Information-driven self-deployment and dynamic sensor coverage for mobile sensor networks

We address the problem of tracking m targets using n mobile sensors for the case of m ≫ n using a coupled estimation and control algorithm. We propose an information-driven flocking algorithm as a self-deployment algorithm for mobile sensor networks for multi-target tracking. The self-deployment algorithm is a modified form of Olfati-Saber´s flocking algorithm [10] with n distinct γ-agents. The mobile sensors track their dedicated γ-agent that are located at the optimal points that maximize the information value of the collective sensed data by any sensor. A Fisher Information Matrix (FIM) based metric called the “information value function” is introduced to measure the quality of information sensed by mobile sensors with limited sensing range. The information-driven flocking algorithm attempts to simultaneously increase the sensor coverage and the information value while maintaining a minimum distance between the sensors and from the boundaries of the sensing region. Simulation results are presented for tracking 250 maneuvering targets in a rectangular region using 25 mobile sensors that are self-deployed using coupled information-driven flocking and information fusion.