Adaptive congestion avoidance scheme based on reinforcement learning for wireless sensor network

Energy efficiency and QoS-aware are the key issues of wireless sensor network (WSN). In this paper, we proposed a congestion avoidance scheme devoting to efficient use of energy and adaptive maintain well QoS quality by self-adapt routing. Because it is difficult to obtain the state of network energy and QoS in a practical condition, we are motivated to utilize reinforcement learning to obtain the routing strategy in multi-path communication of WSN. We extend the R-learning algorithm to solve the difficulty of the nodes obtaining the network´s status information. We compare the proposed scheme to other congestion avoidance protocols, such as CR. The simulation results show that the performance of our schemes is prior to existing ones.