Evolutionary Self-Learning Scheduling Approach for Wireless Sensor Network

Energy efficiency is very important for wireless sensor network (WSN). This paper presents an evolutionary self-learning scheduling approach (ESSA) to reduce energy consumption for WSN. The ESSA is based on a new proposed scheme - evolutionary Q-learning with continuous-action (EQC) approach, which combines an extension of Q-learning method with particle swarm optimization (PSO) algorithm. The action space of EQC is partitioned into lots of subintervals. And each endpoint of the subintervals is characterized by a discrete action value and a Q-value. The continuous action value is the weighted average of discrete actions according to their Q-values. The PSO algorithm is combined to let an agent profit the experience of other agents. We valid the ESSA in a MAC protocol and simulation results show that the ESSA is an effective method and performs much better than SMAC protocol.