Extracting Target Detection Knowledge Based on Spatiotemporal Information in Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) have been deployed for many applications of target detection, such as intrusion detection and wildlife protection. In these applications, the first step is to detect whether the target is present or not. However, most of the existing work uses the "simple disk model" as signal model, which may not capture the sensing environment. In this work, we utilize a more realistic signal model to describe sensing process of sensors. On the other hand, the "majority rule" is widely used to make the final decision, which may not obtain the true judgment. To this end, we utilize a more realistic signal model and also use a probabilistic decision model to make the final decision. Moreover, we propose a probabilistic detection algorithm in which all sensors' local measurement values are fully used. This algorithm does not need any artificial threshold compared with traditional algorithms. It makes the most of spatiotemporal information to obtain the final decision. For the spatial perspective, sensors are distributed in different locations cooperating with each other. Meanwhile, for the temporal perspective, multiround subdecisions are fused. The effectiveness of the proposed method is validated by extensive simulation results, which show high detection probabilities and low false alarm probabilities.

Serial distributed detection for wireless sensor networks with sensor failure

“…Authors in [22] study a binary tree topology, where the sensor nearest the target serves as the root. Authors in [23,24] think there are several "target spots" in the ROI where the target often appears, then analyze the area centered at the "target spot" and propose a target detection …”

Target Detection Coverage Algorithm Based on 3D-Voronoi Partition for Three-Dimensional Wireless Sensor Networks

“…us, we can organize the movement of the sensor nodes through self-deployment (e.g., by humans or mobile robots) [1]. In recent years, WSNs have witnessed many applications, such as target detection [3], target location [4], healthcare monitoring [5], and data collection [6]. Research on WSNs coverage is mainly classified into three branches: area coverage, barrier coverage, and target event coverage.…”

“…Hence, how to reduce the energy consumption of the sensor nodes and improve the coverage ratio of the network and the reliability of the algorithm are critical factors to be considered. In the literature [3], a more realistic signal detection model has been developed to obtain information on the target event and make the final decision by using a probabilistic decision model. Furthermore, a probabilistic detection algorithm [3] has been proposed to exploit the local measurements collected by the sensor nodes and improve the utilization rate of the nodes.…”

“…In the literature [3], a more realistic signal detection model has been developed to obtain information on the target event and make the final decision by using a probabilistic decision model. Furthermore, a probabilistic detection algorithm [3] has been proposed to exploit the local measurements collected by the sensor nodes and improve the utilization rate of the nodes. In addition, a tracking framework based on Voronoi Tessellations has been proposed [10], whereby two mobility models are designed to control the coverage degree according to the existence of the target.…”

Adaptive Threshold Selection for Collaborative Target Tracking

“…In [5], a hierarchical decision fusion scheme is proposed to improve network-wide detection probability. Based on the signal energy and Gaussian noise, sensor node chooses its threshold, and improves the detection accuracy [6]. However, if the chosen threshold is too small, it is easy to appear false alarms.…”