Location Algorithm Optimization For Wireless Sensor Network

As a new network technology, wireless sensor network (WSN) has received increasing attention in academic and industry field, which has wide applications in the aspects of industry, military, and environment. As one of the key enabling technologies and research hotspots, the nodes localization is very important significance due to its direct correlation with theoretical study and practical application.Self-localization of sensor node is to get the position information of the node by using communication of some nodes that already know itself position. There are still lots of shortcomings in the localization technology such as great impacted by circumstances, high algorithm complexity and huge energy-cost, unsuitable for mobile sensor network.This paper focuses on the localization optimization methods for WSN, aims to obtain accurate location information, lower energy consumption and comprehensive performance optimization of positioning system.Firstly, the structure, the main characteristics and the application of WSN are discussed comprehensively in this paper; the characteristic and the sensor node location technology of WSN indoor location system are studied systematically; the location algorithems of typical indoor location system are analyzed thoroughly; the location technology based on ultrasonic-radio signal is researched systematically.The intelligent optimization algorithms are applied to solve the node location problem. Specifically, the simulated annealing based location (SA-L) algorithm can obtain good location performance with low computational complexity. Moreover, an improved particle swarm optimization based location (PSO-L) algorithm is proposed. Considering the two dimensional characristic of node position, the Euclid distance is adopted to adjust the velocity update formula of particles, which makes the particals always approach to the optimum ones quickly, thus achieve the node position with high precision. Simulation results demonstrated that these algorithms can obtain significant location performance with low computational complexity, hence have good serviceability.