charge batteries centrally. As the supply of fully charged batteries in the BSS shrinks, it becomes necessary to schedule the charging of the depleted batteries rapidly that users have swapped for fully-charged ones. The charging schedule for depleted batteries must be made without knowledge of future battery arrivals. In this context, this paper develops a mathematical model for online charging scheduling of BSSs, formulates the charging strategy as a two-dimensional rectangle packing problem, and quickly calculates the scheduling arrangement of batteries by partitioning the remaining available capacity of a BSS. Since there are limited battery types within the BSS which can provide battery replacement services, this paper supplements the proposed model with known battery types, which improves the utilization of the available capacity of BSSs. Finally, numerical results verify the effectiveness of the proposed model.Note to Practitioners—Electric vehicles (EVs) are becoming an alternative way to reduce carbon emissions in transportation systems. Herein, the optimal battery charging problem is the core problem when it comes to dispatching a huge number of EVs. Up to now, battery-swapping is widely used for EVs due to its simple, convenient way. Furthermore, a business model for the battery swapping stations (BSSs) is brought up, where EV users send their depleted batteries to the BSS and the BSS provides the users with a fully charged replacement battery from its warehouse, which only takes a few minutes. Since the maximum charging power of the BSS is limited by the capacity of the transformer connecting the BSS to the power grid, the BSS will adopt an optimal charging schedule that maximizes the charging benefit for large quantities of depleted batteries in the warehouse. However, the challenge is that the charging schedule for depleted batteries must be made without knowledge of future battery arrivals because the EV behaviors are difficult to predict. To address this problem, this paper developed an online charging scheduling algorithm, which formulates the charging strategy as a two-dimensional rectangle packing problem. The proposed method can provide battery replacement services in real-time and solve quickly without any information about incoming depleted EV batteries. The proposed model and method have been tested on the system with different numbers of batteries to show the effectiveness. Besides, the online two-dimensional rectangle packing problem can provide an online decision for BSSs.

Energy storage (ES), with its flexible characteristics, has been gaining attention in recent years. The ES planning problem is highly significant to establishing better utilization of ES in power systems, but different market regulations impact the ES planning strategy. Thus, this paper proposes a novel ES capacity planning model under the joint capacity and energy markets, which aims to minimize the total cost for power consumers. The great challenge is that the ES planning model has a large number of time periods, which significantly increases the problem dimensionality. In order to alleviate the computational burden, a fully parallel algorithm is proposed to temporally decompose the original problem into a series of small sub-problems, which can be solved in parallel. Moreover, we find that the corresponding analytical solutions to the sub-problems remarkably accelerate the calculation speed while ensuring accurate results. Finally, numerical results verify the effectiveness of the proposed model. Note to Practitioners—Energy storage (ES) has become more and more essential to guaranteeing power balance in power and energy systems by shifting peak loads to valley loads. However, investors may face challenges to ES capacity planning due to the lack of business models. To address this challenge, price tariffs should be carefully investigated. In the practical power system, the market price should consider both the energy price and capacity price for industries and big companies. In the energy market, investors can gain a profit by selling energy at the peak load (high price) and buying energy in the valley (low price). It should be noted that the energy market cannot recover the ES investment cost, but investors can, in fact, reduce their capacity cost since ES can reduce the peak load. Consequently, we have designed a new business model for ES planning under joint capacity and energy markets to analyze the profits via the two market regulations. The computational burden is another challenge for the proposed multi-period convex optimization model. The model must consider a long-term simulation, potentially containing thousands of time periods, which can be difficult to solve. In order to alleviate the computational burden resulting from the long-term market simulation, we further propose a fully parallel algorithm to solve the proposed business model for ES quickly. To sum up, the proposed model and method have been tested on a practical company with a practical price tariff in China to show their effectiveness.

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.

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.

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.

In this paper, we propose an Effective and lightweight Transformer for image shadow detection and removal named SpA-Former to recover a shadow-free image from a single shaded image. In contrast to conventional methods that require two stages for shadow detection and then shadow removal, the SpA-Former is a one-stage network capable of learning the mapping function between shadows and no shadows, and does not require a separate shadow detection. SpA-Former is composed of Transformer encoder and CNN decoder, where the CNN decoder contains the GAN network. In the Transformer encoding stage, Gated Feed-Forward Network(GFFN) is devised to control the information flow. In the CNN decoding stage, Two-wheel RNN joint spatial attention(TWRNN) and Fourier transform residual block (FTR) are designed to achieve satisfactory results in shadow removal. The combination of Transformer and CNN is able to feed global features from the Vision Transformer encoder into CNN to enhance the global perception of CNN branches, taking into account the complementarity of local features and the global. The SpA-Former's inference speed is 0.0459s, and the final Parameters and FLOPS are only 0.47MB and 15G, achieving the current lightweight of image shadow removal. The source code of MemoryNet can be obtained from https://github.com/zhangbaijin/SpA-Former-shadow-removal

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.

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.

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.

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.

Industrial cyber-physical systems (ICPS) refer to an emerging generation of intelligent systems, where distributed data acquisition is of great importance and is influenced by data transmission. In the improvement of the overall performance of sensing accuracy and energy efficiency, sensing and transmission are tightly coupled. Due to the unknown transmission channel states in the harsh industrial field environment, intelligently performing sensor scheduling for distributed sensing is challenging. In this paper, edge computing technology is utilized to enhance the level of intelligence at the edge side and deploy advanced scheduling algorithms. We propose a learning-based distributed edge sensing-transmission co-design (LEST) algorithm under the coordination of the sensors and the edge computing unit (ECU). Deep reinforcement learning is applied to perform real-time sensor scheduling under unknown channel states. The conditions for the existence of feasible scheduling policies are analyzed. The proposed algorithm is applied to estimate the slab temperature in the hot rolling process, which is a typical ICPS. The simulation results demonstrate that the overall performance of LEST is better than other suboptimal algorithms.

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.

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.

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.

Integrated energy systems become more and more important to realize the energy complementary property. Micro-integrated energy system, served as the terminal integrated energy system, will have the electricity delivered directly to the local customers by energy hubs (EHs). Here, the data and information of the EHs during the operation are confidential and should be kept by each owner. Therefore, this article designs a dual-decomposition-based distributed algorithm to address this problem, where the optimal consensus problem is used for the dual problem to update the multipliers. The primary and dual problems are alternatively solved until the Karush-Kuhn-Tucher condition is satisfied. For the proposed distributed algorithm, the feasibility can be strictly guaranteed during the iteration process. Moreover, theorems and lemmas are proved for the linear convergence rate. The numerical results verify the effectiveness of the proposed algorithm.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

In this paper, we consider the distributed parameter estimation problem over sensor networks in the presence of quantized data and directed communication links. We propose a two-stage algorithm aiming at achieving the centralized sample mean estimate in a distributed manner. The running average technique is utilized in the proposed algorithm to smear out the randomness caused by the probabilistic quantization scheme. It is shown that the centralized estimate can be achieved in the mean square sense, which is not observed in the conventional consensus algorithms. Simulation results are presented to illustrate the effectiveness of the proposed algorithm and highlight the improvements by using running average technique.

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.

In this paper, we deal with distributed estimation using consensus algorithms for heterogenous wireless sensor networks (WSNs). To accommodate with the heterogeneity, we introduce a novel distributed estimator to track the weighted average of the input signals. Different from existing models, we consider a more practical scenario to take account of hierarchical processing abilities of different sensors: type-I sensors with high processing ability and type-II senors with low processing ability for distributed sensor fusion in WSNs. We investigate the properties of our model and illustrate the feasibility of the proposed estimator via a case study where we use the estimator to track the weighted average of a noisy time-varying signal based on the sensors' noisy and distorted measurements. Convergence analysis in this scenario is given as well as the effect of network topology and estimator parameters are also studied. Simulation results are provided to demonstrate the performance and effectiveness of the proposed estimator.