Edge Artificial Intelligence for IoT: Architectures, Applications, Challenges, and Future Directions

Abstract

The exponential growth of Internet of Things (IoT) devices has resulted in massive volumes of continuously generated data, creating significant demands on communication networks and centralized cloud infrastructures. Edge Artificial Intelligence (Edge AI) addresses these challenges by bringing machine learning and data-processing capabilities closer to IoT devices and data sources. This review provides a comprehensive analysis of Edge AI architectures, technologies, and applications within IoT ecosystems. The study examines edge-based machine learning, deep learning inference, federated learning, model compression, and distributed intelligence techniques. Applications across healthcare, smart cities, industrial automation, autonomous transportation, agriculture, and environmental monitoring are reviewed. The advantages of Edge AI in terms of reduced latency, bandwidth utilization, privacy, reliability, and real-time decision-making are analyzed. At the same time, challenges involving limited computational resources, energy constraints, heterogeneous hardware, model optimization, security, and distributed data management are discussed. The review further explores emerging concepts such as TinyML, edge-cloud collaboration, federated learning, and hardware acceleration. Future research opportunities are identified for developing lightweight, secure, energy-efficient, and adaptive AI models capable of supporting large-scale IoT deployments.