Driven by the new generation of information technology, modern manufacturing is shifting from traditional rigid production to flexible, customized models. This transformation imposes higher demands on systems' dynamic adaptability and real-time responsiveness. The emergence of Edge Computing (EC) and the Age of Information (AoI) offers promising solutions to these challenges. Significant progress has been made in areas such as edge resource allocation and information update strategies, contributing to enhanced system responsiveness. However, most existing studies assume task independence, which limits their applicability in complex scenarios (such as high-end manufacturing), where task dependency is dynamic and ubiquitous. In addition, ensuring end-to-end integration of sensing, transmission, computation, and control (STCC) remains a major challenge. To address this gap, this study proposes a collaborative framework focusing on sensing, transmission, computation, and control (STCC), centering around task chains. For the first time, it introduces and defines the Dependency-Aware Age of Information (DAoI) metric to quantify inter-task dependencies and the effects of delay propagation, thereby enabling a more accurate assessment of data timeliness. Additionally, an optimal controller using the Linear Quadratic Regulator (LQR) is designed. Our model optimizes control performance and energy consumption through a Markov Decision Process (MDP). The proposed Dependency-Aware Heterogeneous Task and Resource Co-scheduling (MAPPO-DHTCO) algorithm efficiently manages task dependencies and resource allocation. Experimental results show that this method has significantly improved performance compared to the benchmark method.

The Flexible Flow Shop Scheduling Problem (FFSP) involves scheduling jobs across multiple machines with flexible operation sequences. As an NP-hard problem with discrete variables and complex constraints, FFSP presents significant challenges in production scheduling. To address this, a mathematical model is developed with the goal of minimizing completion time, and an MDP model is formulated for Deep Reinforcement Learning (DRL) to identify optimal scheduling policies. A Cross-Entropy Firefly Algorithm-assisted Deep Reinforcement Learning (CEFA-DRL) framework is proposed to handle the discrete nature and NP-hardness of FFSP. Compared to conventional DRL algorithms, CEFA-DRL reduces the agent’s reliance on gradient information, making it more effective for problems with highly discrete solution spaces. The algorithm combines the Cross-Entropy (CE) method and Firefly Algorithm (FA) for co-evolution, assisting DRL in exploring the strategy space. An innovative sampling strategy is introduced to balance exploration and exploitation during training. The CEFA-DRL framework optimizes the Actor-Critic architecture through evolutionary reinforcement learning, improving exploration in solution spaces with limited gradient information, which is particularly suitable for scheduling problems. Cross-environment comparative experiments show that CEFA-DRL reduces makespan by approximately 29%–70% and variance by 7%–17% compared to other DRL and metaheuristic methods.

Meeting the deterministic demands of industrial tasks can be quite challenging due to the diversity of devices and the unclear relationship between tasks and platforms in industrial edge computing scenarios. To tackle this issue, this study introduces an entropy-weighted scheduling method grounded in resource quantification. First, we scrutinized the affinity challenge when tasks operate across different platforms and broadened the scope of scheduling evaluation criteria within existing real-time systems. This expansion was accomplished by examining the alignment between various task attributes and platform characteristics through resource quantification. Subsequently, we employed the entropy weight method to handle the information entropy of all scheduling evaluation criteria and calculated the weighted sums to allocate the optimal scheduling device for each task. Ultimately, the entropy-weighted scheduling algorithm, which relies on resource quantification, was formulated to assess the algorithm’s scheduling performance under various parameter configurations. The experimental analysis indicated that the scheduling method based on resource quantification could effectively optimize the resource demand relationship between tasks and platforms, and the scheduling success rate of the proposed algorithm was 5.1%, 7.7%, and 34.5% higher than those of multitarget tracking sensor scheduling algorithm, D-Quantify, and RRA algorithms, respectively.

Blockchain-based vehicular edge computing (VEC) is regarded as a promising computing paradigm that can enhance the computing capabilities of mobile vehicles while ensuring security during task offloading. However, the blockchain consensus for secure task offloading inevitably increases the communication and computation resource consumption. More importantly, the frequent handover among roadside units during the fast movement of vehicles also raises the communication cost for blockchain consensus. To address these issues, this article proposes intelligent secure task offloading and caching (ISTOC) scheme for VEC networks. Specifically, we first establish a digital twin-assisted VEC network that migrates the blockchain consensus process from the physical space to the cyber space, supporting the dynamic handover of vehicles. Correspondingly, we propose a lightweight blockchain scheme named diffused delegated Byzantine fault tolerance (d2BFT). Then, aiming at simultaneously reducing the task processing latency and improving the blockchain transaction throughput, we formulate the joint blockchain, communication, computation, and caching (B3C) optimization problem subject to task division, communication bandwidth, computing frequency, cache storage, task deadline, and blockchain stability. Due to the nonconvexity of B3C, we transform it into a Markov decision process, and propose a multiagent double actor-critic (MADAC) algorithm in light of the distributed characteristic of blockchain. Through offline training and online execution, we jointly optimize the task division, communication bandwidth, computing frequency and cache storage allocation, block size, and block generation interval for ISTOC. Experimental results show that the proposed MADAC-based ISTOC scheme can stably converge with a much higher reward than the benchmark schemes based on MADDPG, soft actor-critic, deep deterministic policy gradient, and TD3. The improvement of MADAC-ISTOC over SAC-ISTOC is more than 25.93%.

Minimum weight distribution (MWD) is an important metric to calculate the first term of union bound called minimum weight union bound (MWUB). In this paper, we first prove the maximum likelihood (ML) performance approaches MWUB as signal-to-noise ratio (SNR) goes to infinity and provide the deviation when MWD and SNR are given. Then, we propose a nested reliability sequence, namely MWD sequence, to construct polar codes independently of channel information. In the sequence, synthetic channels are sorted by partial MWD which is used to evaluate the influence of information bit on MWD and we prove the MWD sequence is the optimum sequence evaluated by MWUB for polar codes obeying partial order. Finally, we introduce an entropy constraint to establish a relationship between list size and MWUB and propose a heuristic construction method named entropy constraint bit-swapping (ECBS) algorithm, where we initialize information set by the MWD sequence and gradually swap information bit and frozen bit to satisfy the entropy constraint. The simulation results show the MWD sequence is more suitable for constructing polar codes with short code length than the polar sequence in 5G and the ECBS algorithm can improve MWD to show better performance as list size increases.

Computationally intensive control tasks, especially the control of image-based mobile controllers, usually require more computational capacity and network transmission resources than traditional control tasks due to the assembly of camera-based visual perception sensors. As a result, the collaborative design of network, computation, and control is of great importance to improve the efficiency of resource usage while ensuring control performance, thus achieving overall system optimality. In this article, we proposed a synergistic algorithm for network and computational resource scheduling through deep deterministic policy gradient and control based on self-triggered model predictive control, which provides optimal resource scheduling instructions after observing the system state in real time, and uses these resources to complete control tasks in the controller with a self-triggering mechanism to achieve the goal of ensuring control performance and reducing energy consumption. Finally, we designed numerical simulation experiments to verify the effectiveness of the algorithm proposed in this article.

In this paper, we consider the trade-off problem of control performance and computing resource utilization in edge controller in the context of smart manufacturing. In the Internet factory, an edge device needs to complete multiple computing tasks including control tasks under the condition of limited computing resources. Therefore, we propose a Time Division Multiplexing based self-triggered model predictive control algorithm and a computing resource allocation algorithm based on reinforcement learning to solve the optimal control input and optimal computing resource allocation scheme at the same time. Through the information interaction between the controller and the computing resource management unit, the proposed algorithm can calculate the future optimal control strategy and resource allocation scheme according to the real-time state of physical system and computing resource utilization requirement. Through simulation analysis, we get the trade-off relationship between control performance and computing resource allocation, and verify the effect of the method in the real-time operation of the system.

It has been widely recognized that the practical application of deep learning (DL) to condition monitoring of industrial equipment relies heavily on having comprehensive coverage of essential features in the training data set, i.e., the data collected should include all potential feature modes under various operating conditions. Given this limitation, this article proposes a multilevel branch neural network (MLBNN) with self-evolution capability. In particular, the MLBNN structure can be updated online when emerging fault modes or operating conditions are encountered during equipment operation. Two case studies employing an industrial robot arm joint bearing and a motor bearing fault diagnosis data set demonstrate the efficacy of MLBNN. We then demonstrate that MLBNN is suitable for deployment under cloud-edge computing architecture, which can further improve the speed of model update operations during self-evolution process and model inference to meet online condition monitoring requirements. Finally, the performance of MLBNN is compared to classical DL and transfer learning approaches under an incomplete training data set scenario, and results show that the accuracy of MLBNN is superior to others.

Deterministic response is the core foundation for the safe operation of industrial production systems. However, with the increasing demand for intelligence, flexibility, and agility, ensuring the deterministic response of computing and control tasks while meeting new demands has become the primary issue that manufacturers urgently need to address. In response to this issue, this article focuses on AI-driven cyber–physical production systems (AI-CPPSs) and conducts research on the adaptive interaction mechanism of network, computing, and manufacturing resources with guaranteed performance. The efficient adaptive configuration of network, computing, and manufacturing resources is used to meet the response requirements of dynamic tasks. To achieve on-demand configuration of multidimensional resources for tasks, we first propose an AI-CPPS-oriented modeling method named the hourglass method, which redefines task models and multidimensional resources with resources as the core. Furthermore, through the proposed method of computing power quantification and a heterogeneous frame structure, we achieve the unified arrangement of network, computing, and manufacturing resources in the time dimension. Finally, to ensure the security and reliability of resource interaction, a multidimensional resource interaction mechanism is proposed for network computing control, namely, the quicksand mechanism. The experimental results indicate that the proposed quicksand mechanism can optimize resource utilization based on ensuring a deterministic task response.

Industrial Internet, motivated by the deep integration of new-generation information and communication technology (ICT) and advanced manufacturing technology, will open up the production chain, value chain, and industry chain by establishing complete interconnections between humans, machines, and things. This will also help establish novel manufacturing and service modes, where personalized and customized production for differentiated services is a typical paradigm of future intelligent manufacturing. Thus, there is an urgent requirement to break through the existing chimney-like service mode provided by the hierarchical heterogeneous network architecture and establish a transparent channel for manufacturing and services using a flat network architecture. Starting from the basic concepts of process manufacturing and discrete manufacturing, we first analyze the basic requirements of typical manufacturing tasks. Then, with an overview on the developing process of industrial Internet, we systematically compare the current networking technologies and further analyze the problems of the present industrial Internet. On this basis, we propose to establish a novel “thin waist” that integrates sensing, communication, computing, and control for the future industrial Internet. Furthermore, we perform a deep analysis and engage in a discussion on the key challenges and future research issues regarding the multi-dimensional collaborative sensing of task–resource, the end-to-end deterministic communication of heterogeneous networks, and virtual computing and operation control of industrial Internet.

Subsurface flow is the core of reservoir engineering. Research on subsurface flow problems can enhance our understanding of the development status of oilfields, thus enabling the prediction of the distribution of residual oil and the formulation of production plans. Machine learning-based methods have been widely studied in developing data-driven models to solve subsurface flow problem. However, a large amount of labeled data are required to build highly accurate models. It is applicable to embed domain-knowledge into data-driven machine learning methods to reduce data requirements. In this study, we propose a residual-enhanced physics-guided machine learning method with hard constraints (RHC-PGML) to predict reservoir pressure. Specifically, a physics-guided machine learning with hard constraints strategy is proposed, which makes the prediction results strictly satisfy the prior domain knowledge to improve the prediction accuracy of the model. In addition, we integrate residual learning in RHC-PGML model to compensate for systematic errors caused by the inability of the embedded physical mechanism to perfectly describe the complex seepage process and further improve the prediction accuracy. The proposed method is verified by a seepage problem in a heterogeneous reservoir model. The results show that the RHC-PGML method can obtain reliable prediction results in the case of sparse and limited data.

As the precursor of cyber-attacks, the campaigns of scanning groups are able to reflect the attack target and attack trend to a great extent, which provide highly valuable threat intelligence for cyber defenders to understand the current cyber security situation. However, how to identify scanning groups in the context of limited information, especially in the absence of relevant threat intelligence, remains a challenging problem. In this paper, we utilize the honeynet as the unique data source to propose a scanning group identification system, Scanner-Hunter, which focuses on identifying scanning groups targeting ICS devices. To better characterize scanning patterns, a novel traffic representation scheme for scanning traffic is proposed, which is composed of a set of feature vectors to describe all the ICS request packets. On this basis, we propose a novel self-expanding multi-class classification (SEMCC) model and the IP prefix judgment, which are deliberately integrated to cope with sophisticated scanning groups. Take the Modbus protocol as an example, we implement a prototype of Scanner-Hunter, and use six years of real-world honeynet datasets to evaluate its performance. The experimental results illustrate its effectiveness and superior performance compared with some popular machine learning methods and existing SOTA scanning group identification methods. In addition, Scanner-Hunter is further leveraged to investigate the group distribution and maliciousness of 506 unknown scanners, and some suspicious attack groups with APT characteristics are analyzed. Furthermore, accurate scanning group information will contribute to revealing potential attack organizations and supporting decision making to prevent or interrupt cyber-attacks in time.

Beam pumping units (BPUs) are key equipment in oilfield production. Currently, many fault diagnosis methods for BPUs have been developed, and most of them are based on feature or image classification of indicator diagrams. However, low-quality monitoring data and the limited proportion of effective pixels in indicator diagram greatly restrict the performances of these methods. This article proposes an efficient two-step fault diagnosis method for BPUs. In the first step, to overcome the impact of low-quality monitoring data, a dynamic time warping-based matching method is proposed to extract the period of the data, and then a physical model driven method optimized by Bayesian gradient descent is proposed to reconstruct the data. In the second step, to overcome the impact of the limited proportion of effective pixels in indicator diagram, a parallel deep network is proposed which directly takes the time series of the displacement and the load of BPUs as the inputs. Extensive experiments on dataset from 45 real oil wells have shown that, the proposed method can achieve the best performance compared with the state-of-the-art methods, meanwhile the computational load is only 5% of other deep learning-based methods.

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction.

Accurately estimating the state of equipment plays an important role in ensuring the efficient operation of Industrial 4.0 systems. This article focuses on monitoring the operating state and detecting the faults of beam pumping units under the condition of heavy noise within the Industrial Internet of Things. On the one hand, the equipment operating state monitoring system designed in this article uses an acceleration sensor, the signal of which contains considerable noise that greatly reduces the motion state estimation accuracy. On the other hand, the complexity of the indicator diagrams of beam pumping units makes it difficult to extract features, which limits the ability to improve the fault detection accuracy. To overcome these issues, first, a period estimation method based on self-checking that employs acceleration data is proposed to effectively overcome the influence of complex noise on the estimated data period; second, a denoising method based on a physical model is proposed to effectively reduce the influence of complex noise on the acceleration-based displacement estimation; and third, a method for detecting the faults of beam pumping units based on edge intelligence is proposed to effectively improve the fault detection accuracy while maintaining a low computational demand. Extensive experiments on real data verify the effectiveness of the proposed method. To the best of our knowledge, this is the first work to discuss the impact of the quality of data on the performance of fault detection of beam pump units.

The cyber–physical production system (CPPS) has practical requirements, such as distributed, reconfigurable, and high-performance, which bring a new challenge to performance guarantee of remote manufacturing with time delay under shared resources. Time delay has a great influence on system performance, and the mainstream methods mainly reduce its influence by optimizing control. However, due to the uncertainty of time delays, the existing methods have great limitations, and the configuration complexity is high. To address this issue, we take teleoperation as the control model, then we introduce 5G slicing and edge computing technologies to turn this control problem into a control–communication–computing co-design problem. An industrial teleoperation testbed is implemented to help clarify this problem and explore the key points in solving it. Then, a novel co-design teleoperation platform (CdTP) is designed that can quantitatively describe the relationship between the time delay and system configuration. Based on CdTP, system performance assurance does not have to be achieved by blindly increasing the total amount of resources, but can be achieved by improving the utilization of shared control–communication–computing resources. In addition, CdTP can dynamic configure resources based on changing requirements, which make it combines flexibility, real time, and reliability. Finally, we propose a resource allocation method to minimize the maximum job delay. The evaluation and experimental results indicate that our platform can achieve an all-in-one configuration, and validation of the proposed method is conducted to provide deterministic delay guarantees.

With the interdisciplinary advances of mobile communication and edge computing, massive heterogeneous tasks are accessing wireless networks and competing for the edge-end computing and communication resources. Digital twin (DT), which establishes the digital models of physical objects for simulation, analysis and optimization, provides a promising method for network scheduling and management. This paper proposes a DT-driven edge-end collaborative scheduling algorithm for heterogeneous tasks and heterogeneous computing/communication resources. Specifically, multiple end devices (EDs) cooperate with each other to accomplish a complex job, where each ED can offload individual task to multiple edge servers (ESs) for parallel computing. By fully considering deadline requirements of heterogeneous tasks, maximum computing capabilities of ESs and EDs, computing resource estimation deviations of DT, maximum transmit powers of EDs and tolerable peak interference powers to coexisting EDs, we formulate a job completion time minimization problem to jointly optimize the edge-end task division, transmit power control, computing resource type matching and allocation. To solve this non-convex problem, we first reformulate it by multi-agent Markov decision process, where a compound reward leveraging latency reward and deadline reward according to the task criticality is designed. Then, we propose a multi-agent deep reinforcement learning-based scheduling algorithm, where Actor-Critic framework with estimation and target networks is designed for policy and value iterations. Meanwhile, a step-by-step ϵ-greedy algorithm is proposed to balance exploration and exploitation, avoiding local optimal trap. Through offline centralized training by DT and online distributed execution by EDs, we realize edge-end collaborative computing for heterogeneous tasks. Experimental results demonstrate that, comparing with typical benchmark algorithms, the proposed algorithm converges with the highest reward and achieves the smallest job completion time, where the deadlines of heterogeneous tasks can be well satisfied respectively.

Service uncertainty modeling is an important problem of manufacturing service composition optimization, this article proposes a cloud manufacturing service composition optimization framework based on cloud-edge collaboration considering manufacturing service uncertainty. In the proposed framework, on the edge side, a model parameters estimation method of the manufacturing services' uncertainty is proposed based on Gaussian mixture regression; while on the cloud side, an intelligent evolutionary algorithm is adopted to effectively optimize the manufacturing service composition. Since the Gaussian mixture distribution is used to approximate the service availability distribution, the service uncertainty can be modeled adaptively. Compared with the previous optimization methods of manufacturing service composition with uncertainty based on the deterministic parameter models, the method proposed in this article can model the uncertainty of service more effectively, thus obtain better service composition solutions. Extensive experimental results prove the effectiveness of the algorithm.

With the fast evolution in the cellular communication, the unlicensed spectrum is exploited to resolve the shortage of band resources. The sharing of the unlicensed spectrum extends the applications of LTE and 5G NR techniques, especially in the industrial Internet of Things (IIoT). However, the coexistence problem among various communication technologies in the unlicensed spectrum arises great concerns due to the different communication mechanisms. The existing solutions are either not compatible with the LTE/NR standards or not flexible enough for complex and dynamic IIoT environments. In this paper, we propose a policy learning based unlicensed communication (PLUC) framework to directly learn coexistence policies from the spectrogram of frequency channels. A recurrent neural network (RNN) is built to deal with the observations from time-variant spectrogram and extract deep learning features. We further verify this framework under the duty cycle mechanism and the listen before talk mechanism in 3GPP standards, respectively. The experiments reveal the effectiveness of the proposed framework in the dynamic environment.

Compared to industrial wired networks, 5G can improve device mobility and reduce the cost of networking. However, the real-time performance and reliability of 5G new radio (NR) still need to be improved to satisfy industrial applications’ requirements. In factories, the main factor that affects the performance of 5G NR is the unstable signal quality caused by high temperatures and metal. Although assigning dedicated resources to all transmissions and retransmissions is an effective method to improve the performance of 5G NR, the unstable signal quality causes the resources required for retransmissions to be uncertain. To address the problem, we introduce the mixed-criticality task model to 5G NR. When high-criticality packets cannot be transmitted, they are allowed to preempt the resources shared with low-criticality packets. The mixed-criticality scheduling problem of 5G NR is NP-hard. We formulate it as an optimization modulo theories (OMT) specification and propose a scheduling algorithm based on bin packing methods to make 5G NR satisfy industrial applications’ requirements. Finally, we conduct extensive evaluations based on an industrial 5G testbed and random test cases. The evaluation results indicate that our algorithm makes communication reliability greater than 99.9% on unlicensed spectrum, and for most test cases, our algorithm is close to optimal solutions.

Edge artificial intelligence will empower the ever simple industrial wireless networks (IWNs) supporting complex and dynamic tasks by collaboratively exploiting the computation and communication resources of both machine-type devices (MTDs) and edge servers. In this paper, we propose a multi-agent deep reinforcement learning based resource allocation (MADRL-RA) algorithm for end-edge orchestrated IWNs to support computation-intensive and delay-sensitive applications. First, we present the system model of IWNs, wherein each MTD is regarded as a self-learning agent. Then, we apply the Markov decision process to formulate a minimum system overhead problem with joint optimization of delay and energy consumption. Next, we employ MADRL to defeat the explosive state space and learn an effective resource allocation policy with respect to computing decision, computation capacity, and transmission power. To break the time correlation of training data while accelerating the learning process of MADRL-RA, we design a weighted experience replay to store and sample experiences categorically. Furthermore, we propose a step-by-step ε -greedy method to balance exploitation and exploration. Finally, we verify the effectiveness of MADRL-RA by comparing it with some benchmark algorithms in many experiments, showing that MADRL-RA converges quickly and learns an effective resource allocation policy achieving the minimum system overhead.

Demand response is an effective way for ensuring safety and stabilization of power grid by maintaining the balance between the supply and the demand of power grid, and this article focuses on using electric water heaters for demand response. In addition to considering comfort and price factors as did in previous works, this article considers the overshoot temperature and its influence on demand response. First, a theoretical model of the heating and cooling processes of the electric water heater is established; second, the demand response process using electric water heaters is analyzed, including the influences of the physical parameters and the settings of electric water heaters on the demand response process; third, a model is established considering the demand response requirement, the comfort of owners of electric water heaters, and the electricity price, simultaneously; fourth, an optimization method based on deep reinforcement learning is proposed for demand response using electric water heaters. Meanwhile, the influence of parameters on the results of demand response is discussed in details. Experimental results show the effectiveness of the proposed method.

In Industry 4.0, massive heterogeneous industrial devices generate a great deal of data with different quality of service requirements, and communicate via industrial wireless networks (IWNs). However, the limited time-frequency resources of IWNs cannot well support the high concurrent access of massive industrial devices with strict real-time and reliable communication requirements. To address this problem, a deep reinforcement learning-based dynamic priority multichannel access (DRL-DPMCA) algorithm is proposed in this article. Firstly, according to the time-sensitivity of industrial data, industrial devices are assigned with different priorities, based on which their channel access probabilities are dynamically adjusted. Then, the Markov decision process is utilized to model the dynamic priority multichannel access problem. To cope with the explosion of state space caused by the multichannel access of massive industrial devices with dynamic priorities, DRL is used to establish the mapping from states to actions. Next, the long-term cumulative reward is maximized to obtain an effective policy. Especially, with joint consideration of the access reward and priority reward, a compound reward for multichannel access and dynamic priority is designed. For breaking the time correlation of training data while accelerating the convergence of DRL-DPMCA, an experience replay with experience-weight is proposed to store and sample experiences categorically. Besides, the gated recurrent unit, dueling architecture and step-by-step$\varepsilon$-greedy method are employed to make states more comprehensive and reduce model oscillation. Extensive experiments show that, compared with slotted-Aloha and deep Q network algorithms, DRL-DPMCA converges quickly, and guarantees the highest channel access probability and the minimum queuing delay for high-priority industrial devices in the context of minimum access conflict and nearly 100% channel utilization.

Design space exploration (DSE) of cyber-physical production systems (CPPS) is a search problem in the space of potential compositional configurations. Current design methodologies follow the separated design paradigm in which the architecture, control, and scheduling are separately designed. Optimization of each part considers only the corresponding goals of interest and overlooks other aspects by adopting gross assumptions, which makes it difficult to determine the global optimal solution for a given system. To address this problem, in this article, we propose a codesign method that considers the design spaces of architecture, control, and scheduling as monolithic, mixed discrete-continuous spaces. We formulate DSE as an optimization problem and propose a generic iterative algorithm schema involving simulation in the loop to solve the abovementioned new problem. To illustrate the effectiveness of the proposed method, a real single-stage reducer assembly production system is considered. The design results demonstrate that our method provides better solutions than does the separated design method.

At present, safely solving complex and high- precision assembly tasks in an unstructured environment is still an unresolved challenge. The development of artificial intelligence technology provides new ideas for robots in unstructured scenes to autonomously solve the problem of contact-rich peg-in-hole tasks. In this paper, we construct the shaft hole assembly task as a reinforcement learning problem, explore the change of the convergence rate when adding force and torque information in the state space, and evaluate the performance of the guided policy search algorithm on the shaft hole assembly task. We also compared the changes in the force and torque feedback from sensors at the beginning and end of learning. The experiment proves that we can complete the specific shaft hole assembly task by learning, and also shows the effectiveness of using the force and torque information when the peg and hole parts are in contact.

This paper proposes a novel edge-computing based structure to support learning-based decision-making in industry manufacturing field. This structure consists of four functional layers, respectively realizing model establishment, task allocation and task processing work. In order to take full advantage of the distributed computing resources at the edge, the manufacturing computing task can be further decomposed into several sub-tasks, separating the complex computing problem with large problem size into regional scheduling ones with much smaller problem size. All the sub-tasks are allocated to the edges, accomplished by the algorithm deployed on computing devices of region-related edge node, which contributes to faster data-processing and problem-solving speed. A simulation test has been performed in which a multi-AGV scheduling problem was solved according to a distributed reinforcement learning method configured in such edge computing architecture. The objective of each edge node is to acquire AGV schedule of related region that minimizes the makespan. Simulation results demonstrate that this distributed edge computing system can be enabled to learn satisfying solution and converge much faster when it is compared with conventional method applied in centralized architecture.

Using unmanned aerial vehicles (UAVs) for equipment condition monitoring is an important application of Industrial Internet of Things (IIoT), and the limited energy is the key factor to restrict the application of UAV. In order to reduce the computational load for intelligence computing of UAV, this article proposes a cloud edge collaborative intelligent method for object detection, and applies it to insulator string recognition defect detection in the power IIoT. First, the impact of the extremely large aspect ratio of object on the detection accuracy and the computational load is analyzed, then the cloud edge collaborative intelligent method for insulator string detection and defect recognition is presented, in which on the UAV side a low cost method is proposed for estimating possible directions of insulator strings, and on the cloud side, an effective method is proposed for insulator string defect detection. The experimental results show the effectiveness of the proposed algorithm. To the best knowledge of us, this article is the first work to analyze the impact of the extremely large aspect ratio of insulator string on the detection accuracy and the computational load.

Mobile video communication and Internet of Things are playing a more and more important role in our daily life. Mobile Edge Computing (MEC), as the essential network architecture for the Internet, can significantly improve the quality of video streaming applications. The mobile devices transferring video flow are often exposed to hostile environment, where they would be damaged by different attackers. Accordingly, Mobile Edge Computing Network is often vulnerable under disruptions, against either natural disasters or human intentional attacks. Therefore, research on secure hub location in MEC, which could obviously enhance the robustness of the network, is highly invaluable. At present, most of the attacks encountered by edge nodes in MEC in the IoT are random attacks or random failures. According to network science, scale-free networks are more robust than the other types of network under the random failures. In this paper, an optimization algorithm is proposed to reorganize the structure of the network according to the amount of information transmitted between edge nodes. BA networks are more robust under random attacks, while WS networks behave better under human intentional attacks. Therefore, we change the structure of the network accordingly, when the attack type is different. Besides, in the MEC networks for mobile video communication, the capacity of each device and the size of the video data influence the structure significantly. The algorithm sufficiently takes the capability of edge nodes and the amount of the information between them into consideration. In robustness test, we set the number of network nodes to be 200 and 500 and increase the attack scale from 0% to 100% to observe the behaviours of the size of the giant component and the robustness calculated for each attack method. Evaluation results show that the proposed algorithm can significantly improve the robustness of the MEC networks and has good potential to be applied in real-world MEC systems.

The smart factory is a representative element reshaping conventional computer-aided industry to data-driven smart industry, while it is nontrivial to achieve cost effectiveness, reliability, mobility, and scalability of smart industrial systems. Data-driven industrial systems mainly rely on sensory data collected from statically deployed sensors. However, the spatial coverage of industrial sensor networks is constrained due to the high deployment and maintenance cost. Recently, mobile crowd sensing (MCS) has become a new sensing paradigm owing to its merits, such as cost effectiveness, mobility, and scalability. Nevertheless, traditional MCS systems are vulnerable to malicious attacks and single point of failure due to the centralized architecture. To this end, in this article we integrate MCS with industrial systems without introducing any additional dedicated devices. To overcome the drawbacks of traditional MCS systems, we propose a blockchain-based MCS system (BMCS). In particular, we exploit miners to verify the sensory data and design a dynamic reward ranking incentive mechanism to mitigate the imbalance of multiple sensing tasks. Meanwhile, we also develop a sensory data quality detection scheme to identify and mitigate the data anomaly. We implement a prototype of the BMCS on top of Ethereum and conduct extensive experiments on a realistic factory workroom. Both experimental results and security analysis demonstrate that the BMCS can secure industrial systems and improve the system reliability.

In a vertical industry alliance, Internet of Things (IoT) deployed in different smart factories are similar. For example, most automobile manufacturers have the similar assembly lines and IoT surveillance systems. It is common to observe the industrial knowledge using deep learning and data mining methods based on the IoT data. However, some knowledge is not easy to be mined from only one factory's data because the samples are still few. If multiple factories within an alliance can gather their data together, more knowledge could be mined. However, the key concern of these factories is the data security. Existing matrix-based methods can guarantee the data security inside a factory but do not allow the data sharing among factories, and thus their mining performance is poor due to lack of correlation. To address this concern, in this article we propose the novel federated tensor mining (FTM) framework to federate multisource data together for tensor-based mining while guaranteeing the security. The key contribution of FTM is that every factory only needs to share its ciphertext data for security issue, and these ciphertexts are adequate for tensor-based knowledge mining due to its homomorphic attribution. Real-data-driven simulations demonstrate that FTM not only mines the same knowledge compared with the plaintext mining, but also is enabled to defend the attacks from distributed eavesdroppers and centralized hackers. In our typical experiment, compared with the matrix-based privacy-preserving compressive sensing (PPCS), FTM increases up to 24% on mining accuracy.

In cyber-physical petroleum systems (CPPS), accurate estimation of interwell connectivity is an important process to know reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas (O&G) field. In this study, an artificial neural network (ANN) based analysis method is proposed to estimate interwell connectivity. The generated neural network is used to define the mapping function between production wells and surrounding injection wells based on the historical water injection and liquid production data. Finally, the proposed method is applied to a synthetic reservoir model. Experimental results show that ANN based approach is an efficient method for analyzing interwell connectivity.

The agent-based multi-AGV system is envisioned to be highly flexible and adaptable for the evolving requirement of industrial automation. However, the current design method of the automation software remains monolithic and is still based on the centralized control mechanism, which makes the system engineers struggle with the complexity of the development process under the distributed system requirements. This paper applies component-based design method and the embedded agent architecture for multi-AGV system to reduce the agent design effort and improve the flexibility of the design process, and proposes a task-oriented component granularity partitioning method. A multi-AGV system in laboratory level test scenario verifies the easy-and-flexible-using of this investigation.

Wireless sensor-actuator networks enable an efficient and cost-effective approach for industrial sensing and control applications. To satisfy the real-time requirement of such applications, these networks adopt centralized scheduling algorithms to optimize the real-time performance based on global information. Existing centralized algorithms mostly focus on scheduling time-triggered flows. They cannot effectively schedule event-triggered flows due to the dynamics and unpredictability of events. In this paper, we propose three fundamental centralized algorithms that reserve as few resources as possible for event-triggered flows such that the real-time performance of time-triggered flows is not affected. We then analyze their advantages and disadvantages. Based on the analysis, we combine their advantages, including those in terms of their resource requirements, into a centralized algorithm. Finally, we conduct extensive simulations based on both real topologies and random topologies. The simulations indicate that for most test cases the schedulability of our combined algorithm is close to optimal solutions.

Information security risk assessment for industrial control system is usually influenced by uncertain factors. For effectively dealing with problem that the uncertainty and quantification difficulties are caused by subjective and objective factors in the assessment process, an information security risk assessment method based on attack tree model with fuzzy set theory and probability risk assessment technology is proposed, which is applied in a risk scenario of ship control system. Firstly, potential risks of the control system are analyzed and the attack tree model is established. Then triangular fuzzy numbers and expert knowledge are used to determine the factors that influence the probability of a leaf node and the leaf nodes are quantified to obtain the interval probability. Finally, the fuzzy arithmetic is used to determine the interval probability of the root node and the attack path. After defuzzification, the potential risks of the system and the probability of occurrence of each attack path are obtained. Compared with other methods, the proposed method can greatly reduce the impact of subjectivity on the risk assessment of industrial control systems and get more stable, reliable, and scientific evaluation results.

Industrial Internet of Things (IIoT) plays an indispensable role for Industry 4.0, where people are committed to implement a general, scalable, and secure IIoT system to be adopted across various industries. However, existing IIoT systems are vulnerable to single point of failure and malicious attacks, which cannot provide stable services. Due to the resilience and security promise of blockchain, the idea of combining blockchain and Internet of Things (IoT) gains considerable interest. However, blockchains are power-intensive and low-throughput, which are not suitable for power-constrained IoT devices. To tackle these challenges, we present a blockchain system with credit-based consensus mechanism for IIoT. We propose a credit-based proof-of-work (PoW) mechanism for IoT devices, which can guarantee system security and transaction efficiency simultaneously. In order to protect sensitive data confidentiality, we design a data authority management method to regulate the access to sensor data. In addition, our system is built based on directed acyclic graph -structured blockchains, which is more efficient than the Satoshi-style blockchain in performance. We implement the system on Raspberry Pi, and conduct a case study for the smart factory. Extensive evaluation and analysis results demonstrate that credit-based PoW mechanism and data access control are secure and efficient in IIoT.

In this paper, we propose a general model to address the overfitting problem in online similarity learning for big data, which is generally generated by two kinds of redundancies: 1) feature redundancy, that is there exists redundant (irrelevant) features in the training data; 2) rank redundancy, that is non-redundant (or relevant) features lie in a low rank space. To overcome these, our model is designed to obtain a simple and robust metric matrix through detecting the redundant rows and columns in the metric matrix and constraining the remaining matrix to a low rank space. To reduce feature redundancy, we employ the group sparsity regularization, i.e., the `2;1 norm, to encourage a sparse feature set. To address rank redundancy, we adopt the low rank regularization, the max norm, instead of calculating the SVD as in traditional models using the nuclear norm. Therefore, our model can not only generate a low rank metric matrix to avoid overfitting, but also achieves feature selection simultaneously. For model optimization, an online algorithm based on the stochastic proximal method is derived to solve this problem efficiently with the complexity of O(d2). To validate the effectiveness and efficiency of our algorithms, we apply our model to online scene categorization and synthesized data and conduct experiments on various benchmark datasets with comparisons to several state-of-the-art methods. Our model is as efficient as the fastest online similarity learning model OASIS, while performing generally as well as the accurate model OMLLR. Moreover, our model can exclude irrelevant / redundant feature dimension simultaneously.

The IEC standard WIA-PA is a communication protocol for industrial wireless sensor networks. Its special features, including a hierarchical topology, hybrid centralized-distributed management and packet aggregation make it suitable for large-scale industrial wireless sensor networks. Industrial systems place large real-time requirements on wireless sensor networks. However, the WIA-PA standard does not specify the transmission methods, which are vital to the real-time performance of wireless networks, and little work has been done to address this problem. In this article, we propose a real-time aggregation scheduling method for WIA-PA networks. First, to satisfy the real-time constraints on dataflows, we propose a method that combines the real-time theory with the classical bin-packing method to aggregate original packets into the minimum number of aggregated packets. The simulation results indicate that our method outperforms the traditional bin-packing method, aggregating up to 35% fewer packets, and improves the real-time performance by up to 10%. Second, to make it possible to solve the scheduling problem of WIA-PA networks using the classical scheduling algorithms, we transform the ragged time slots of WIA-PA networks to a universal model. In the simulation, a large number of WIA-PA networks are randomly generated to evaluate the performances of several real-time scheduling algorithms. By comparing the results, we obtain that the earliest deadline first real-time scheduling algorithm is the preferred method for WIA-PA networks.

In this paper, a fully distributed hierarchical control strategy is proposed for operating networked gridsupporting inverters (GSIs) in islanded ac microgrids (MGs). The primary control level implements frequency and voltage control of an ac MG through a cascaded structure, consisting of a droop control loop, a virtual impedance control loop, a mixed H2/H∞-based voltage control loop, and a sliding-mode-control-based current loop. Compared to conventional proportional-plus-integral-based cascaded control, the proposed cascaded control does not require a precise model for the GSI system. The proposed secondary control level implements distributed-consensus-based economic automatic generation control and distributed automatic voltage control, which integrates the conventional secondary control and tertiary control into a single control level by bridging a gap between traditional secondary control and tertiary control. Simulation results demonstrate the effectiveness of the proposed hierarchical control strategy.

Continuous body temperature measurement (CBTM) is of great significance for human health state monitoring. To avoid interfering with users' daily activities, CBTM is usually achieved using wearable noninvasive thermometers. Current wearable noninvasive thermometers employ steady-state models used in nonwearable thermometers; as a result, the reaction time is long and the measurement can be disturbed by users' activities. However, there is no work to solve these issues. In this paper, first, differences between wearable and nonwearable temperature measurement are analyzed. Second, the relationship among the human body temperature, the skin temperature, and the device temperature is modeled based on artificial neural networks (ANNs). Third, this paper proposes a novel multiple ANNs-based wearable CBTM method. Experiments show that the reaction time of the proposed method is about one-tenth of that of other popular wearable noninvasive CBTM methods, while the accuracy and the robustness are improved.

The connection of business processes along the value chain and the integration of the production system with business process are two cornerstones of Industry 4.0. The Industry 4.0 Demonstration Production Line (DPL) located at Shenyang Institute of Automation (Shenyang) is a prototype smart factory that implements end-to-end customized production. It is also a testbed for intelligent manufacturing research. Modeling and simulation provide researcher with good insights into the real manufacturing system and help exploring and evaluating new manufacturing control methods. Ptolemy II, a platform that support heterogeneous modeling and simulation, was used to model the Demonstration Production Line. This paper discusses about the main concerns of the modeling problem and presents the modeling process in detail. Simulation result shows that the model can be used for controller logic verification and throughput estimation.

Within the context of industry 4.0, the personalized customization requires the manufacture more agile and flexible, which means the reconfigurable manufacturing system is crucial for enterprise to remain competitive. However, most of the existing systems have to suspend when reconfiguring, as the online reconfiguration may lead system to disorder and uncertainty. To minimize the leading time of reconfiguration while ensuring system stability, this paper proposes a solution based on an industrial pipeline structure by utilizing IEC 61499 Function Blocks combined with Multi-agent systems and ontology. And a simulation is conducted to verify the effectiveness of this solution.

With the development of wireless transmission technology, the coexistence of a variety of network protocols is inevitable in industrial control networks for a certain period of time. Due to the lack of a necessary coordination mechanism between different network protocols, this may impact the transmission performance of the backhaul network connecting several subnetworks with different protocols when there are traffic bursts in certain subnets. This paper solves the problem from the perspective of dynamic routing that is based on power control. When a subnet gateway node cannot meet the bandwidth demands of the traffic burst, the interference-reducing dynamic power control method (IR-DPC) is proposed to resolve the impact on backhaul networks. It via the nodes can be connected to the gateway (1) directly, (2) by increasing the power of the gateway and (3) by adjusting the antenna direction and power respectively to conduct power control and routing recovery. Then, the method was analyzed through theoretical analysis and simulation. The results show that the routing recovery time is within 10 ms and the traffic burst bandwidth demand that is satisfied is more than twice that of traditional methods within a given range.

Wireless sensor networks (WSNs) have been widely used in industrial systems that demand a high degree of reliability and real-time performance in communications. However, because of limited network resources and delays caused by transmission conflicts and channel contention, many industrial applications cannot meet these demands. To resolve these issues, we introduce Narrow Band Internet of Things (NB-IoT) technology into industrial networks to reduce transmission delays. Two challenges are addressed in this work: (1) the primary goal is to guarantee the schedulability of the system, so the controller must determine when NB-IoT nodes transmit to base stations; and (2) because communication through the base station is a paid service, the controller must minimize the use of NB-IoT slots. We resolve these issues via a scheduling analysis and transmission mode selection. In addition, we propose a Path Selection Algorithm (PSA) to improve the schedulability of the industrial system. Experiments indicate the effectiveness and efficacy of our approach.

The increasing new product variants force the manufacturing enterprise to take a short time in adjusting the automatic assembly line rapidly, but it seems a challenging task. Realizing integration of information technology and manufacturing technology is the very essence of new manufacturing schema. This paper introduces an ontology-based modeling and application method to test the feasibility of manufacturing system. This work uses the SPARQL query and update technology to reflect the accurate entities' state and obtain device information. The predefined rules are used to build the corresponding rule repository according to the practical product process and knowledge base. A reasoning engine is utilized to infer facts about the assembling environment from the formalized knowledge model and decide whether the current environment can support the given assembling requirements. The result shows the ontology technology is useful for the assembly line of manufacturing system.

This paper presents an approach to enabling ontology-based web service integration for flexible manufacturing systems based on industry 4.0 demonstration production line. The mass personalized customization requires increased agility and flexibility in manufacturing systems to adapt incessant changes in manufacturing environments and requirements. The fundamental for this method lies in the incorporation of knowledge expressed by ontologies concerning order, products, industrial equipment, manufacturing process, event and service. It is a significant method to use ontology for representing manufacturing knowledge in a computer-interpretable way. This knowledge model can be used by automated decision-making to configure the control component that monitors and harmonizes the whole manufacturing system. The semantic model of the flexible manufacturing systems can be automatically updated based on event notifications sent by the specialized web services. The architecture and knowledge model have been demonstrated in a pilot case named industry 4.0 demonstration production line and can be applied to other flexible manufacturing systems.

Due to the growing dependencies of information network technology, networked control systems are undergoing a severe blow of cyberattacks, and simply modeling cyberattacks is inadequate and impractical for the detection requirements, because of various vulnerabilities in these systems and the diversities of cyberattacks. Actually, a feasible viewpoint is to identify misbehaviors by constructing a normal model of industrial communication behaviors. However, one of the chief difficulties is how to completely and appropriately summarize industrial communication behaviors according to the specific communication characteristics. In view of process control and data acquisition, this paper associates industrial communication characteristics with the time sequence, and further extracts two distinct behaviors: function control behavior and process data behavior. Based on these double behavior characteristics, we introduce one-class classification to detect the corresponding anomalies, respectively. Besides, we also present the weighted mixed Kernel function and parameter optimization method to improve classification performance. Experimental results clearly demonstrate that the proposed approach has significant advantages of classification accuracy and detection efficiency.

A smart factory generates vast amounts of data that require transmission via large-scale wireless networks. Thus, the reliability and real-time performance of large-scale wireless networks are essential for industrial production. A distributed data transmission scheme is suitable for large-scale networks, but is incapable of optimizing performance. By contrast, a centralized scheme relies on knowledge of global information and is hindered by scalability issues. To overcome these limitations, a hybrid scheme is needed. We propose a hierarchical data transmission framework that integrates the advantages of these schemes and makes a tradeoff among real-time performance, reliability, and scalability. The top level performs coarse-grained management to improve scalability and reliability by coordinating communication resources among subnetworks. The bottom level performs fine-grained management in each subnetwork, for which we propose an intrasubnetwork centralized scheduling algorithm to schedule periodic and aperiodic flows. We conduct both extensive simulations and realistic testbed experiments. The results indicate that our method has better schedulability and reduces packet loss by up to $22\%$ relative to existing methods.

Industrial systems demand high degree of reliability and real-time requirements in communications. To meet the stringent real-time performance requirements of control systems, there is a critical need for estimation system schedulability before system running. Concerning this issue, in this paper, we propose a supply/demand bound function analysis approach based on earliest deadline first scheduling to estimate system schedulability when we know network routing and the information of flows. By estimating system upper-bound demand, we can determine the schedulability of industrial wireless networks. Experiments indicate the effectiveness and efficacy of our approach.

Time synchronization is one of the key technologies in Industrial Wireless Sensor Networks (IWSNs). Considering the demand of low energy consumption, fast convergence and robustness for IWSNs, this paper presents a novel Cluster-based Maximum consensus Time Synchronization method. Based on the theory of distributed consensus, the method utilizes the maximum consensus approach to realize the intra-cluster time synchronization. In the inter-cluster time synchronization, adjacent clusters exchange the time messages via overlapping nodes to synchronize with each other. In addition, the clustering technique is incorporated in the method, which can effectively reduce the redundant data. And the hops can be controlled, which improves the energy efficiency and convergence rate very well. At last, the simulation results show that our method reduces the communication overhead and improves the convergence rate in comparison to existing works.

In order to detect abnormal communication behaviors efficiently in today's industrial control system, a new intrusion detection algorithm based on One-Class Support Vector Machine OCSVM is proposed in this paper. In this algorithm, a normal communication behavior model is established by using OCSVM, and the Particle Swarm Optimization algorithm is designed to optimize OCSVM model parameters. Furthermore, we adopt the normal Modbus function code sequence to train OCSVM model, and then use this model to detect abnormal Modbus TCP traffic. Our simulation results show that the proposed algorithm not only is efficient and reliable but also meets the real-time requirements of anomaly detection in industrial control system. Copyright © 2015 John Wiley & Sons, Ltd.

It has been brought to our attention that the above article 1 failed to adequately refer to a previous iteration, published as part of a conference proceeding. Wenli Shang, Lin Li, Ming Wan and Peng Zeng, " Industrial communication intrusion detection algorithm based on improved one-class SVM," 2015 World Congress on Industrial Control Systems Security (WCICSS), London, 2015, pp. 21– 25. doi: 10.1109/WCICSS.2015.742031 We apologize for this error and hope this corrigendum provides clarity.

In the formal analysis of real-time systems, modeling of branching codes and modeling of intratask parallelism structures are two of the most important research topics. These two real-time properties are combined, resulting in the fork-join real-time task (FJRT) model, which extends the digraph-based task model with forking and joining semantics. We prove that the EDF schedulability problem on a preemptive uniprocessor for the FJRT model is coNP-hard in the strong sense, even if the utilization of the task system is bounded by a constant strictly less than 1. Then, we show that the problem becomes tractable with some slight structural restrictions on parallel sections, for which we propose an exact schedulability test with pseudo-polynomial time complexity. Our results thus establish a borderline between the tractable and intractable FJRT models.

Abstract Power transmission is the bulk transfer of electrical energy from power plants to sub‐stations. A wireless sensor network is a promising technology for transmission line monitoring due to its low cost, easy installation, large‐scale coverage, and fault tolerance characteristics. A wireless sensor network is application‐specific; therefore, we investigate the new features and requirements of the wireless sensor network used in transmission line monitoring. Then, we propose an efficient wireless sensor network framework, which includes a clustering algorithm to simplify network management and to balance the network's energy consumption and a hybrid media access control (MAC) (H‐MAC) protocol to handle traffic variability. The framework takes advantage of the features of network topology and traffic pattern to optimize the protocols' performance on real time and energy efficiency. The results indicate that the H‐MAC shows a significant improvement in the network's reliability, real‐time performance, and energy efficiency, and the cluster hierarchy can balance the network's energy consumption. Furthermore, the cluster hierarchy also prolongs the network's lifetime. Copyright © 2014 John Wiley & Sons, Ltd.

ABSTRACT Reliability is a crucial aspect of time synchronization for industrial wireless applications in wireless sensor networks. Existing time synchronization algorithms often provide good synchronization in laboratory environments; however, outdoor environments with associated radio interference influence the performance of time synchronization. In this paper, we propose a random time source protocol for industrial wireless applications in wireless sensor network synchronization. Each synchronized node randomly selects its time source for each period in order to prevent reliance on a fixed time source because this may lead to resynchronization once the source fails. We have implemented the algorithm on the SIA2420 platform usingTinyOS, and the results show the reliability of our protocol. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract Wireless process control has been a popular topic recently in the field of industrial control. In the industrial field, wireless technologies are considered despite the lack of an ideal industrial wireless standard. However, application development of industrial wireless networks is slow due to the lack of an ideal standard. Open standards are the foundation of industrial wireless application extensions. This paper first summarizes a standardized process for industrial wireless network technologies and then introduces network composition, network topology, protocol stack architecture, and some key protocol technologies of WIA‐PA, which is an international specification of industrial wireless networks for process automation. Furthermore, a comparison between WIA‐PA and other main industrial wireless network specifications like WirelessHART and ISA100.11a is provided. Architecture and key technologies of a WIA‐PA are also introduced. Our first‐hand experiences in developing WIA‐PA testbed based on the modularization method are given. Finally, experiment results illustrate the performance and efficiency of WIA‐PA. Copyright © 2010 John Wiley & Sons, Ltd.

In the following paper, we study the tradeoff between network lifetime and network utility for energy-constrained wireless sensor networks (WSNs) with the real-time requirement. By introducing a parameter r, we combine these two objectives into a single weighted objective, and consider rate control and routing in this tradeoff framework simultaneously. For real-time requirement, we set up real-time constraints by forcing the end-to-end delay of each route to be bounded by the maximum tolerated delay and incorporate real-time constraints into the tradeoff framework. Consequently, the tradeoff model is formulated nonlinear programming. By using the dual decomposition method and gradient/subgradient algorithms, we propose a distributed algorithm to solve nonlinear programming. Rigorous analysis and simulation are presented in order to validate our algorithm.

WIA-PA is one of two IEC open wireless standards for the industrial process automation. In this demonstration we build a fully operational WIA-PA network and illustrate how to interconnect WIA-PA network with PLC system. We show the construction of the network, the process of configuring WIA-PA through existed PLC system, protocol and data transfer, and flow of data for a process monitoring and control application. This demonstration network serves as a proof of the WIA-PA standard viability and as a platform for our future research and experiments.

We propose a novel model to formally define the lifetime of target tracking sensor network based on energy by considering the relationship between individual sensors and the whole sensor network, the importance of different sensors based on their roles in delay constrained routing. The model is valuable for two reasons. First, it exposes the dependence of lifetime on factors like hop bound, network density, radio transmission range, sensing range and target behavior within the region. This allows us to see what factors have the most impact on lifetime and consequently where engineering effort is best expended. Secondly, the model implies the best routing strategy. In this paper, we also suggest a simple routing algorithm to achieve the bound.