Energy Efficiency for Green AIoT: A Comprehensive Survey

Abstract

The Internet of Things (IoT) has evolved into the Artificial Intelligence of Things (AIoT), where intelligent data processing complements large-scale connectivity across applications ranging from smart homes to industrial automation. However, its rapid expansion and the increasing adoption of AI have led to growing environmental concerns, particularly increased energy consumption and electronic waste. These issues highlight the importance of Green AIoT practices, which extend Green IoT by combining energy-efficient communication, computing, and intelligent resource management to achieve energy-efficient and sustainable AIoT operation. This paper presents a comprehensive survey of techniques aimed at improving the energy efficiency and sustainability of Green AIoT systems. The focus is placed on networking aspects, particularly machine-to-machine (M2M) communications and wireless sensor networks (WSNs), alongside the roles of computing infrastructures, data centers, and energy-efficient processor architectures. The survey further examines how AI-assisted techniques, including TinyML, edge AI, and intelligent computation offloading, complement traditional Green IoT mechanisms to reduce energy consumption. Key approaches such as low-power communication protocols, energy-efficient data processing, data compression, smart energy management, and energy harvesting are reviewed and compared. Furthermore, the paper summarizes representative state-of-the-art solutions with quantitative insights and discusses open challenges and future research directions toward environmentally sustainable AIoT systems.