Sensor nodes deployment is very crucial for wireless sensor networks (WSNs). Current methods are apt to enlarge the coverage by achieving a nearly even deployment with similar density in the whole network. However, in some specific applications, the even distribution may not satisfy the sensing requirements. This paper proposes a virtual force directed coevolutionary particle swarm optimization (VFCPSO) algorithm, which uses a combined objective function to achieve the tradeoff of coverage and energy consumption. By considering deployment as an optimization problem, VFCPSO is more reliable and flexible for WSNs, since it can satisfy the combined requirements instead of only enlarging coverage. For investigating the performance of different paradigms, centralized VFCPSO is extended to distributed VFCPSO, heterogeneous hierarchical VFCPSO and homogeneous hierarchical VFCPSO (Homo-H-VFCPSO), and the solution of preferential deployment in interested region is also analyzed. Simulation results show that the Homo-H-VFCPSO has the best performance, i.e., it is more efficient than other three VFCPSO algorithms and the VF-style algorithms in terms of computation time, coverage and efficient moving energy consumption. It is obvious that the Homo-H-VFCPSO has good global searching ability and scalability, and it can rapidly and effectively achieve the sensor nodes deployment in WSNs.

Energy constraint is an important issue in wireless sensor networks. This paper proposes a distributed energy optimization method for target tracking applications. Sensor nodes are clustered by maximum entropy clustering. Then, the sensing field is divided for parallel sensor deployment optimization. For each cluster, the coverage and energy metrics are calculated by grid exclusion algorithm and Dijkstra's algorithm, respectively. Cluster heads perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric. Particle filter is improved by combining the radial basis function network, which constructs the process model. Thus, the target position is predicted by the improved particle filter. Dynamic awakening and optimal sensing scheme are then discussed in dynamic energy management mechanism. A group of sensor nodes which are located in the vicinity of the target will be awakened up and have the opportunity to report their data. The selection of sensor node is optimized considering sensing accuracy and energy consumption. Experimental results verify that energy efficiency of wireless sensor network is enhanced by parallel particle swarm optimization, dynamic awakening approach, and sensor node selection.

Target tracking is an important task addressed in wireless sensor network (WSN), because the resources of WSN are limited. A distributed lightweight particle filter algorithm is proposed to achieve robust target tracking with low resource requirement in the WSN consisting of acoustic sensor nodes. In the proposed algorithm, each sensor node carries out partial particle filter with its local data and the neighbor nodes' data in distributed manner. Then local results of selected sensor nodes are fused to make final decision. To simplify the computation of particle filter, lightweight sampling and resampling schemes are introduced, where a simple range-free algorithm is adopted to restrict potential area of target's location. The experimental results verify that the proposed distributed lightweight particle filter algorithm can effectively achieve target tracking in WSN with low resource consumption. Compared to previous target localization algorithms, such as maximum likelihood estimation and centralized particle filter, the proposed algorithm has outstanding performance in accurate target tracking and low resources consumption.

A wireless sensor network (WSN) is a powerful unattended distributed measurement system, which is widely used in target surveillance because of its outstanding performance in distributed sensing and signal processing. This paper introduces a multiview visual-target-surveillance system in WSN, which can autonomously implement target classification and tracking with collaborative online learning and localization. The proposed system is a hybrid system of single-node and multinode fusion. It is constructed on a peer-to-peer (P2P)-based computing paradigm and consists of some simple but feasible methods for target detection and feature extraction. Importantly, a support-vector-machine-based semisupervised learning method is used to achieve online classifier learning with only unlabeled samples. To reduce the energy consumption and increase the accuracy, a novel progressive data-fusion paradigm is proposed for online learning and localization, where a feasible routing method is adopted to implement information transmission with the tradeoff between performance and cost. Experiment results verify that the proposed surveillance system is an effective, energy-efficient, and robust system for real-world application. Furthermore, the P2P-based progressive data-fusion paradigm can improve the energy efficiency and robustness of target surveillance.

Rotating machinery anomaly detection is of paramount significance for industries to prevent catastrophic breakdown and improve productivity and personnel safety. The kernel classifier support vector machine (SVM) has shown excellent performance towards this purpose, but it is difficult to optimize relevant hyper-parameters. In this paper, we propose a new anomaly detection approach by merging Gaussian process classifiers (GPCs) and bootstrap methods. GPCs are Bayesian probabilistic kernel classifiers and provide a well established Bayesian framework to determine the optimal or near optimal kernel hyper-parameters. They are largely unexplored for anomaly detection applications; consequently we take the initiatives to investigate GPCspsila performance in these scenarios. Bootstrap methods are incorporated to improve GPCspsila performance for small machinery anomaly samples by resampling at random. The proposed approach is evaluated on a motor testbed and wavelet packet is utilized to perform vibration analysis. Experiment results show bootstrap GPCs are highly effective and outperform GPCs and SVM with cross validation for anomaly detection. Moreover GPCs also prove to outperform SVM. Thus the proposed approach is promising for rotating machinery anomaly detection.

To implement visual target classification, this paper proposes a collaborative statistical learning algorithm for online support vector machine(SVM) classifier learning in wireless multimedia sensor network (WMSN). For achieving robust target classification, classifier learning should be carried out iteratively for updating classifiers according to various situations. Because only unlabeled samples can be acquired, semi-supervised learning is desired to make full use of unlabeled samples. According to the restrict limitation in energy and bandwidth, the proposed algorithm incrementally implement classifier learning with the selected features from multiple sensor nodes, where rough set based feature reduction is used for retaining most of the intrinsic information. Furthermore, some metrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes, and a sensor node selection strategy is also proposed to reduce the impact of inevitable missing detection and false detection. Experimental results demonstrate that the collaborative statistical learning algorithm can effectively implement target classification in WMSN. With the rough set based feature reduction, the proposed algorithm has outstanding performance in energy efficiency and time cost.