Adaptive flocking control for dynamic target tracking in mobile sensor networks

Target tracking is an important task in sensor networks, especially in mobile sensor networks. Flocking control is used to control a mobile sensor network to track a target. However, there are some existing problems in this control method, such as network fragmentation, loss of formation and poor tracking performance. In order to handle these problems we propose a novel approach to flocking control of a mobile sensor network to track a moving target within changing environments. In our approach, each agent can cooperatively learn the network's parameters to decide the size of network in a decentralized fashion so that the connectivity, formation and tracking performance can be improved when avoiding obstacles. In addition, to demonstrate the benefit of our approach a comparison between this approach and the existing method is given. Computer simulations are performed to demonstrate the effectiveness of the proposed approach.