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

arXiv: https://arxiv.org/abs/2503.15865 · HTML: https://mlsanity.com/paper/2503.15865/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)

## Abstract (arXiv)

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.