Edge Artificial Intelligence: A Systematic Review of Evolution, Taxonomic Frameworks, and Future Horizons

2025-10-01

AI Summary

This paper systematically reviews the evolution, technical challenges, and future directions of Edge Artificial Intelligence (Edge AI). Addressing core constraints—including resource scarcity, security and privacy risks, high energy consumption, and unreliable connectivity—the study conducts a rigorous literature analysis guided by the PRISMA framework. It proposes a multidimensional taxonomy encompassing deployment location, computational hierarchy, application scenarios, and hardware architectures. The review integrates emerging paradigms such as TinyML, federated learning, and neuromorphic computing, identifying three key advancement pathways: continual learning, cloud-edge collaboration, and trustworthy AI integration. Furthermore, it synthesizes enabling technologies—including model compression, domain-specific accelerators, efficient communication protocols, and secure computation mechanisms. The resulting comprehensive reference framework bridges theoretical rigor and practical engineering guidance, serving both researchers and practitioners in the Edge AI domain.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

Edge Artificial Intelligence (Edge AI) embeds intelligence directly into devices at the network edge, enabling real-time processing with improved privacy and reduced latency by processing data close to its source. This review systematically examines the evolution, current landscape, and future directions of Edge AI through a multi-dimensional taxonomy including deployment location, processing capabilities such as TinyML and federated learning, application domains, and hardware types. Following PRISMA guidelines, the analysis traces the field from early content delivery networks and fog computing to modern on-device intelligence. Core enabling technologies such as specialized hardware accelerators, optimized software, and communication protocols are explored. Challenges including resource limitations, security, model management, power consumption, and connectivity are critically assessed. Emerging opportunities in neuromorphic hardware, continual learning algorithms, edge-cloud collaboration, and trustworthiness integration are highlighted, providing a comprehensive framework for researchers and practitioners.

Problem

• Analyzing evolution and taxonomy of Edge AI systems and frameworks
• Addressing resource limitations, security, and power consumption challenges
• Exploring emerging opportunities in hardware and algorithms for Edge AI

Innovation

• Embedding intelligence directly into edge devices
• Using specialized hardware accelerators and optimized software
• Employing federated learning and TinyML for processing