Research And Application Of Swarm Intelligence In Wireless Sensor Network Positioning Technology

The location technology of wireless sensor network is an important foundation and prerequisite of the study of various techniques of wireless sensor networks,among them improving the positioning accuracy is the key of positioning technology. Positioning technology based distance measurement has a wide application prospect. This paper will focus on the positioning of wireless sensor network technology based on ranging technology, the main contents of this thesis are:(1) It introduced the basic situation of the wireless sensor networks, including network structure, the structure and characteristics of node, at the same time, outlines the research background and the current situation of domestic and international positioning technology. In addition, expounds the common method based on distance measurement and factors affecting the positioning accuracy.(2) For the problem of poor positioning accuracy of the least-square method, this parer applied the cat swarm algorithm based on distance measurement to improve the positioning accuracy. Through converting the problem of positioning in wireless sensor network into optimization problem of nonlinear equations, meanwhile the measurement error is taked as a fitness function, by using global optimization ability of the cat swarm algorithm to solve nonlinear problem,we can reduce the error effectively. The simulation experiments show that compared with the LS and PSO algorithm, the localization algorithm based on CSO can reduce the positioning error, improve the positioning accuracy.And the proposed algorithm has good robustness.(3) In order to reduce the influence of NLOS error caused by non line of sight propagation in wireless sensor network, this paper proposes the use of localization algorithm of bee colony optimization algorithm of Elamn neural network. Colony algorithm was used to optimize the initial weights of Elman neural network, then the optimized BC-Elman neural network is used to correct the TDOA measurement values, with Taylor algorithm to determine the unknown sensor node position. Simulation results show that, compared with the classical Taylor, Chan algorithm, the algorithm can effectively improve the location accuracy.