Survey on Self-Sustaining IoT Systems via Multi-Source Energy Harvesting and Adaptive Power Intelligence for Predictive Maintenance

Abstract

The rapid expansion of Internet of Things (IoT) technologies in industrial environments has created significant demand for energy-efficient and autonomous sensing systems capable of operating under resource-constrained conditions. Traditional battery-powered IoT devices face critical limitations, including limited operational lifetime, frequent maintenance, and increased deployment costs, particularly in remote and inaccessible industrial locations. To address these challenges, recent research has focused on integrating energy harvesting techniques, intelligent power management, and lightweight edge intelligence into Industrial IoT (IIoT) frameworks.
This survey paper presents a comprehensive review of self-sustaining IoT systems designed for predictive maintenance applications. The study examines various energy harvesting approaches, including solar, vibration,
thermal, and hybrid energy harvesting mechanisms, highlighting their efficiency, reliability, and suitability for industrial environments. In addition, the survey analyzes adaptive power-aware strategies that dynamically regulate sensing, computation, and communication operations based on real-time energy availability.
The paper further investigates the role of edge computing and lightweight machine learning models in enabling real-time anomaly detection while minimizing energy consumption. Existing intelligent energy management techniques, predictive energy allocation models, and low-power communication protocols are comparatively evaluated in terms of energy efficiency, computational overhead, response latency, and maintenance requirements.