Energy-Efficient Edge Computing Architectures for IoT Devices

Abstract

With the rapid growth of the Internet of Things (IoT), billions of connected devices are generating massive volumes of data. Processing this data efficiently while maintaining low energy consumption is a significant challenge, especially for resource-constrained IoT devices. Edge computing, which brings computation closer to the data source, has emerged as a promising solution to mitigate latency, reduce network bandwidth, and enhance privacy. However, energy consumption at the edge remains a critical concern. This paper presents a comprehensive review of energy-efficient edge computing architectures designed for IoT devices. We analyze various strategies, including lightweight hardware design, energy-aware task scheduling, model compression techniques, and adaptive resource allocation. Additionally, we explore hybrid architectures that integrate fog, cloud, and edge layers for optimal energy utilization. The paper also discusses future research directions in energyefficient IoT-edge integration, aiming to provide a roadmap for the development of sustainable IoT ecosystems.

KEYWORDS: Edge computing, IoT devices, energy efficiency, task scheduling, model compression, resource allocation