Reinforcement learning for adaptive battery management of structural health monitoring IoT sensor network
Tahsin Afroz Hoque Nishat,
Jong-Hyun Jeong,
Hongki Jo,
Shenghao Xia and
Jian Liu
Applied Energy, 2025, vol. 390, issue C, No S0306261925004611
Abstract:
Battery-powered wireless sensor networks (WSNs) provide an affordable and easily deployable option for Structural Health Monitoring (SHM). However, their long-term viability becomes challenging due to uneven battery wear across the sensor network, logistical planning difficulties for battery replacement, and maintaining the desired Quality of Service (QoS) for SHM. A system-level battery health management strategy is vital to extend the lifespan and reliability of WSNs, especially considering the expensive maintenance trips required for battery replacement. This study presents a reinforcement learning (RL) based framework to actively manage battery degradation at the system level while preserving SHM QoS. The framework focuses on group battery replacement, reducing logistical burdens, and enhancing WSN longevity without compromising desired QoS. To validate the RL framework, a detailed simulation environment was created for a real-world WSN setup on a cable-stayed bridge SHM. The simulation accounted for various environmental and operational factors such as weather-induced solar harvesting variability, communication uncertainties, lithium-ion battery degradation models, sensor power consumption, and duty cycle strategies etc. Additionally, a mode shape-based quality index was introduced for a SHM network. The RL agent was trained within this environment to learn optimal node selection for specific duty cycles. The results demonstrate the framework's effectiveness in optimizing battery replacement efforts by ensuring a similar end of lifetimes with more uniform battery degradation and allowing the longer and more reliable operation of WSNs under uncertainties.
Keywords: Battery health management; Wireless sensor network (WSN); Deep reinforcement learning (DRL); Quality of service (QoS); Battery group replacement; Structural health monitoring (SHM) (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925004611
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:390:y:2025:i:c:s0306261925004611
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic
DOI: 10.1016/j.apenergy.2025.125731
Access Statistics for this article
Applied Energy is currently edited by J. Yan
More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().