Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2018, Vol. 41 ›› Issue (4): 91-96.doi: 10.13190/j.jbupt.2017-262

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Information Fusion Algorithm Based on Improved Ant Colony Optimization BP Neural Network in WSN

YU Xiu-wu1,2, LIU Qin1,2, LI Xiang-yang1, ZHANG Ke1, XIAO Ren-rong1   

  1. 1. School of Environment and Safety Engineering, University of South China, Hunan Hengyang 421001, China;
    2. State Key Laboratory of Safety and Health for Metal Mines, Sinosteel Maanshan Institute of Mining Research Company, Anhui Maanshan 243000, China
  • Received:2017-12-29 Online:2018-08-28 Published:2018-10-09

Abstract: 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.

Key words: ant colony optimization, back-propagation neural network, wireless sensor network, information fusion, deep mine

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