Energy Harvesting-Aware Power Management for Batteryless IoT Systems a Review and Research Gap Analysis
TL;DR
A compact review of energy harvesting approaches for batteryless IoT is discussed, which discusses many approaches, such as energy harvesting techniques, harvest-use and harvest-store-use architectures, adaptive power management, energy-aware scheduling, TinyML-based operation, and intermittent computing.
Abstract
The rapid growth of Internet of Things (IoT) applications has increased the demand for sustainable, long-life, and maintenance-free sensing systems. Conventional battery-powered IoT nodes suffer from limited lifetime, battery replacement difficulty, maintenance cost, and poor scalability in remote or large-scale deployments. Energy harvesting has emerged as a promising solution for enabling self-powered and batteryless IoT systems by utilizing ambient energy sources such as solar, radio frequency, thermal, vibration, piezoelectric, and hybrid sources. However, harvested energy is intermittent, environment-dependent, and difficult to predict, which affects sensing continuity, communication reliability, workload execution, and Quality of Service (QoS). A compact review of energy harvesting approaches for batteryless IoT is discussed in this article. This review discusses many approaches, such as energy harvesting techniques, harvest-use and harvest-store-use architectures, adaptive power management, energy-aware scheduling, TinyML-based operation, and intermittent computing. To identify existing approaches, a comparative analysis is presented. The review also reveals several unresolved issues, particularly the weak consideration of QoS, limited use of energy forecasting, poor integration among harvesting and control modules, unstable task execution during power interruptions, and the processing overhead of intelligent techniques Finally, future directions toward intelligent, lightweight, and QoS-aware batteryless IoT frameworks are discussed. Keywords: Energy harvesting, batteryless IoT, power management, energy-aware scheduling, intermittent computing, TinyML, QoS-aware systems.