An adaptive coverage method for dynamic wireless sensor network deployment using deep reinforcement learning

Abstract

Coverage optimization stands as a foundational challenge in Wireless Sensor Networks (WSNs), exerting a critical influence on monitoring fidelity and holistic network efficacy. Constrained by the limited energy budgets of sensor nodes, the imperative to maximize network longevity while sustaining sufficient coverage has ascended to the forefront of research priorities. Traditional deployment methodologies frequently falter in complex topographies and dynamic operational environments, encountering difficulties in striking an optimal equilibrium between coverage quality and energy efficiency. To mitigate these inherent limitations, this paper introduces ACDRL (Adaptive Coverage-Aware Deployment based on Deep Reinforcement Learning)—a novel strategy that enables intelligent, self-optimizing node placement in WSNs through deep reinforcement learning paradigms. Our proposed framework establishes a sophisticated deep reinforcement learning architecture integrating a multi-objective reward mechanism and hierarchical state representation, which innovatively resolves the dual predicaments of coverage optimization and energy balancing in intricate scenarios. Extensive simulation results validate that ACDRL consistently outperforms state-of-the-art approaches by maintaining superior coverage ratios, significantly extending network operational lifespan, and demonstrating enhanced adaptability in high-density deployment scenarios.