A Comprehensive Review of Artificial Intelligence of Things (AIoT): Towards Energy-Efficient and Secure Node Architectures
TL;DR
This paper systematically examines the AIoT layered architecture, state-of-the-art machine learning and deep learning approaches for energy management, AI-driven intrusion detection systems, blockchain-based trust frameworks, lightweight cryptographic primitives, and federated learning mechanisms, and the interplay between energy efficiency and security overhead.
Abstract
The proliferation of Internet of Things (IoT) devices across healthcare, smart cities, industrial automation, transportation, and agriculture has ushered in an era of unprecedented connectivity and data generation. Despite these advances, conventional IoT architectures remain fundamentally constrained by limited battery capacity, restricted computational resources, and growing exposure to sophisticated cyber-threats. Artificial Intelligence of Things (AIoT)—the convergence of artificial intelligence (AI) with IoT infrastructure—has emerged as a transformative paradigm capable of addressing these systemic limitations. This paper presents a thorough and structured review of AIoT with a dual emphasis on energy optimization and node-level security. We systematically examine the AIoT layered architecture, state-of-the-art machine learning and deep learning approaches for energy management, AI-driven intrusion detection systems, blockchain-based trust frameworks, lightweight cryptographic primitives, and federated learning mechanisms. Furthermore, the interplay between energy efficiency and security overhead is critically analyzed. Key open challenges, including scalability, real-time attack detection, privacy preservation, and standardization gaps, are identified, and a forward-looking research agenda is proposed. This review aims to serve as a consolidated reference for researchers and practitioners seeking to develop secure, energy-aware, and intelligent AIoT ecosystems.