The Research Of Mobile Nodes Security Based On Mcl Method

The localization of mobile node is one of the hotspot in wireless sensor networks study. In mobile wireless sensor network, there are a number of locating methods based on different moving states of anchor nodes and unknown nodes. Each method has different characteristics under different application background. However, part of the positioning technologies about mobile node aren't involved with the security. This considers the security during mobile node localization, preventing the illegal node in localization period into the network.In mobile wireless sensor network after the mobile node is deployed. The first thing should be done is locate the position and then establish communication channels. Using Monte Carlo method, in the case of the node moving randomly, does not require additional equipments of hardware, the more accurate position can be got. However, if there are insecure nodes being mingled in the initial stage of positioning, the nearby nodes will easily regard it as neighbor node in the network structure, which will be harmful to the entire wireless sensor networks. Therefore, an method is needed to exclude the insecure nodes before the communications channel is established.This is that using network security protocol SNEP(Sensor Network Encryption Protocol ) protocol with a low communication cost and authenticating the communication nodes in the stage of locating with the Monte Carlo positioning method can ensure the security of every positioning node. Using NS2 simulation tools, simulation experiments can demonstrate the idea proposed in this paper. From the aspects that positioning accuracy is affected by the convergence and speed, the comparison between the Monte Carlo positioning method with not take into account security and the Monte Carlo positioning method with security consideration is carried out. Experimental results show that the improved Monte Carlo positioning method has better convergence. In the case of node with a slow motion, the improved Monte Carlo positioning method performs well on positioning deviation.