Distributed wireless sensor scheduling for multi-target tracking based on matrix-coded parallel genetic algorithm

The aim of designing a sensor scheduling scheme for target tracking in wireless sensor network is to improve the tracking accuracy, balance the network energy and prolong the network lifespan. It is viewed as a multi-objective optimization problem. A modified matrix-coded parallel genetic algorithm (MPGA) is proposed in which multiple subpopulations evolve synchronously and satify the specific constraint arised from the senario of multi-target tracking that a sensor can only track just one target. Simulation results show that MPGA, compared with traditional genetic algorithm, converges to the better result with higher speed when applied in multi-target tracking in wireless sensor network. And our proposed distributed sensor scheduling scheme based on MPGA outperforms than existed schemes.

[1]  Sherali Zeadally,et al.  A Novel Distributed Sensor Positioning System Using the Dual of Target Tracking , 2008, IEEE Transactions on Computers.

[2]  J. Liu,et al.  Multitarget Tracking in Distributed Sensor Networks , 2007, IEEE Signal Processing Magazine.

[3]  Chen Jian-xia,et al.  A Dynamic Task Allocation Scheme for Wireless Sensor Networks , 2006 .

[4]  Chang Wen-Ping,et al.  Application of double matrix hybrid coded genetic algorithm in unit commitment , 2008, 2008 China International Conference on Electricity Distribution.

[5]  Aaron D. Lanterman,et al.  Unscented Kalman Filters for Multiple Target Tracking With Symmetric Measurement Equations , 2009, IEEE Transactions on Automatic Control.

[6]  DU Xiao-yu.  A Method of Sensor Management Based on Target Priority and Information Gain , 2005 .

[7]  Frank L. Lewis,et al.  Energy-Efficient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor Networks , 2009, IEEE Transactions on Instrumentation and Measurement.

[8]  Jianfeng Fan,et al.  Elastic neural network method for multi-target tracking task allocation in wireless sensor network , 2009, Comput. Math. Appl..

[9]  Jianwei Wan,et al.  Iterated Unscented Kalman Filter for Passive Target Tracking , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Stephen B. Wicker,et al.  On the Complexity of Distributed Self-Configuration in Wireless Networks , 2003, Telecommun. Syst..

[11]  Jeffrey Nash.  Optimal allocation of tracking resources , 1977, 1977 IEEE Conference on Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications.

[12]  Jianxun Li,et al.  Unscented Kalman Filtering based quantized innovation fusion for target tracking in WSN with feedback , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[13]  John W. Fisher,et al.  Maximum Mutual Information Principle for Dynamic Sensor Query Problems , 2003, IPSN.

[14]  Li Haihao,et al.  Research on Task Allocation Technique for Multi-Target Tracking in Wireless Sensor Network , 2007, 2007 International Conference on Mechatronics and Automation.