%0 Journal Article
%A LI Xiang-yang
%A LIU Qin
%A XIAO Ren-rong
%A YU Xiu-wu
%A ZHANG Ke
%T Information Fusion Algorithm Based on Improved Ant Colony Optimization BP Neural Network in WSN
%D 2018
%R 10.13190/j.jbupt.2017-262
%J Journal of Beijing University of Posts and Telecommunications
%P 91-96
%V 41
%N 4
%X In order to ensure the effective working of wireless sensor network (WSN) in deep mine, an information fusion algorithm based on improved ant colony optimization back-propagation (BP) neural network in WSN (IFA-IACOBP) is proposed. The heuristic factor of ant colony optimization (ACO) is improved by planning the direction of ants' motion and introducing the residual energy of nodes to improve the selection probability of the ant next-hop node. The improved ant colony algorithm is used to optimize the BP neural network, which is applied to WSN information fusion in mine. These data are processed by two-level fusion, which can remove most redundant information. Simulation results show that the IFA-IACOBP algorithm can effectively decrease the network data communication, improve data real-time performance, reduce network energy consumption and prolong network lifetime.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2017-262