Multi-Task Lifelong Reinforcement Learning for Wireless Sensor Networks

TL;DR

This paper introduces a lifelong reinforcement learning approach for wireless sensor networks that adapts to environmental changes, optimizing energy use and data transmission more efficiently than existing methods.

Contribution

The paper presents a novel adaptive control strategy using lifelong reinforcement learning to improve WSN performance under dynamic conditions.

Findings

Achieves near-optimal adaptation approximately 30% faster than RL.

Outperforms Lyapunov-based optimization by 60% in adaptation speed.

Demonstrates effective transfer of environment-specific knowledge.

Abstract

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].