Localization For The Nodes Of Wireless Sensor Network

Wireless sensor network(WSN) is composed of a large number of sensor nodes, which can real-time monitor and acquire physical information in the distribution detection area, has gained a widely attention in recent years, and has been applied to the industry, the military,remote health, smart home, environmental monitoring and other fields. According to most of the applications in WSN, the information without space and time data is unvalued. In order to better monitor and track the target, the location of sensor node is one of the most important information. In recent years, localization of wireless sensor network node has become a popular and significant issue.The paper analyzes the advantages, disadvantages and the existing problems of Amorphous algorithm and MCL(Monte Carlo Localization) algorithm. Then Amorphous static node localization algorithm based on BP(Back Propagation) artificial neural network and PSOMCL mobile node localization algorithm based on PSO(Particle Swarm Optimization)are proposed in the paper. The main research contents and results are as follows:(1)Amorphous static node localization algorithm based on BPThe classical Amorphous algorithm will produce large localization error in the process of localization. In order to solve the problem, an improved localization algorithm is proposed in this paper to reduce the localization error. The improved algorithm introduces the received signal strength threshold to modify the minimum hop from the unknown node to the beacon node, then BP algorithm is introduced to optimize the threshold and reduce the localization error. The simulation results show that the improved algorithm is better than other algorithms in the localization accuracy and energy consumption, and it is simple to achieve in the hardware.(2)PSOMCL mobile node localization algorithm based on PSOBecause sampling efficiency of MCL algorithm is low and the localization accuracy is not high, PSOMCL mobile node localization algorithm based on PSO is proposed in this paper to solve the problems. Firstly, the motion model of mobile node and MCL algorithm can be used to predict the estimated location of the mobile node. The correction function of location will be determined by the distance from the estimated location to anchor node and the measuring distance from the node to anchor node. Then PSO algorithm is used to optimize the correction function of location, and the optimal location of the mobile node will be get.The improved algorithm is easy to achieve, at the same time it improves the localization accuracy with low energy consumption.