Physics-Aware Bottleneck-First Target Coverage Scheduling for Solar-Powered Wireless Rechargeable Sensor Networks

Abstract

Long-term target coverage in solar-powered wireless rechargeable sensor networks (WRSNs) is fundamentally challenged by sensing uncertainty, weather-driven energy variability, and the strong coupling between adjustable sensing ranges and energy consumption. Existing approaches often rely on simplified sensing or harvesting models, which may lead to unstable schedules and degraded coverage at vulnerable targets.
This paper proposes Physics-aware Bottleneck-first Target Coverage Scheduling (PBTCS), a unified framework for sustainable target coverage in WRSNs under energy-neutral operation constraints. PBTCS integrates a physics-prior, interpretable day-ahead photovoltaic (PV) forecasting model to derive feasible and auditable energy budgets, and employs a budget-driven time partitioning mechanism to stabilize day-night operations. Based on the probabilistic sensing model, a bottleneck-first scheduling principle is introduced to explicitly prioritize the weakest space-time points, rather than optimizing average coverage metrics. To efficiently realize this objective under adjustable sensing radii, a closed-form marginal-gain decomposition and a budgeted dynamic programming scheme are developed for per-sensor schedule construction.
Extensive simulations using real PV and meteorological data demonstrate that PBTCS consistently outperforms state-of-the-art methods in surveillance quality, coverage fairness, and long-term network sustainability across different seasons and network scales.