用深度强化学习优化传感器网络电池使用，实现集中更换
Active management of battery degradation in wireless sensor network using deep reinforcement learning for group battery replacement
- 通过深度强化学习动态调整网络任务周期，系统性管理电池衰减
- 使电池集中失效，减少更换次数，且不影响网络整体性能
- 在多种规模网络中验证有效，适合远程监测场景
无线传感器网络（WSNs）在结构健康监测中具有重要应用，尤其适用于难以到达或偏远地区。尽管电池供电的WSN相比有线系统具诸多优势，但有限的电池寿命仍是实际应用中的主要障碍，无论是否采用能量采集技术。现有电池健康管理方法多聚焦于单个电池寿命延长，缺乏系统级视角。这导致各电池失效时间不一，极大增加了电池更换任务的规划与调度难度。本文提出一种基于深度强化学习（DRL）的主动电池退化管理方法，通过在系统层面优化WSN的任务周期，有效减少个体电池过早失效，从而支持集中式电池更换，同时保持网络性能不受影响。基于真实世界WSN部署环境构建仿真平台，训练DRL智能体以学习最优任务周期策略。在不同网络规模的长期实验中验证了该策略的高效性与可扩展性。
原文摘要 · Abstract (English)
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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