Charging optimization in multi-app wireless sensor networks through reinforcement learning

Abstract

Wireless sensor networks are becoming increasingly prevalent in modern systems. These networks can be outfitted with a mobile charger that travels the network and replenishes the energy of the nodes within. This paper introduces a novel resource management problem for controlling mobile chargers in rechargeable wireless sensor networks shared among multiple applications. A reinforcement learning approach is developed to optimize the charger's actions, increasing the network's lifetime while ensuring that each application's throughput and coverage requirements are met to the best of the charger's ability. The resultant algorithm optimizes mobile charger network traversal and energy usage to maximize the network's lifespan while meeting application Quality of Service (QoS) requirements. It can also adjust the mobile charger behaviour when some applications are assigned higher priority than others, ensuring critical network operations are maintained more effectively. Numerical results show that the proposed approach ensures minimum QoS requirements are met through network node energy level maintenance and prolonged network up-time.