Robust distributed target tracking in Wireless Sensor Networks based on multi-objective optimization

This paper addresses the problem of distributed target tracking in Wireless Sensor Networks (WSN) based on multi-objective algorithm for jointly selecting the appropriate group of sensors and optimizing the quantization. Firstly, we jointly select the best group of candidate sensors and optimize the quantization 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 sensor 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 selection is also based on the transmitting power between one sensor and the cluster head (CH). The simulation results show that the proposed method, outperforms the QVF under sensing range constraint and the quantized particle filter (QPF) algorithm.