AI Enabled Edge and Fog Computing for Intelligent, Scalable, and Real-Time Data Processing in Next-Generation Networks

Abstract

The rapid proliferation of Internet of Things (IoT) devices and the exponential growth of data traffic have driven the need for decentralized computing paradigms. Traditional cloud computing architectures face significant challenges related to latency, bandwidth consumption, and real-time responsiveness. To address these limitations, Edge and Fog Computing have emerged as complementary paradigms, extending computational intelligence closer to data sources. With the integration of Artificial Intelligence (AI), these distributed frameworks gain the ability to autonomously process, analyze, and optimize data flows at the network edge. This paper provides an in-depth exploration of AI-enabled Edge and Fog Computing, discussing their architectures, applications, and synergy in achieving intelligent, low-latency, and context-aware computing. The paper also highlights major challenges, existing solutions, and potential research opportunities to strengthen the future of AI-driven distributed computing infrastructures.

KEYWORDS: AI-enabled computing, Edge computing, Fog computing, Distributed intelligence, IoT, Cloud continuum, Low-latency processing, Autonomous systems