Enhanced Evolutionary Multi-Objective Deep Reinforcement Learning for Reliable and Efficient Wireless Rechargeable Sensor Networks
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
Despite rapid advancements in sensor networks, conventional battery-powered sensor networks suffer from limited operational lifespans and frequent maintenance requirements that severely constrain their deployment in remote and inaccessible environments. As such, wireless rechargeable sensor networks (WRSNs) with mobile charging capabilities offer a promising solution to extend network lifetime. However, WRSNs face critical challenges from the inherent trade-off between maximizing the node survival rates and maximizing charging energy efficiency under dynamic operational conditions. In this paper, we investigate a typical scenario where mobile chargers move and charge the sensor, thereby maintaining the network connectivity while minimizing the energy waste. Specifically, we formulate a multi-objective optimization problem that simultaneously maximizes the network node survival rate and mobile charger energy usage efficiency across multiple time slots, which presents NP-hard computational complexity with long-term temporal dependencies that make traditional optimization approaches ineffective. To address these challenges, we propose an enhanced evolutionary multi-objective deep reinforcement learning algorithm, which integrates a long short-term memory (LSTM)-based policy network for temporal pattern recognition, a multilayer perceptron-based prospective increment model for future state prediction, and a time-varying Pareto policy evaluation method for dynamic preference adaptation. Extensive simulation results demonstrate that the proposed algorithm significantly outperforms existing approaches in balancing node survival rate and energy efficiency while generating diverse Pareto-optimal solutions. Moreover, the LSTM-enhanced policy network converges 25% faster than conventional networks, with the time-varying evaluation method effectively adapting to dynamic conditions.
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 10 of its 51 references that are in the map. First posted on arXiv 24 Oct 2025.
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10 of 51 references.
References · 10
- 2023Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescuecited by 14
- 2022Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computingcited by 11
- 2025Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learningcited by 9
- 2024Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learningcited by 8
- 2024UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learningcited by 5
- 2025Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learningcited by 3
- 2025Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approachcited by 3
- 2025Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networkingcited by 2
- 2025Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Methodcited by 2
- 2024Chaos-based reinforcement learning with TD3cited by 1
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- 2024Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeable
- 2026HQARRF: Hierarchical Q-learning and Force-aware Routing for Multi-Charger Scheduling in Wireless Rechargeable Sensor Networks
- 2024HGFF: A Deep Reinforcement Learning Framework for Lifetime Maximization in Wireless Sensor Networks
- 2025Active management of battery degradation in wireless sensor network using deep reinforcement learning for group battery replacement
- 2025Multi-Task Lifelong Reinforcement Learning for Wireless Sensor Networks
- 2026Adaptive Scheduling: A Reinforcement Learning Whittle Index Approach for Wireless Sensor Networks
- 2026LLM-Enhanced Multi-Agent Reinforcement Learning for Unified Electric Vehicles-Charging Station-Grid Optimization in Public Charging Systems
- 2025Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers