Research On Wireless Sensor Network Management And Node Localization

Sensor technology and wireless communications technology continues to advance, promoting the development and applications of wireless sensor networks and raises higher demands of the management of wireless sensor networks. While the wireless sensor network management and application-specific sensor nodes depend on the location information. The mobile node localization is one of the key technologies, so the research wireless sensor network management and mobile node localization has important theoretical significance and application value.Most wireless sensor network localization algorithm does not consider the node mobility, Monte-Carlo localization algorithm was originally applied to robot positioning, it can effectively address the positioning of mobile nodes, but requires a large number of samples to get a better positioning results. In this paper, through optimization of sampling techniques, improving of Monte-Carlo localization algorithm, and applied it to mobile node positioning of wireless sensor networks.This paper first proposes a sampling-based optimization of the Monte-Carlo localization algorithm that, according to the continuity of the movement characteristics of the node, using curve fitting method, the node position of the sample obtained posterior density values larger area, the region within the sample to optimize the weights of the node, thus completing the unknown node location. The algorithm effectively reduces the targeted number of samples required, improves the positioning accuracy and reduces the location cost.Secondly, there is no generally accepted and effective method for the positioning of wireless sensor network node problem in the three-dimensional space .To this end this paper theoretically proved that the optimized sampling error of Monte-Carlo localization algorithm without dimension restrictions, and thus sample optimized Monte-Carlo calculation can be applied to three-dimensional positioning of wireless sensor networks mobile nodes location.Finally, Monte-Carlo localization algorithm and the optimization of Monte-Carlo sampling-based mobile positioning algorithm are analyzed and compared according to simulation. Simulation results show that optimal sampling Monte-Carlo localization algorithm has been enhanced positioning accuracy, and in the case of lower sampling frequency remained relatively high positioning accuracy.