A Coverage Control Method Based On Target Trajectory Prediction In Wireless Sensor Network

With the development of sensor technology, microelectronics technology and embedded computing technology, wireless sensor networks lead us to a new area of computing revolutionary, which has wide application prospects in many important fields (such as military defense, biological medicine, emergency rescue and disaster relief, urban management, environmental monitoring, dangerous area remote control). Wireless sensors set up a bridge between real world and virtual world, after analyzing and processing related information which is gathered from monitored targets, user policies are issued to the corresponding sensor nodes. In goal monitoring systems, most methods are lack of extent in theoretical study which causes a great extent influence the application of the wireless sensor network.This paper focuses on the prediction of target trajectory and the coverage. The main work of this paper is as follows:In target trajectory prediction, with the method of data mining, a specific target trajectory prediction algorithm based on mobile pattern matching is proposed, which is called PS-Tree algorithm. In this algorithm, historical data of targets’ movements generated in monitored region are used for pattern mining, coordinate series of targets are converted to region trajectory series, and frequent moving modes are figured out by building up PS-tree. The algorithm matches current trajectories with the ones in pattern library to forecast the movements of the target when a target moves into monitored region. The simulation results prove that PS-Tree algorithm has low time and space consumption but high prediction accuracy. As with target coverage control method, a new incomplete coverage control based on target tracking sensor network which called mobile-constrained optimal target tracking coverage control algorithm is presented. In our approach, mobile sensors collaborate with static sensors to achieve an optimal coverage which based on a target trajectory prediction model. Simulation results show that, MCOTT has more advantages like good robustness, high level of target coverage, low energy consumption. The algorithm can save the number of sensors and prolong the network lifetime effectively.