Genetic Algorithm Optimization for Quantized Target Tracking in Wireless Sensor Networks

This work presents a multi-objective algorithm for jointly selecting the appropriate group of candidate sensors and optimizing the quantization for target tracking inWireless Sensor Networks (WSN). We focus on a more challenging problem of how to effectively utilize quantized sensor measurement for target tracking in sensor networks by considering sensors selection problem. Firstly, we jointly optimize the quantization level and the group of candidate sensors selection in order to provide the required data of the target and to balance the energy dissipation in the WSN. Then, we estimate the target position using quantized variational filtering (QVF) algorithm. The quantization optimization and the sensors selection are based on multi-objective (MO) that define the main parameters that may influence the relevance of the participation in cooperation for target tracking. This optimization is also based on the transmitting power between one sensor and the CH. The best sensors selection and quantization optimization are designed to reduce the communication cost and the estimation error, which leads to a significant reduction of energy consumption and an accurate target tracking. The simulation results show that the proposed method, outperforms the quantized variational filtering algorithm under sensing range constraint and the centralized quantized particle filter.

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