Highly Effective Online Energy Restoration of Wireless Rechargeable Sensor Networks by a Mobile Charger

Abstract

Wireless rechargeable sensor network (WRSN) employs a Mobile Charger that mitigates sensor's energy depletion helps to maximize active node counts (operational size) and minimizing node downtime (disconnection time). In this paper, we introduce the Continuous Local Learning (CLL) strategy. CLL is an online replenishment strategy, where MC predicts sensor power consumption rates, calculates the dispatch route and adjusts dispatch decisions during active charging rounds. CLL operates without prior network maps or centralized coordination. CLL relies on single-hop local communication between MC and sensors, with minimum memory allocation, and low computations on the MC. Empirical evaluations confirm that CLL achieves empirical immortality without long-range telemetry and prevents node outages across extended operational horizons. In comparison to a periodic strategy, denoted as H, CLL demonstrates superior performance: CLL consistently expands operational network size while drastically reducing average disconnection time across all evaluated settings.