%0 Journal Article
%A LI Qing
%A LIU Baojian
%A ZHANG Xiaoyi
%T Evolutionary game theory based clustering algorithm for multi-target localization in wireless sensor network
%D 2016
%R 10.11772/j.issn.1001-9081.2016.08.2157
%J Journal of Computer Applications
%P 2157-2162
%V 36
%N 8
%X Aiming at the problem that the network lifetime was reduced because of the high energy consumption of the nodes covered by multiple radiation sources in large scale Wireless Sensor Network (WSN), a new clustering algorithm based on Evolutionary Game Theory (EGT) was proposed. The non-cooperative game theory model was established by mapping the search space of the optimal node sets to the strategy space of the game and using the utility function of the game as objective function respectively; then the optimization objective was achieved by using Nash equilibrium analysis and the perturb-recover process of equilibrium states. Furthermore, a detailed clustering algorithm was presented to group the optimal node sets into clusters for further location. The proposed algorithm was compared with the nearest-neighbor algorithm and the clustering algorithm based on Discrete Particle Swarm Optimization (DPSO) algorithm in the location accuracy and the network lifetime under the RSSI (Received Signal Strength Indication)/TDOA (Time Difference of Arrival) two rounds cooperative location scheme. Simulation results show that the proposed algorithm decreases the energy consumption of the nodes covered by multiple radiation sources, prolongs the network lifetime and guarantees the precise location.
%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2016.08.2157