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.