REAL-TIME DECISION-MAKING WITH EDGE COMPUTING

Abstract

Traditional cloud-centric approaches are facing increasing demands for instant data processing and significant challenges in meeting the latency requirements of modern applications. Edge computing emerges as a promising approach that brings computation and data storage closer to the edge of the network, enabling real-time decision-making with reduced latency and enhanced efficiency.

The paper provides a comprehensive overview of edge computing, describing its fundamental principles, architectural components, and key advantages over centralized cloud infrastructures. By minimizing data transfer latency, edge computing ensures that critical data analysis and processing occur close to data sources, resulting in enhanced responsiveness and improved user experiences.

The paper analyzes two case studies, including autonomous driving vehicles and finance, in order to highlight the applications of edge computing in areas where real-time decision-making is crucial.

In addition to discussing the benefits of edge computing, the paper addresses the potential challenges and constraints associated with  edge computing implementation. Security and privacy concerns in decentralized edge environments are explored, and several protection strategies are described.

Finally, the paper outlines the future of edge computing for organizations and the technology industry. In doing so, the paper discusses the potential of edge computing in supporting emerging technologies, such as 5G networks and autonomous systems.

Received: December 19, 2023
Accepted: February 2, 2024