Research On Localization In Wireless Sensor Networks For Mobile Nodes

Node localization is a key technology of wireless sensor networks.At present,the theoretical achievements of static node location technology are relatively rich,while there is a few research on mobile node localization.Monte Carlo localization algorithm reasonably utilizes the mobility of nodes for localization,and it is easy to implement,so it becomes an important method for mobile node localization.This paper focuses on the research of mobile node localization algorithm based on Monte Carlo method.The main contents include:(1)This paper researches the node localization technology,and focuses on the principle of node localization based on Monte Carlo method.Then,three classical mobile node localization algorithms and their advantages and disadvantages are analyzed.(2)To solve the problems of low sampling efficiency and dependence on anchor node density in the localization algorithm based on Monte Carlo method,this paper proposes a node localization algorithm based on improved sampling box.The algorithm utilizes the anchor node and common node position information at last time and current time,further narrowing down the sampling area,which can improve the location accuracy and sampling efficiency.Using the location information of common nodes can reduce dependence on anchor nodes.The algorithm uses the crossover idea of genetic algorithm in the re-sampling process,and uses the existing valid samples to generate new samples,which improves the sampling efficiency of the algorithm.The simulation results show that the proposed algorithm can maintain good positioning accuracy at low anchor node density.(3)To solve the problem of poor anti-noise performance of many localization algorithms,this paper proposes a Monte Carlo node localization algorithm based on fuzzy inference.In this algorithm,the network is irregular,and the communication radius of the node is no longer an ideal circle.This algorithm uses fuzzy inference system to establish the relationship between the received signal strength and the distance between nodes,and uses a precise signal strength input value to output a fuzzy distance.The algorithm uses fuzzy distance as the conditional constraint for constructing the sampling box and sample filtering,which effectively improves the location accuracy of the node and has strong anti-noise ability.