Node energy consumption minimization strategy in wireless sensor networks based on CICRL

Abstract

To address challenges such as energy consumption control, coverage stability, and complex interference in wireless sensor networks, this study proposes a collaborative information coverage reinforcement learning algorithm. It enhances state representation with multi-source coverage and neighborhood energy interaction, and optimizes policies via an energy consumption differential update mechanism. The method improves policy convergence and energy balancing in high-dimensional scenarios. Tests on public datasets achieved up to 89.7% coverage, outperforming benchmarks by 5%–12%. Ablation studies confirmed the contributions of collaborative information and energy differential terms, boosting coverage by 8%–15%. Under various interference levels, the energy per packet remained stable (1.18–1.89 J/packet), lower than that of comparison methods. Network lifetime increased by about 20% in continuous and discrete transmission scenarios, demonstrating advantages in coverage, energy efficiency, and robustness for large-scale sensor and IoT applications.