Active management of battery degradation in wireless sensor network using deep reinforcement learning for group battery replacement

• URL: http://arxiv.org/abs/2503.15865v2
• Date: Sat, 22 Mar 2025 20:21:34 GMT
• Title: Active management of battery degradation in wireless sensor network using deep reinforcement learning for group battery replacement
• Authors: Jong-Hyun Jeong, Hongki Jo, Qiang Zhou, Tahsin Afroz Hoque Nishat, Lang Wu,
• Abstract summary: Wireless sensor networks (WSNs) have become a promising solution for structural health monitoring (SHM)<n>Battery-powered WSNs offer various advantages over wired systems, however limited battery life has always been one of the biggest obstacles in practical use of the WSNs.<n>This study investigate a deep reinforcement learning (DRL) method for active battery degradation management by optimizing duty cycle of WSNs at the system level.
• Score: 4.469172054222021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wireless sensor networks (WSNs) have become a promising solution for structural health monitoring (SHM), especially in hard-to-reach or remote locations. Battery-powered WSNs offer various advantages over wired systems, however limited battery life has always been one of the biggest obstacles in practical use of the WSNs, regardless of energy harvesting methods. While various methods have been studied for battery health management, existing methods exclusively aim to extend lifetime of individual batteries, lacking a system level view. A consequence of applying such methods is that batteries in a WSN tend to fail at different times, posing significant difficulty on planning and scheduling of battery replacement trip. This study investigate a deep reinforcement learning (DRL) method for active battery degradation management by optimizing duty cycle of WSNs at the system level. This active management strategy effectively reduces earlier failure of battery individuals which enable group replacement without sacrificing WSN performances. A simulated environment based on a real-world WSN setup was developed to train a DRL agent and learn optimal duty cycle strategies. The performance of the strategy was validated in a long-term setup with various network sizes, demonstrating its efficiency and scalability.

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