Energy Efficiency Optimization Task Processing Mechanism Based on Reinforcement Learning in WSN

Aiming at improving the energy efficiency of task processing in wireless sensor networks， a nearly optimal task processing mechanism is proposed， in which wireless sensor nodes can dynamically unload tasks to edge servers and perform local processing according to the number of tasks in the task cache and channel conditions. The task processing mechanism is modeled as a Markov decision process. Since the wireless sensor node does not know the state transition probability of this process， the A3C algorithm is used to realize exploration and learning under unknown environmental parameters， so as to obtain the approximately optimal task processing strategy. Under certain buffer conditions and channel conditions， the optimal task quantity， modulation level and transmission power are selected by this strategy， and the average task processing energy efficiency is improved. Simulation results show that compared with other mechanisms， the proposed task-processing mechanism can improve node energy efficiency and has faster convergence speed.