RL-Sleep: Temperature Adaptive Sleep Scheduling using Reinforcement Learning for Sustainable Connectivity in Wireless Sensor Networks

Abstract

Temperature variations have a significant effect on the sustainable operation of the power-constrained wireless sensor networks. The characteristics of wireless communication links deteriorates considerably with increase of temperature. Proactive measures may not always perform well in a dynamic environment where both the wireless links and sensor nodes are supposed to behave unexpectedly. Environment adaptive efficient sleep-schedule strategy can preserve the resources of the low power sensor nodes and thereby alleviate the adverse effects of temperature. In this paper, temperature adaptive intelligent sleep-scheduling strategy (RL-Sleep) for the wireless sensor nodes has been proposed. This algorithm is based on Reinforcement Learning which enables a node in the network to perceive the environment and decide autonomously about the action (transmit, listen or sleep) conducive for a stable operation of the network. Simulation results exhibit a good performance of the proposed approach in terms of sustainable operations of the network and connectivity.