In smart grids, smart meters periodically collect users' fine-grained multi-dimensional energy data, which poses great concerns on users' privacy and security. Existing privacy preserving multi-dimensional aggregation schemes suffer from heavy computational burdens, especially for smart meters with limited computational resources. To address these limitations, in this paper we propose an efficient and flexible multi-dimensional data aggregation scheme called MDA-SMuSha, by which smart meters employ the Shamir's multi-secret sharing to generate a set of shared secrets, with the first one kept locally, while the remained ones are packaged and then uploaded to a control center via an aggregator. By the MDA-SMuSha scheme, aggregation results of smart meters' multi-dimensional energy data during multiple periods can be obtained, with only one time of Paillier encryption conducted on the smart meters. In addition, it allows the control center to send query requests flexibly, i.e., at a pre-specified frequency or whenever it wants to obtain statistical data of interests. Rigorous security analyses show that the MDA-SMuSha scheme satisfies security requirements of privacy-preservation, authenticity and data integrity as well as fault-tolerance. Both theoretical analyses and experiment results show that the MDA-SMuSha scheme outperforms state-of-the art methods in terms of computation costs, with comparable communication costs.

In industrial wireless networks with resource-constrained and densely deployed devices, link scheduling is a challenging task. Traditional optimization methods have high computational complexity and low scalability. Graph learning offers a promising approach, yet it also comes with limitations of capturing multivariate relationships from interference, leading to ineffective link scheduling. In this article, hypergraphs are additionally introduced to model cumulative interference from concurrent transmissions. Considering the constructed comprehensive interference model, we propose a Lightweight link scheduling algorithm based on hybrid binary Graph and HyperGraph representation learning (L-GHG) in an unsupervised manner. Thereinto, the L-GHG algorithm extracts and fuses features from various interference relationships for link scheduling decisions. A graph partitioning based on channel state information is proposed to reduce redundant search space arising from cumulative interference. Simulations demonstrate that our proposed algorithm outperforms benchmark schemes regarding generalizability and scalability. We also validate the availability of the proposed algorithm in practical experiments.

In this paper, we propose a generalizable deep neural network model for indoor pathloss radio map prediction (termed as IPP-Net). IPP-Net is based on a UNet architecture and learned from both large-scale ray tracing simulation data and a modified 3GPP indoor hotspot model. The performance of IPP-Net is evaluated in the First Indoor Pathloss Radio Map Prediction Challenge in ICASSP 2025. The evaluation results show that IPP-Net achieves a weighted root mean square error of 9.501 dB on three competition tasks and obtains the second overall ranking.

Accurate detection of aero-engine blades is critical for aviation safety and maintenance. While industrial endoscopes enable efficient inspection of complex engine interiors, existing systems struggle with blade detection due to geometric variations, lighting changes, reflections, and occlusions. This paper proposes a novel spatial-temporal transformer model that enhances small blade detection via multi-scale feature extraction and improves edge robustness with an enhanced deformable DETR module, addressing defects like erosion or fractures. For video sequences, a unified spatial-temporal representation resolves counting errors (e.g., omissions/duplicates). Experiments on a custom inspection platform demonstrate superior accuracy and reliability over state-of-the-art methods.

Industrial Control Systems (ICSs) are the core of industrial production. Wireless technology, with its flexibility and adaptability, is catalyzing a transformative shift from traditional ICS to the advanced Industrial Wireless Control Systems (IWCSs). However, the openness of wireless media, high dynamics of the environment, and resource scarcity present unprecedented security challenges of high security defense costs and low detection inaccuracy for IWCS. State-of-the-art methods primarily treat ICS as a typical cyber-physical system, which focuses on security issues from the cyber and control domains, rather than the physical domain. As a result, they are unable to fully address the high dynamics of wireless channels and unknown attacks, ultimately failing to meet the stringent security requirements of industrial systems. To this end, this paper proposes a physical-domain whitelist as the final line of security defense leveraging the finite nature of the physical behavior space in industrial production systems. Moreover, a holistic cross-domain security-safety architecture is introduced, drawing inspiration from the integrated cyber-control-physical collaboration. In the proposed architecture, the top-down inherent security-safety defense and bottom-up risk backtracking form a close loop, which not only prevents unknown attacks but also facilitates rapid localization and response to attacks. In the experiment, the composite AGV scheduling control has been developed to verify the effectiveness of the architecture. Ultimately, the potential challenges of the cross-domain architecture for IWCS safety-security defense have been summarized.

In the construction industry, the integration of artificial intelligence (AI) and robotics has led to significant advancements in automating various tasks. One critical aspect is the intelligent navigation of automatic mobile robot (AMR) within construction sites, where dynamic environments pose challenges such as inaccurate robot state estimation, irregular and textureless obstacle detection. To solve these issues, this paper designs an AI enabled obstacle-aware AMR intelligent navigation approach while ensuring robot safety and efficiency. Specifically, the robot is equipped with the complementary RGB camera, inertial measurement unit (IMU) and wheel encoder to estimate the states (i.e., positions, orientations and velocities), which have been tightly fused in the optimization framework. Furthermore, the RGB-Depth images are jointly fed into the AI model. It combines of Mask-RCNN and multi-scale attention network, to detect and segment the obstacles, and estimate the corresponding relative depth. It should be noted that the system incorporates obstacle awareness mechanisms to dynamically switch the robot’s velocities in response to obstacles in the environment, ensuring smooth and collision-free movement. Experimental results demonstrate the effectiveness and robustness of the proposed AI-enabled navigation system in real-world construction scenarios, showcasing its potential to enhance productivity and safety in construction automation.

Fiber-reinforced composites (FRCs) are widely utilized across various sectors, due to their outstanding mechanical properties. The arrangement of fibers within these composites considerably influences their mechanical behavior. However, the state-of-the-art techniques on fiber orientation measurement are plagued by issues such as discontinuous boundaries and the imprecise measurement of finely oriented fibers. To this end, this work introduces a pioneering margin-aware cylindrical deep hough network (MAC-DHN) to solve these problems. The cylindrical Hough architecture, which acknowledges the$\pi$-periodicity of fiber orientations, is specifically crafted to address the problem of discontinuous boundaries. Furthermore, we design an innovative sample-wise reweighting strategy for the cross-entropy loss that enhances the differentiation between finely oriented fibers. This strategy lessens the loss related to samples with minimal prediction probability margins between the correct classification and the adjacent fine-grained categories. To comprehensively examine the fiber orientation measurement techniques in FRCs, a new dataset named FrCs orientation measurement dataset (FCOM) has been built, and the proposed method has been rigorously assessed on this dataset. Experimental results indicate that the proposed method surpasses existing techniques in terms of$F\!-\!\text{measure}$and mean absolute error (MAE), with respective scores of 0.981 and 0.219.

Time-Critical Wireless Network (TCWN) is a promising communication technology that can satisfy the low latency, high reliability, and deterministic requirements of mission-critical applications. Multiple TCWNs required by various applications inevitably coexist with each other. Most existing works aim to achieve acceptable latency or consider the simplest topology (i.e., line topology). As latency requirements become more stringent, exploring the minimum data collection latency becomes an interesting problem. In this paper, the coexisting system consists of multiple tree-topology-based TCWNs. We first establish a conversion framework to convert an arbitrary tree topology into multiple analogous line topologies to reduce the analysis complexity. We then propose a Time-Critical wireless network Scheduling (TCS) algorithm to minimize the data collection latency of coexisting TCWNs. The TCS algorithm consists of two phases. In the internetwork scheduling phase, we strictly derive a general expression to characterize the practical network requirements. In the intranetwork scheduling phase, we design two levels of priority assignment algorithms to accurately characterize the critical states and resource requirements of different nodes. We conduct extensive simulations to verify the effectiveness of the TCS algorithm. The evaluation results show that the TCS algorithm can achieve minimum data collection latency in more than 99.956% cases, and the maximum difference compared to the optimal value is one time slot.

Industrial wireless sensor networks for factory automation are globally considered as an integral part of smart manufacturing. Wireless networks for industrial automation-factory automation (WIA-FA) is the first and only international standard specifying industrial wireless sensor networks for time-sensitive automation applications. In this article, a channel-hopping-based retransmission (CHR) scheme for WIA-FA networks is proposed. To take advantage of strictly limited communication resources, we design CHR as a hybrid scheduling-based retransmission scheme that is composed of a cyclic reservation diversity retransmission (CRDR) scheme and an on-demand retransmission (ODR) scheme. For device fairness and channel diversity, we propose to combine time-slotted channel hopping with retransmission schemes. Furthermore, we perform the reliability analysis to CHR, guiding the dynamic selection of retransmission schemes according to the actual traffic and available communication resources. Simulation results demonstrate that CHR outperforms existing retransmission schemes in transmission reliability and worst case latency.

Physical layer authentication (PLA) is a promising approach to ensure wireless network security. However, existing PLA algorithms require channel state information calibration and model training at each location, which limits the device's mobile ranges. To enable zero training deployment in uncalibrated scenarios, we propose a transferable PLA (TPLA) algorithm. It exploits the channel time-varying patterns as the scenario-independent features to avoid PLA failure in uncalibrated scenarios. To extract transferable time-varying patterns from dynamic industrial mobile environments, a neural network feature extractor is designed by a multibranch parallel architecture with multiscale channel receptive fields. Furthermore, the global and component features are fused by the model-level and decision-level fusion methods to accommodate different transferability and computational cost requirements. In experiments, TPLA achieves below 2.5% authentication error in a new scenario, which proves that TPLA is an important step toward zero training deployment of the PLA algorithm.

Channel-state information (CSI)-based physical-layer authentication (PLA) has gained significant attention. However, in industrial mobile scenarios, the time-varying channels and changing device locations limit the reliability of CSI-based PLA algorithms. Furthermore, the performance of existing PLA algorithms degrades sharply at uncalibrated locations. To improve the reliability and robustness of authentication, we propose a new spatiotemporal gradient-based-PLA (STG-PLA) algorithm enhanced by CSI-to-image transformation. We first extract correlation and scattering features to depict the multidimensional channel properties, including selectivity and dispersion. We then convert several individual CSI sequences to a CSI-image. Therefore, the spatiotemporal correlation gradient of the CSI-sequences is reflected in one CSI-image. Both simulations and experiments show that STG-PLA reduces the authentication error rate from$>10{\%}$(in existing studies) to$< 1{\%}$, which signifies considerable progress toward the practical applicability. Furthermore, with no model retraining, STG-PLA exhibits the robust performance when the device moves to uncalibrated locations.

Industrial Wireless Sensor Networks (IWSNs) have been widely used in industrial applications that require highly reliable and real-time wireless transmission. A lot of works have been done to optimize the Relay Node Placement (RNP), which determines the underlying topology of IWSNs and hence impacts the network performance. However, existing RNP algorithms use a fixed communication radius to compute the deployment result at once offline, while ignoring that the radio environment may vary drastically across different locations, also known as radio irregularity. To address this limitation, we propose a Voronoi diagram and Q-learning based RNP (VQRNP) method in this article. Instead of using a fixed communication radius, VQRNP employs the Q-learning algorithm to dynamically update the radio environment of measured areas, uses a Voronoi diagram based method to estimate the radio environment of unmeasured areas, and proposes a coverage extension location selection algorithm to place RNs so as to extend the coverage of the deployed network based on the results estimated by Voronoi diagram based Graph Generating (VGG). In this way, the VQRPN method can adapt itself well to the variation of radio environment and largely speed up the deployment process. Extensive simulations verify that VQRNP significantly outperforms existing RNP algorithms in terms of reliability.

The Industrial Internet of Things (IIoTs) plays an important role in various industrial applications, which require multiple time-critical networks to be deployed in the same region. The limited communication resources inevitably incur network coexistence problems. For scenarios where coexisting networks cannot coordinate effectively, the centralized or partial-information-based decentralized resource allocation methods cannot be implemented. To address this concern, we propose a Cooperation-Free Reinforcement Learning (CF-RL) algorithm for the fully distributed resource allocation problem in coexisting IIoT systems. Each network adopts the proposed algorithm to minimize collisions through a trial-and-error approach without any information interaction. To resist the influence of environmental dynamics, each coexisting network learns the state transition probability of the resource block instead of the resource block's position. Moreover, to potentially ensure the overall system performance, each network additionally considers the period offset in the initialization phase and action selection phase, so that the coexisting networks have different preferences for different state transitions. We conduct extensive simulations to verify the convergence performance. Evaluation results show that the CF-RL algorithm almost achieves (more than 99.88%) the effect of centralized resource allocation and has obvious superiorities over other cooperation-free algorithms in terms of the convergence rate, the number of collisions, and the resource utilization ratio.

In smart grids, various Internet-of-Things-based (IoT-based) components are massively deployed across the power systems. However, most of these IoT-based components have their own vulnerabilities, leveraging which malicious users can launch different cyber/physical attacks to steal electricity. Economic losses caused by electricity theft amount to $96 billion in 2017. Most existing electricity theft detection techniques suffer from either a high deployment cost or a low detection accuracy. To address these concerns, we propose a novel Electricity Theft Detector based upon Convolutional Long Short Term Memory neural networks, called ETD-ConvLSTM. By installing a central observer meter in each community, we can know which communities have malicious users. For these communities, users’ time series of electricity consumptions with temporal correlations are transformed into spatio-temporal sequence data, mainly by constructing a two-dimensional matrix containing both consumptions and consumption differences among several adjacent days. This matrix is then divided into a sequence of sub-matrices, which are then fed into a ConvLSTM network consisting of multiple stacked ConvLSTM layers, with each layer formed by several temporarily concatenated ConvLSTM nodes. When capturing the periodicity in users’ consumption patterns, the ETD-ConvLSTM method considers both global and local knowledge, and hence the detection accuracy improves significantly. Simulations results show that compared with existing state-of-the-art detectors, the proposed ETD-ConvLSTM method can obtain better or comparable performance in terms of detection accuracy, false negative rates and false positive rates within much shorter detection time.

Facing the dual challenges of massive access and time-sensitive traffics, grant-free non-orthogonal multiple access (GF-NOMA) emerges as a promising technology for implementing massive ultra-reliable and low-latency communications (mURLLC). In this paper, we propose a differentiated power level access (DPLA) policy that exploits the correlations among power levels of GF-NOMA, and implement DPLA by a dynamically distributed GF-NOMA framework. Further, a closed-form expression to the reliability of DPLA is analytically derived and the optimal framework parameters to maximize reliability are obtained. Finally, considering the traffic variation over time, we propose a dynamically-distributed differentiated-layered transmission $(\mathrm{D}^{3}$ LT) algorithm to improve the reliability online. Simulation results show that the proposed scheme in this work outweighs existing schemes in transmission reliability.

For accommodating rapidly increasing power demands, power systems are transitioning from analog systems to systems with increasing digital control and communications. Although this modernization brings many far-reaching benefits, the hardware and software newly incorporated into the power systems also incur many vulnerabilities. By taking advantage of these vulnerabilities, adversaries can launch various cyber/physical attacks to tamper with electricity meter readings, i.e., to steal electricity. It is reported that total worldwide annual economic losses caused by electricity theft reached up to almost one hundred billion dollars in recent years. With methods to tamper with meter readings becoming more versatile, secret, and flexible, electricity theft tends to get even more serious in modernized power systems. For preventing adversaries from stealing electricity, researchers have done a lot of works. Although some related surveys on these works exist, they are not updated or just discuss electricity theft in a specific region. This survey aims to gain a comprehensive and in-depth understanding of the electricity theft issue. After investigating how adversaries tamper with meter readings, we systematically survey all existing detection methods up to date, which is classified into machine learning- and measurement mismatch-based methods. Adverse effects and political and socioeconomic factors of electricity theft are also provided. This survey can help relevant researchers to shape future research directions, especially in the area of developing new effective electricity theft detection methods.

We formulate and investigate the problem of distributed channel rendezvous in collision-prone wireless networks. Existing researches on this topic are mainly devoted to designing channel hopping sequences, each pair of which can overlap on a common channel within bounded delay. However, this overlap-based canonical rendezvous design does not take into account channel collision, which may render existing rendezvous algorithms fail to achieve bounded delay in collision-prone environment. Motivated by this observation, we formulate and investigate the collision-aware channel rendezvous problem in a generic scenario, where a collision occurs if more than$C$packets overlap in time on a same channel. Our generic formulation allows to model both the baseline single packet reception model with$C=1$and the more sophisticated multiple packet reception model with$C > 1$. We further abstract the collision-aware rendezvous problem as the problem of constructing a robust rendezvous system. We establish the theoretical limit of the problem, guided by which we design a collision-resilient distributed rendezvous algorithm with truly bounded rendezvous delay. We then demonstrate the performance of our rendezvous algorithm both analytically and numerically.

Divergences or their counterpart (dis)similarity measures of two probability distributions play an important role in information theory. Especially, Csiszar divergences have many forms. Among so many forms of Csiszar divergences, we plan to find which divergence is the best suit of the analysis of binary sensor deployments. A binary sensor outputs a binary digit 1 or 0 when detecting an object or not, respectively. Recently, modulators made of opaque materials are utilized to modulate the sensing view of binary sensors to enhance their spatial awareness. In this paper, we construct two probability models of binary sensors modulated by modulators, i.e., an ideal model and an actual deployment model. Moreover, we utilize 13 forms of Csiszar divergences to analyze the distribution of those probability models. Based on the divergence calculation results, we classify the 13 divergences into five classifications. Furthermore, we propose a smoothing method to deal with the events which are absent in the experiments, i.e., occurring with zero probability. Our experiment results show that the smoothing method eliminates the zero probabilities and has little influence on the nonzero probabilities. Finally, we select the best divergence among the 13 divergences to analyze binary sensors modulated with modulators.

Conventional wireless automated guided vehicle (AGV) systems based on WiFi or ZigBee suffer random network performance and fail to guarantee the ordered and reliable transmission in AGV applications. In this article, we study the transmission scheduling with order constraints in WIA-FA-based AGV systems. We first present the transmission process of data packets in the WIA-FA-based AGV system, and design a novel superframe structure to support the ordered data exchange in the AGV system. Then, we design three heuristic rules for timeslot allocation to regulate the device transmissions subject to order constraints. Finally, inspired by the designed rules, we propose a dynamic expected packet loss rate-based timeslot allocation (DELTA) algorithm and prove its time complexity rigorously. The simulation results show that the proposed DELTA algorithm outperforms the existing works in terms of transmission reliability for different channel conditions and network scales.

WIA-FA and IEEE 802.11 are two most widely adopted industrial wireless standards in discrete manufacturing. However, comprehensive performance comparisons between WIA-FA and IEEE 802.11 are still missing and industrial applications urgently need experimental methods to guide the selection of appropriate wireless technologies. To this end, this article performs extensive experiments between WIA-FA and IEEE 802.11 in two practical industrial scenarios, with one ordered scenario defining the transmission order of devices and the other order-free scenario imposing no order constraints to the transmission order of devices. Network performance indices of the WIA-FA and IEEE 802.11 networks, including reliability, delay, jitter, and disorder rate, are compared for different network sizes and data generation periods. Experimental results show that the WIA-FA protocol provides stable network performance, while the network performance of the IEEE 802.11 protocol is random and uncontrollable. Additionally, we perform preliminary comparisons of WIA-FA with IEEE 802.11ax and 5G New Radio.

The increasingly wide utilization of Wireless Sensor Networks (WSNs) in industrial applications outstands the significance of the Delay Constrained Relay Node Placement (DCRNP) problem. Existing algorithms to the DCRNP problem are designed based on the ideal geometric disk wireless channel model, and no real-world deployments are performed to verify the effectiveness of these algorithms. However, the unreliable and unpredictable wireless links in WSNs may lead these algorithms to fail in practice. Therefore, we first conduct extensive real-world deployments under the guidance of existing algorithms to evaluate their performance and to gain some insights for designing practical deployment algorithms. The results exhibit that the WSNs built by existing algorithms have a favorable performance in end-to-end delay but a poor performance in reliability, which is mainly due to the lack of methods ensuring high-quality links. To this end, we first devise a Set-Covering-based Algorithm (SCA) which figures out the DCRNP problem while ensuring the quality of each link better than a given threshold. As our experiments also show that the fault-tolerant topology can significantly improve network reliability, we then design a k-Set-Covering-based Algorithm (kSCA) to build fault-tolerant WSNs based on the methodology of SCA. Furthermore, the elaborate analysis proves that both SCA and kSCA are polynomial-time algorithms, and their approximation ratios are both O(ln n), where n is the number of sensor nodes. Finally, extensive experiments are performed under the guidance of SCA and kSCA to demonstrate the effectiveness of these two algorithms.

In industrial wireless sensor networks (IWSNs), monitoring data generated by field devices are supposed to be delivered to the gateway with low latency and high reliability. However, most of industrial wireless standards are based on IEEE 802.15.4 and offer limited data rates, which prevents their adoption in critical scenarios. Based on IEEE 802.11, WIA-FA is proposed to address higher communication requirements in factory automation. In this paper, we first analyze the drawbacks of the default NACK-based retransmission scheme of WIA-FA, and then propose an automatic on-demand retransmission (AODR) scheme. Finally, we give a detailed reliability analysis of the proposed AODR scheme. Simulation results show that the proposed AODR scheme outperforms existing works in terms of reliability for different scenarios.

The centralized computing model in traditional Wireless Sensor/Actuator Networks (WSANs) can lead to large delays and unbalances, which severely restricts the adoption of WSANs in applications requiring high network performance. To address this limitation, the in-network computation model has been proposed, in which the computation capability is distributed among wireless nodes in WSANs, i.e., wireless nodes perform not only data communication but also data processing. Node placement is a primary step to build the underlaying topologies of WSANs. Nevertheless, the problem of node placement to design underlaying network topologies supporting in-network computation is still unexplored. To this end, we propose an In-network-oriented Node Placement Algorithm (INPA) to build WSANs supporting in-network computation. Moreover, we investigate the time complexity of INPA and verify the efficiency of INPA through extensive simulations.

An automatic and accurate state recognition of switches on electrical cabinet control panels plays an increasingly important role in the routine inspection of power equipment. This paper presents a novel method for real-time cabinet panel switch state recognition using hybrid visual features. Compared to traditional methods, the proposed approach can ensure the rectangular object regions from images captured at arbitrary angles by applying the perspective transformation model. Besides, the switch regions are segmented and the corresponding visual features are extracted on HSV space, instead of raw RGB space, which overcomes the illumination variability issues. The morphological operations and the inherent geometrical constraints, are employed to group the switch regions. Eventually, the switch recognition is implemented on high-dimensional vector space using feature similarity discriminants. The proposed method has been evaluated on the image dataset collected from power station cabinets. The experimental results verify the effectiveness of the method.

Integrated with cutting-edge equipment and technologies, smart grid takes prominent advantages over traditional power systems. However, hardware and software techniques also bring smart grid numerous security concerns, especially various cyberattacks. Malicious users can launch cyberattacks to tamper with smart meters anytime and anywhere, mainly for the purpose of stealing electricity. This makes electricity theft much easier to commit and more difficult to detect. Researchers have devised many approaches to identify malicious users. However, these approaches suffer from either poor accuracy or expensive cost of deploying monitoring devices. This paper aims to locate malicious users using a limited number of monitoring devices (called inspectors) within the shortest detection time. Before inspectors conduct any inspection, suspicions that users steal electricity are comprehensively assessed, mainly through analyzing prior records of electricity theft as well as deviations between the reported and predicted normal consumptions. On the basis of these suspicions, we further propose a suspicion assessment-based inspection (SAI) algorithm, in which the users with the highest suspicions will be first probed individually. Then, the other users will be probed by a binary tree-based inspection strategy. The binary tree is built according to users' suspicions. The inspection order of the nodes on the binary tree is also determined by the suspicions. The experiment results show that the SAI algorithm outperforms the existing methods.

Recently, many researchers multiplex binary infrared sensors in object tracking, habitat monitoring, and atypical behavior detection. Due to the binary digit output of binary sensors, multiplex binary sensors may lead to count the wrong number of targets when three or more targets present in the field of interest (FOI). We call this as the invisible targets' problem. To enhance the sensing results of sensors, a reference structure tomography technique is used to segment and code the FOI by modulating the sensing view of sensors. In this paper, we propose a subregion coding method to count targets moving in the FOI. Hexagon modulators are designed to make their projections segment the FOI into hexagon cells. We also propose a signature construct scheme to code cells and a encoder to count the number of targets in the FOI. Experiment results show that the accuracy to correctly count targets of our method is around 90% which is much better than 45% of a conventional method.

The limited spectrum resources inevitably incur the spectrum sharing among coexisting industrial wireless networks (IWNs), and multiple coexistence IWNs form a heterogeneous environment. An effective resource allocation thus plays a crucial role in coordinating the efficient operations of multiple IWNs. Existing works only study the constrained coexistence problem among specified types of networks with a limited number of nodes over one single channel. In this paper, we investigate a general coexistence problem over multiple channels among arbitrary types of networks with line topologies, and the number of nodes in each network is also arbitrary. We rigorously analyze theoretical scheduling latency of this general coexistence problem, then we propose an algorithm to attain the optimal result. The presented Coexisting Line topology Networks Resource Allocation (CLNRA) algorithm consists of two phases. In the inter-network resource allocation phase, non-overlapped channels are allocated to each network according to the corresponding transmission priority. While in the intra-network resource allocation phase, we filter out the nodes that may generate continuous empty buffers so as to enhance the resource utilization ratio. We also verify the effectiveness of the CLNRA algorithm through extensive simulations. Evaluation results show that the CLNRA algorithm can attain the theoretical optimal result in 99:3% cases, and it has obvious superiorities on resource utilization ratio and scheduling latency.

In industrial wireless sensor networks (IWSNs), monitoring data generated by field devices should be delivered to the gateway prior to deadlines. Traditional field devices with one radio interface can only work in the half-duplex mode, which may cause severe degradation of the network real- timeliness. Considering the scenarios where each field device is with multiple radio interfaces, we study the joint scheduling of slots, channels and radio interfaces in IWSNs with mesh topologies. Specifically, a new method to calculate the total and remaining resource blocks of each transmission is first given. Then, a two-level priority assignment rule is designed by jointly considering remaining resource blocks and deadlines. Finally, a remaining resource blocks based least laxity first (RRBs-LLF) algorithm based on the above rule is proposed. Simulation results show that the proposed RRBs-LLF algorithm outperforms existing works in terms of schedulable ratio.

Clustering is an effective method to manage communications in cognitive radio sensor networks (CRSNs). This letter proposes a network stability-aware clustering (NSAC) protocol for CRSNs. Spectrum dynamics and energy consumption are for the first time simultaneously integrated into the protocol design of NSAC. Extensive simulations show that the proposed NSAC protocol obviously outperforms existing methods in the aspects of network stability and energy consumption.

Intelligent factory automation systems strongly rely on industrial wireless control networks which have to ensure timely and reliable data exchange among their components. This paper presents a comprehensive survey on recently approved International Electrotechnical Commission standard Wireless networks for Industrial Automation-Factory Automation (WIA-FA). This paper first introduces the system architecture of WIA-FA including network device, network topology, and system management, and then illustrates WIA-FA protocol stack and key technologies. Furthermore, two WIA-FA testbeds are described to demonstrate the high performance of WIA-FA. After that, three examples of practical applications are provided in this paper. One application deploys a WIA-FA network to monitor and control industrial robots in a digital workshop. The second application adopts the deployment of WIA-FA as a real-time wireless network that connects automated guided vehicles (AGVs) in a logistic sorting system. The last application coordinates multiple cooperative AGVs via the WIA-FA network to carry large and complex components. Finally, the open issues and future directions for WIA-FA networks are presented.

Electricity theft is a widespread problem that causes tremendous economic losses for all utility companies around the globe. As many countries struggle to update their antique power systems to emerging smart grids, more and more smart meters are deployed throughout the world. Compared with analog meters which can be tampered with by only physical attacks, smart meters can be manipulated by malicious users with both physical and cyber-attacks for the purpose of stealing electricity. Thus, electricity theft will become even more serious in a smart grid than in a traditional power system if utility companies do not implement efficient solutions. The goal of this paper is to identify all malicious users in a neighborhood area in a smart grid within the shortest detection time. We propose an adaptive binary splitting inspection (ABSI) algorithm which adopts a group testing method to locate the malicious users. There are two considered inspection strategies in this paper: a scanning method in which users will be inspected individually, and a binary search method by which a specific number of users will be examined as a whole. During the inspection process of our proposed scheme, the inspection strategy as well as the number of users in the groups to be inspected are adaptively adjusted. Simulation results show that the proposed ABSI algorithm outperforms existing methods.

The light-weight thermal-RGB-inertial sensing units are now gaining increasing research attention, due to their heterogeneous and complementary properties. A robust and accurate registration between a thermal-RGB camera and an inertial sensor is a necessity for effective thermal-RGB-inertial fusion, which is an indispensable procedure for reliable tracking and mapping tasks. This paper presents an accurate calibration method to geometrically correlate the spatial relationships between an RGB camera, a thermal camera and an inertial measurement unit (IMU). The calibration proceeds within the unified calibration framework (thermal-to-RGB, RGB-to-IMU). The extrinsic parameters are estimated by jointly optimizing both the chessboard corner reprojection errors and acceleration and angular velocity error terms. Extensive evaluations have been performed on the collected thermal-RGB-inertial measurements. In this experiments study, the average RMS translation and Euler angle errors are less than 6 mm and 0.04 rad respectively under 20% artificial noise.

Cognitive radio has been widely recognized as a promising solution to reliable and time-efficient wireless sensor networks. However, cognitive capability requires an extra energy consumption in spectrum sensing and spectrum access, which imposes a rather challenging problem to low cost sensors. This paper proposes a short preamble cognitive medium access control (SPC-MAC) protocol which supports reliable and fast spectrum access while addressing the energy conservation problem in cognitive radio sensor networks (CRSNs). The novelty of SPC-MAC lies in the combination of short preamble sampling (for supporting low duty cycling in CRSNs) and the opportunistic forwarding (for reliable and fast transmission). Because of the self-organizing nature, SPC-MAC does not require a common control channel. Extensive simulations demonstrate the advantage of SPC-MAC over existing works in terms of energy consumption and throughput.

Wireless Sensor Networks (WSNs) are gradually employed in many applications requiring real-time data transmission. As hop count is an important factor affecting end-to-end delay, in this paper, we investigate the Hop Constrained Relay Node Placement (HCRNP) problem where at least one path fulfilling the hop constraint is built between each Sensor Node (SN) and the sink. To address this problem, we present a Cover-based Relay Node Placement (CRNP) algorithm which places Relay Nodes (RNs) from SNs to the sink. Through formulating the deployment of RNs in each iteration as a cover problem (the set cover problem for arbitrary settings or the discrete unit disk cover problem for special settings) with respect to hop constraint, the CRNP algorithm iteratively deploys RNs adjacent to the SNs or the previously placed RNs so as to gradually connect SNs to the sink. Through rigorous analysis, we show that the CRNP algorithm has an approximation ratio better than existing algorithms for the HCRNP problem (i.e., O(1) for special settings and O(ln n) for arbitrary settings, where n is the number of SNs). Finally, we conduct extensive simulations to verify the effectiveness of the proposed algorithm.

This article studies a green-energy-powered cognitive radio network (GCRN) in an underlay paradigm, wherein multiple battery-free secondary users (SUs) capture both the spectrum and the energy of primary users (PUs) to communicate with an access point (AP). By time division multiple access, each SU transmits data to AP in the allocated time and harvests energy from the RF signals of PUs otherwise, all in the same licensed spectrum concurrently with PUs. Thus, the transmit power of each SU is jointly constrained by the peak interference power at PU and the harvested energy of SU. With the formulated green coexistence paradigm, we investigate the sum-throughput maximization problem with respect to time and power allocation, which is non-convex. To obtain the optimal resource allocation, we propose a joint optimal time and power allocation (JOTPA) algorithm that first transforms the original problem into a convex optimization problem with respect to time and energy allocation, and then solve it by iterative Lagrange dual decomposition. To comprehensively evaluate the performance of the GCRN with JOTPA, we deploy the GCRN in three typical scenarios and compare JOTPA with the equal time and optimal power allocation (ETOPA) algorithm. Extensive simulations show that the deployment of the GCRN significantly influences the throughput performance and JOTPA outperforms ETOPA under all considered scenarios.

The increasing demand for health informatics has become a far-reaching trend in the ageing society. The utilization of wearable sensors enables monitoring senior people daily activities in free-living environments, conveniently and effectively. Among the primary health-care sensing categories, the wearable visual-inertial modality for human motion tracking gradually exerts promising potentials. In this paper, we present a novel wearable heading estimation strategy to track the movements of human limbs. It adaptively fuses inertial measurements with visual features following locality constraints. Body movements are classified into two types: general motion (which consists of both rotation and translation). or degenerate motion (which consists of only rotation). A specific number of feature correspondences between camera frames are adaptively chosen to satisfy both the feature descriptor similarity constraint and the locality constraint. The selected feature correspondences and inertial quaternions are employed to calculate the initial pose, followed by the coarse-to-fine procedure to iteratively remove visual outliers. Eventually, the ultimate heading is optimized using the correct feature matches. The proposed method has been thoroughly evaluated on the straight-line, rotatory and ambulatory movement scenarios. As the system is lightweight and requires small computational resources, it enables effective and unobtrusive human motion monitoring, especially for the senior citizens in the long-term rehabilitation.

Combining light-weight visual and inertial modalities for motion capturing has been popular in robotics researches. There exist scale ambiguity, inaccurate pose estimation with little or no baseline, incremental drifts over time in visual-inertial fusion. Thus, in this paper, we propose a robust motion capturing method based on the multi-joint kinematic chain using monocular visual-inertial sensors. Our method is able to recover monocular visual scale through the joint geometry constraint. Additionally, we take inertial pre-integration to assist visual outlier removal using Maximum A Posteriori method. Ultimately, the kinematic chain model is leveraged to constrain the associated multiple visual-inertial estimation drifts during long time tracking. In the experiments, we conduct multi-joint capturing on a robotic arm. The quality of motion reconstruction is evaluated by comparing the estimated results with the measurements from an optical motion tracking system OptiTrack.

The lifetime of Wireless Sensor Networks (WSNs) is significantly shortened by the energy hole problem that is caused by the many-to-one communication pattern adopted by most WSNs. Various approaches have been designed to solve the energy hole problem, and this paper considers improving the energy efficiency by deploying additional relays, which is called the Lifetime Constrained Relay Node Placement (LCRNP) problem. To address the NP-hardness of the LCRNP problem, this paper proposes a Cluster-based Approximation Algorithm (CAA) that first groups the sensors into different clusters in which the lifetime constraint can be ignored and sensors are close to each other, and then builds network connectivity for each cluster. Next, the Augmented CAA is designed based on the CAA to further improve network lifetime by building addition paths for the relays prone to suffer heavy traffic loads. Unlike existing works, we prove that the proposed algorithms can guarantee polynomial time complexities and explicit approximation ratios. Finally, the efficiency of the proposed algorithms is verified through extensive simulations.

As the smart meters are vulnerable to physical attacks as well as cyber attacks, electricity theft in smart grids is much easier to commit and more difficult to detect than that in traditional power grids. In this paper, to facilitate the inspection of the malicious meters, a full and complete binary inspection tree whose leaves stand for smart meters is employed as a logical structure. We can logically configure an inspector (a meter for detection) at any node on the tree. By calculating the difference between the inspector’s reading and the summation of the readings reported from the smart meters on the subtree of one node, as well as the difference between the total amount of stolen electricity on the subtrees of an internal node and its left child, we propose a difference-comparison-based inspection algorithm which allows the inspector to skip a large number of nodes on the tree and hence accelerates the detection speed of the malicious meters remarkably. Furthermore, for quickly identifying a complete set of malicious meters, we propose an adaptive reporting mechanism which adopts much shorter reporting periods during the inspection process. Analysis with proofs about the performance bounds of the proposed algorithm in terms of the number of inspection steps is provided. Simulations not only validate the theoretical analysis, but also show the superiority of the proposed algorithm over the existing works in terms of inspection steps, regardless of the ratio and the permutation of malicious meters.

This paper studies a green paradigm for the underlay coexistence of primary users (PUs) and secondary users (SUs) in energy harvesting cognitive radio networks (EH-CRNs), wherein battery-free SUs capture both the spectrum and the energy of PUs to enhance spectrum efficiency and green energy utilization. To lower the transmit powers of SUs, we employ multi-hop transmission with time division multiple access, by which SUs first harvest energy from the RF signals of PUs, and then, transmit data in the allocated time concurrently with PUs, all in the licensed spectrum. In this way, the available transmit energy of each SU mainly depends on the harvested energy before the turn to transmit, namely energy causality. Meanwhile, the transmit powers of SUs must be strictly controlled to protect PUs from harmful interference. Thus, subject to the energy causality constraint and the interference power constraint, we study the end-to-end throughput maximization problem for optimal time and power allocation. To solve this nonconvex problem, we first equivalently transform it into a convex optimization problem and then propose the joint optimal time and power allocation (JOTPA) algorithm that iteratively solves a series of feasibility problems until convergence. Extensive simulations evaluate the performance of EH-CRNs with JOTPA in three typical deployment scenarios and validate the superiority of JOTPA by making comparisons with two other resource allocation algorithms.

As Wireless Sensor Networks (WSNs) are widely used in time-critical applications, e.g., factory automation and smart grid, the importance of Delay Constrained Relay Node Placement (DCRNP) problem is becoming increasingly noticeable. This paper proposes a Set-Covering-based Approximation (SCA) algorithm to solve the DCRNP problem. The SCA deploys relay nodes by levels from the sink to sensor nodes. To avoid the limitation suffering by existing algorithms and ensure a polynomial time complexity, SCA employs a novel approach to formulate the deployment of relay nodes at each level as the set covering problem subject to delay constraints, and based on the classic greedy-set-covering algorithm, a set of relay nodes are placed to connect the nodes (sensor nodes and relay nodes) that are already connected to the sink. Since delay constraints are met at each level, all the sensor nodes will be connected to the sink via feasible paths fulfilling delay constraints. In addition, the elaborated analysis of the time complexity and the approximation ratio of the SCA algorithm is given out. Extensive simulations show that SCA can significantly save deployed relay nodes in comparison to existing algorithms.

This paper presents an orientation estimation methods using inertial cues (IMU) and visual feature constraint. Our proposed approach combines both of these two modalities in an original way. Two feature-point correspondences between consecutive frames are firstly selected that not merely meet the requirement of descriptor similarity constraint but the locality constraint. Secondly, these two selected correspondences together with inertial quaternions are jointly employed to derive the initial body pose. Thirdly, a coarse-to-fine procedure proceeds in removing visual false matches and in estimating body poses iteratively using the Posteriori Bayes Rule and Expectation Maximization. Eventually, the optimal orientation is estimated via the iteratively selected visual inliers. Experimental results validate that our proposed strategy is effective and accurate in orientation estimate.

Channel rendezvous is a prerequisite for secondary users (SUs) to set up communications in cognitive radio networks (CRNs). It is expected that the rendezvous can be achieved within a short finite time for delay-sensitive applications and over all available channels to increase the robustness to unstable channels. Some existing works suffer from a small number of rendezvous channels and can only guarantee rendezvous under the undesired requirements such as synchronous clock, homogeneous available channels, predetermined roles and explicit SUs' identifiers (IDs). In this paper, to address these limitations, we employ the notion of Disjoint Set Cover (DSC) and propose a DSC-based Rendezvous (DSCR) algorithm. We first present an approximation algorithm to construct one DSC. The variant permutations of elements in the ingeniously constructed DSC are then utilized to regulate the order of accessing channels, enabling SUs to rendezvous on all available channels within a short duration. We derive the theoretical maximum and expected rendezvous latency and prove the full rendezvous degree of the DSCR algorithm. Extensive simulations show that the DSCR algorithm can significantly reduce the rendezvous latency compared to existing algorithms.

This study formulates a novel optimisation problem for joint spectrum sensing and random access control ( JS 2 RAC ) in cognitive radio sensor networks (CRSNs). The JS 2 RAC is formulated as a network utility maximisation problem, which aims to maximise the sum of utilities over all links in the network but subject to the primary user protection constraint, the energy constraint, and the physical constraint. Due to the non‐separable and non‐convex nature of the JS 2 RAC problem, the authors propose a primal‐decomposition‐based iterative (PDI) algorithm which decomposes the JS 2 RAC problem into a spectrum sensing subproblem and a random access control subproblem, and solve the two subproblems iteratively. Then, the authors prove the convergence of the PDI algorithm and show its distributed implementation in practice. Simulations demonstrate the fast convergence and the near‐optimal nature of the PDI algorithm and show its significant improvement in the network utility of CRSNs in comparison with the method of optimising spectrum sensing and random access separately.

To detect the malicious meters committing electricity theft in a neighborhood area smart grid, in this paper, a novel inspection algorithm, termed as the Binary-Coded Grouping-based Inspection (BCGI) algorithm, is proposed. In the proposed algorithm, each meter is identified with a unique binary-coded number. The BCGI algorithm can locate the unique malicious meter (if any) by one inspection step under the assumption that at most one meter becomes malicious in one reporting period. Furthermore, by controlling the reporting periods of meters, we could make the probability of the event that at most one meter becomes malicious in one reporting period arbitrarily close to 1 under some assumptions. We further extend the algorithm into a Generalized BCGI algorithm (G-BCGI) to deal with the case that there are two or more meters which happen to commit the theft of electricity in one reporting period. Simulation results demonstrate the inspection efficiency of the BCGI and G-BCGI algorithms.

In this paper, we explore the malicious meter inspection (MMI) problem in neighborhood area smart grids. By exploiting a binary inspection tree, we propose a Difference-Comparison-based Inspection (DCI) algorithm to quickly target the malicious meters. Different from existing algorithms, the DCI algorithm is designed based on three rules that are derived according to the difference comparison results in each local subtree. An attractive feature of the DCI algorithm is that it manages to skip a large number of nodes on the binary inspection tree and thus accelerates the detection of malicious nodes. Both analysis and simulation results show that DCI outperforms the existing inspection algorithms in terms of inspection speed, regardless of the ratio and permutation of malicious meters.

Rendezvous is a vital step for secondary users who want to initiate a communication in cognitive radio networks. In this paper, we propose a novel Padded-Dyck-Path-based (PDP) rendezvous algorithm that generates channel hopping sequences utilizing global channels. PDP is designed according to the roundabout Dyck path so as to increase rendezvous opportunities. As the global channels may not be shared in distributed environments, we also propose a local PDP (L-PDP) heterogeneous rendezvous algorithm that generates channel hopping sequences utilizing only local available channels. L-PDP can significantly reduce rendezvous latency and allow for distributed implementations. We prove that both PDP and L-PDP can provide guaranteed rendezvous and derive their upper bounds of rendezvous latency. Analytical and simulation results show that PDP and L-PDP outperform existing algorithms in terms of time-to-rendezvous in global and local scenarios, respectively.

In two-tiered Wireless Sensor Networks (WSNs) relay node placement considering resource constraints and high overhead of the relay nodes plays a key role in extending the network lifetime. Therefore, approaches that support fewer relay nodes are desired to cover the WSNs. In this paper, we formulate the relay node placement problem as a Geometric Disc Covering (GDC) problem, and propose a novel local search approximation algorithm (LSAA) to solve the GDC problem. In the proposed LSAA, the sensor nodes are allocated into independent groups and then a Set Cover (SC) for each group is performed. The set of the SC for each group constitutes a SC of the GDC problem. LSAA is extensively investigated and analyzed by rigorous proof and the simulation results presented in this paper clearly demonstrate that the proposed LSAA outperform the approaches reported in literature in the reduction in deployed relay nodes.

Increased mobility coupled with a possible reduction of cabling costs and deployment time makes wireless communication an attractive alternative for the industrial process monitoring and control. The major obstacles towards the utilisation of wireless industrial systems are predominantly the timing and reliability requirements. In this study, the authors take jointly the timing and reliability requirements, limited wireless resources and the cyclic data feature into consideration, and study the performance bounds and two‐stage time‐ and channel‐optimal convergecast scheduling algorithms for wireless industrial systems with hierarchical star and mesh architecture. Specifically, they consider the convergecast communication for wireless industrial systems operating according to the recent wireless network for industrial automation–process automation standard; and they will provide bounds on the minimum convergecast schedule length and bounds on the minimum number of channels for cluster‐line and cluster‐tree routing structures. In both cases, they propose time‐ and channel‐optimal two‐stage scheduling algorithms. They evaluate the author's two‐stage scheduling algorithms by both simulation and real hardwares. Numerical results demonstrate that their algorithms are efficient compared with traditional time division multiple access‐based convergecast scheduling algorithms.

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.

Supervisory control and data acquisition (SCADA) systems are widely used to monitor and control industrial processes. They provide the key functionality of real-time monitoring, logging/archiving, report generation, and automation for smart grid, which is a promising power delivery system for the near future. On the basis of these functionalities, various SCADA architectures, including hardware and software architecture, have been proposed and standardized; however, the most open and expediently growing areas in the smart grid are the infrastructure and technologies for the SCADA communication and security. In this paper, we provide a review for many documented standards in SCADA, and we also review its state-of-the-art communication and security aspects. Copyright © 2013 John Wiley & Sons, Ltd. We provide a review for many documented standards in SCADA, and we also review its state-of-the-art communication and security aspects.

The shared‐medium nature and complex wireless environment of wireless sensor networks (WSNs) poses fundamental challenges to the design of effective transmission scheduling algorithms that are optimised with respect to superframe length and reliability. In this study, the authors propose an adaptive and reliable transmission scheduling algorithm for WSNs based on low‐cost estimation of channel states. The authors establish a hierarchical scheduling framework on global centralised timeslot scheduling and local distributed channel scheduling. On the one hand, global centralised timeslot scheduling aims to guarantee global optimality of resource allocation, during which a mathematical reliability model is built to avoid resource waste by the stationary allocation method and improve the reliability of packet transmission. On the other hand, local distributed channel scheduling shares the responsibility of resource allocation. During channel scheduling, the channel model is constructed by the dynamic programming method and takes both probing cost and channel quality into consideration, which alleviates the uncertain and time‐varying interference and overcomes the blindness of traditional methods. In contrast with previous works that do not consider link reliability and channel probing cost and often assume two channel states, the scheduling algorithm performs reliably for an arbitrary number of channels and arbitrary number of channel states. Extensive simulations and experiments under a variety of network environments have been conducted to validate our theoretical claims.

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 utility and network lifetime for energy-constrained wireless sensor networks (WSNs). By introducing a weighted factor, we combine these two objectives into a single weighted objective, and we consider rate control and routing in this tradeoff framework simultaneously. First, by using a dual decomposition method, we decompose the tradeoff model into two subproblems: the congestion control/routing problem and the network lifetime problem, both of which interact through the dual variables for energy dissipation constraints. Based on the decomposition results, we propose a fully distributed algorithm to solve these two sub-problems and the dual problem by using gradient and sub-gradient projection methods. Second, we propose a fully distributed algorithm by approximating the network lifetime maximization problem by using the network utility maximization (NUM) framework. Third, we extend our distributed algorithm to deal with reliable communication and the real-time requirement. Rigorous analysis and simulations are presented to validate our algorithms.

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.

Abstract In this paper, based on a distributed particle filter, two tracking algorithms are proposed for tracking mobile targets in cluster‐based underwater sensor networks (USNs). Both tracking algorithms run local particle filter sequentially at each cluster along target trajectories, but they adopt different methods of selecting measurements from sensor nodes to balance the information contribution against the cost. Performance metrics are proposed and discussed in terms of tracking performance, communication cost, energy cost, and tracking response time. Simulations are conducted to quantitatively compare the proposed algorithms as well as another tracking algorithm based on extended Kalman filter (EKF). Our results indicate that one tracking algorithm achieves higher tracking accuracy while the other achieves dramatic reduction of communication cost, energy cost, and tracking response time. Furthermore, performance of two tracking algorithms has been studied in terms of detection threshold and sensor density. Copyright © 2008 John Wiley & Sons, Ltd.