Research On Mobile Nodes Localization Algorithm For Wireless Sensor Network In Fever Anchor Nodes Environment

The process of each sensor node certainly the location information they explicitly located in the wireless sensor networks that called node located. It can effectively carry out the basic preconditions for WSN to work is to accurately determine the exact location of work nodes and where events occur,from a practical point of view.Current technology for node localization studies, mainly concentrated in the static WSN network, that node once deployed within the network remains in the stationary state. In practical applications, if the nodes in the network can be containing in a random walk states, the object is able to be more flexible monitoring events across the network to provide more real-time information with the elements, such as the positioning of coal mine workers, positioning within the ranch animals. Therefore,the research on mobile wireless sensor networks node localization algorithm is necessary.This paper describes the importance of the research background of wireless sensor network node localization technology,and then detail described the positioning technology of WSN,and introduces the concept of mobile sensor network node localization, and detail presentation the application of the principle of sequential Monte Carlo method in a mobile sensor network node localization, and also combined with relevant localization algorithm to illustrate the current Research.Due to current Monte Carlo algorithms for wireless sensor network is low localization efficiency under low anchor node density,proposed a Sparse Distributed Anchor Node Monte Carlo Boxed localization algorithm(SDANMCB).The main idea of the algorithm can be embodied by the following aspects:( 1) In localization process,use the mobile node with high positioning precision turn to virtual anchor node to assist other nodes to achieve their position, and according to the actual position error of virtual anchor nodes to introduce the expansion factors, according to this factors properly amplify the communication distance of virtual anchor nodes to optimize the virtual anchor nodes box structures,let the sampling area is more appropriate then improve the efficiency of sampling;(2)Using adaptive sampling at sampling stage, according to the area of the sampling box and anchor node density adjust positioning needed sample size, effectively reduce the amounts of calculation, Optimized the energy consumption of WSN;(3)Add virtual anchor node’s information in filtering stage. After filtering stage, adjusted the weight of sample by the distribution position of sample, and also improve the localization accuracy.The simulation result shows that the SDANMCB algorithm comparing with the MCL, MCBalgorithms, the localization accuracy, sampling efficiency has obvious improvement,especially in the low anchor nodes density situation.