Node Deployment and Energy Saving Optimization Method for Wireless Sensor Networks Based on Q-learning

Abstract

The use of wireless sensor networks can achieve effective protection of the monitored area. The node deployment and energy saving optimization of wireless sensor network is important due to the constraints of limited battery capacity and short life span of nodes, and a node deployment and energy saving optimization method is proposed based on reinforcement learning. The Q-learning algorithm is used to screen the nodes that can detect the range of small animals, deploy the nodes autonomously and achieve effective energy saving optimization. Simulation results show that the method can reduce energy consumption by 30% to 35% with shorter convergence time.