Edge Computing and Its Impact on Real-Time Data Processing for IoT-Driven Applications

Abstract

The proliferation of Internet of Things (IoT) devices has led to an unprecedented surge in data generation, necessitating novel approaches to data processing and analysis. Traditional cloud-centric computing models, while powerful, introduce significant latency and bandwidth constraints that impede the performance of time-sensitive IoT applications. Edge computing has emerged as a promising paradigm that brings computational resources closer to data sources, enabling real-time processing and analysis. This research article examines the technological foundations, architectural frameworks, implementation strategies, and real-world applications of edge computing in the context of IoT-driven systems. Through comprehensive analysis and empirical evidence, this study demonstrates how edge computing addresses critical challenges in real-time data processing, including latency reduction, bandwidth optimization, and enhanced privacy and security. The research further explores the synergistic relationship between edge computing and complementary technologies such as 5G networks, artificial intelligence, and blockchain, highlighting their collective potential to revolutionize IoT ecosystems. Case studies across healthcare, industrial automation, smart cities, and autonomous vehicles illustrate the transformative impact of edge computing on real-time IoT applications. The article concludes with an examination of current limitations, emerging trends, and future research directions that will shape the evolution of edge computing in IoT environments.