A Reinforcement Learning-Based Mobile Charging Agent for Wireless Rechargeable Sensor Networks
學年 113
學期 2
發表日期 2025-07-16
作品名稱 A Reinforcement Learning-Based Mobile Charging Agent for Wireless Rechargeable Sensor Networks
作品名稱（其他語言）
著者 Cuijuan Shang; Qiaoyun Zhang; Wan-Chi Yang; Chih-Yung Chang
作品所屬單位
出版者
會議名稱 IEEE ICCE-TW 2025
會議地點 Kaohsiung, Taiwan
摘要 Wireless Rechargeable Sensor Networks (WRSNs) are crucial for many applications, including environmental monitoring, healthcare, and smart cities. However, optimizing the charging schedule of a mobile charger to maximize network coverage while minimizing charging latency and energy consumption remains a challenge. In this paper, we design a reinforcement learning-based mobile charging agent (RLMCA), which integrates dynamic window search (DWS), Q-learning, and SARSA to improve charging efficiency. The proposed RLMCA combines dynamic threshold-based charging requests and an improved reward function that accounts for sensor coverage contribution. Extensive simulations demonstrate that RLMCA outperforms conventional methods in terms of charging latency, energy usage efficiency, and network coverage.
關鍵字
語言 en
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20250716~20250718
通訊作者
國別 USA
公開徵稿
出版型式
出處
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128904 )

SDGS 尊嚴就業與經濟發展,產業創新與基礎設施