Elastic neural network method for multi-target tracking task allocation in wireless sensor network

  • Authors:

  • Mei Liu;Haihao Li;Yi Shen;Jianfeng Fan;Shuangning Huang

  • Affiliations:

  • School of Electrical and Information Technology, Harbin Institute of Technology, Harbin, 150001, China;School of Electrical and Information Technology, Harbin Institute of Technology, Harbin, 150001, China;Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China;School of Electrical and Information Technology, Harbin Institute of Technology, Harbin, 150001, China;School of Electrical and Information Technology, Harbin Institute of Technology, Harbin, 150001, China

  • Venue:
  • Computers & Mathematics with Applications
  • Year:
  • 2009

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Abstract

Aiming at the task allocation of collaborative technique in wireless sensor network, a method for optimized task allocation based on elastic neural network is proposed under the background of multi-sensor tracking. First a model of multi-coalition tracking multi-target is designed. Then disjoint fully connected subgraphs of neurons are constructed to solve the problem of optimized task allocation in tracking multi-target and the increment of system energy consumption when dynamic coalitions compete and conflict for the resource of sensor nodes. Compared with the conventional method, simulation results show that the energy consumption of the tracking system is reduced significantly and the tracking accuracy is improved greatly, demonstrating the effectiveness of elastic neural network in handling the optimized task allocation problem of multi-sensor tracking multi-target.