Predictive Priority-Based Wireless Charging for Critical Node Reliability in Wireless Rechargeable Sensor Networks: A Systematic Literature Review and Research Framework

Abstract

Power constraints pose a significant challenge to rechargeable wireless sensor networks, particularly in applications requiring high reliability and stable connectivity. This is despite substantial advancements in Wireless Power Transfer (WPT) technology thanks to the use of portable chargers and drones. Most current studies have focused primarily on improving charging efficiency or extending network lifespan. This systematic review aims to analyze recent studies on several criteria, including charging methods, AI and prediction techniques, charging decision-making mechanisms, the level of critical node consideration, and the limitations of each study. This systematic review study presents a systematic literature examines the recent studies on power management and wireless charging in Wireless Rechargeable Sensor Networks (WRSNs) published between 2016 and 2026. It focused on charging scheduling techniques, power prediction, AI-driven charging, and critical node analysis. The analysis revealed that most current studies rely on traditional indicators such as remaining power level or charger path optimization, while the integration of Critical Node Awareness, Predictive Energy Modeling, and Priority-based Charging remains limited. The results also highlighted a significant research gap: the absence of a comprehensive framework linking node importance analysis within the network, early power depletion prediction, and priority-based smart charging mechanisms to enhance network reliability. Therefore, this systematic review proposes an integrated conceptual framework based on Topology-aware Critical Node Identification, Predictive Energy Modeling, and Priority-based Wireless Charging to achieve Selective Reliability in WRSNs. The proposed framework aims to improve the continuity of critical nodes and reduce the likelihood of network collapse due to high-impact node failures, making it a promising approach for developing smarter and more reliable power management systems in future wireless sensor networks.