Edge Computing: Architecture, Applications, and Future Challenges in a Decentralized Era

Abstract

This paper presents a detailed examination of edge computing advancements, emphasizing its role in reducing latency and enhancing real-time data processing by positioning computational resources closer to data sources. Using a robust methodological framework, this study evaluates decentralized edge architectures, edge nodes, and gateways to demonstrate their effectiveness in various Industry 5.0 applications, including smart cities, autonomous systems, and healthcare. The proposed model achieves a substantial 40% reduction in latency and 35% improvement in resource efficiency, underscoring its suitability for environments requiring immediate, localized data processing. Moreover, with a 95% data privacy index via encryption protocols, the model ensures secure data management, a crucial feature for privacy-centric applications. Our analysis includes case studies illustrating edge computing's integration with IoT and 5G, which not only reduces data transmission latency but also supports scalability for high-demand applications. The study identifies significant challenges related to security, interoperability, and adaptive resource management in decentralized settings. Addressing these challenges, we explore advanced machine learning models for real-time adaptability and propose future directions aimed at enhancing interoperability and security standards. These results underscore edge computing's foundational role in enabling the real-time, secure, and responsive systems central to Industry 5.0, laying a path for continued advancements in adaptive, decentralized processing frameworks.