2025/06/19 by Hossein Mohammadi Firouzjaei, Firouzjaei, Hossein Mohammadi, Rafaela Scaciota +3 · 1 citation
Computer Science · Engineering · #Energy Efficient Wireless Sensor Networks #Energy Harvesting in Wireless Networks #FOS: Electrical engineering #Reinforcement Learning in Robotics #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2506.16254
openalex publication_date 2025/06/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Enhancing the sustainability and efficiency of wireless sensor networks (WSN) in dynamic and unpredictable environments requires adaptive communication and energy harvesting strategies. We propose a novel adaptive control strategy for WSNs that optimizes data transmission and EH to minimize overall energy consumption while ensuring queue stability and energy storing constraints under dynamic environmental conditions. The notion of adaptability therein is achieved by transferring the known environment-specific knowledge to new conditions resorting to the lifelong reinforcement learning concepts. We evaluate our proposed method against two baseline frameworks: Lyapunov-based optimization, and policy-gradient reinforcement learning (RL). Simulation results demonstrate that our approach rapidly adapts to changing environmental conditions by leveraging transferable knowledge, achieving near-optimal performance approximately 30% faster than the RL method and 60% faster than the Lyapunov-based approach. The implementation is available at our GitHub repository for reproducibility purposes [1].