A multiobjective optimization approach for adaptive binary quantizer design for target tracking in wireless sensor networks

In this paper, the considered task of the wireless sensor network is to track a target emitting energy. Each sensor measures received signal strength from the target, and sends binary decisions directly to the fusion center. We assume that at each time step of tracking, the local decision thresholds of sensors are updated optimally and dynamically as a result from a Multiobjective Optimization Problem (MOP). The MOP considered in this paper jointly maximizes the Fisher Information for decreasing the error on estimation and minimizes the sum of sensor transmission probabilities. Simulation results show that solutions obtained from the Pareto-optimal front provides good target tracking performance while significantly reducing the average number of sensors transmitting to the fusion center at each time step of tracking.