Real-Time Data Processing with Edge Computing: Challenges and Solutions

Abstract

The increasing demand for real-time data processing in applications such as autonomous vehicles, industrial automation, and smart cities has intensified the focus on edge computing. Edge computing, which processes data closer to the source, addresses the limitations of centralized cloud computing by reducing latency and improving bandwidth efficiency. This paper explores the critical challenges associated with implementing real-time data processing at the edge, including resource constraints, network reliability, data privacy, and scalability. We provide an overview of recent technological advancements and architectural frameworks that address these challenges. In addition, this study evaluates edge computing solutions such as lightweight machine learning algorithms, efficient data compression techniques, and decentralized security measures. By analyzing current strategies and their efficacy in real-world applications, this paper contributes to understanding how edge computing can be optimized for diverse, latency-sensitive environments. Finally, we discuss the future potential and open research areas in edge computing for real-time applications.