Dynamic Weather-Adaptive Enhanced Barrier Coverage with Adjustable-Range Sensors for WRSNs

Abstract

Barrier coverage is vital for wireless sensor networks (WSNs). Traditional approaches using battery-powered, fixed-radius sensors under the Boolean Sensing Model (BSM) struggle to ensure long-term, high-quality monitoring. This paper proposes BCRAS, a barrier coverage algorithm based on solar-powered sensors with adjustable sensing radii and the Probabilistic Sensing Model (PSM). It addresses three key challenges: (1) To cope with solar power uncertainty, a CNN-LSTM model predicts next-day PV energy to support energy-aware scheduling; (2) To manage varying energy consumption across sensing ranges, each sensor selects its sensing radius based on predicted energy gain and usage balance; (3) To enhance coverage under PSM, sensors are scheduled according to their cooperative detection probability at bottleneck points. Experiments show that BCRAS improves surveillance quality, energy utilization, and long-term stability compared to existing methods.