Adaptive Sleep/Wake Algorithms for Ultra Low Power Edge Devices

Abstract

Ultra-low power (ULP) edge devices are increasingly used in Internet of Things (IoT) and pervasive computing systems, where energy efficiency and prolonged battery life are critical. Adaptive sleep/wake algorithms have emerged as a key approach to minimize energy consumption while maintaining real-time responsiveness. This review paper provides a comprehensive survey of adaptive sleep/wake strategies for ULP edge devices, focusing on their design principles, optimization techniques, and performance evaluation metrics. Various algorithmic approaches, including threshold-based, predictive, and machine learning-assisted sleep/wake management, are examined. Trade-offs between energy efficiency, latency, and reliability are discussed. Additionally, simulation and real-world deployment studies are summarized to highlight practical implementation challenges and opportunities. The paper concludes with future directions for research in adaptive energy management in edge computing systems.