Adaptive target tracking in sensor networks using reinforcement learning

This paper, proposes the use of a reinforcement learning approach for a target tracking sensor network application. Harsh and unpredictable situations of sensor nodes in such an application requires a self-tuning mechanism for the nodes to adapt their behavior over time. The method is examined under high dynamic network conditions and compared with a similar method called SORA over different performance measures. The results show a significant improvement over the compared method in the environments with high level of dynamism.