Edge Computing for Real-Time Data Analytics in IoT

Abstract

Edge computing has emerged as a transformative paradigm for enabling real-time data analytics in Internet of Things (IoT) applications. By processing data closer to the source, edge computing reduces latency, alleviates bandwidth constraints, and enhances data privacy. This paper explores the integration of edge computing in IoT ecosystems, focusing on its role in real-time data analytics. We examine the architectural components of edge computing systems, including edge devices, edge servers, and cloud integration. The paper also discusses various data analytics techniques suitable for edge environments, such as stream processing, machine learning inference, and anomaly detection. Additionally, we analyze the benefits of performing data analytics at the edge, including improved scalability, reduced operational costs, and enhanced security. Case studies from industrial IoT scenarios, such as predictive maintenance, quality control, and supply chain optimization, are presented to illustrate the practical applications and effectiveness of edge computing. The paper concludes by identifying the challenges associated with deploying edge computing solutions, including resource constraints, interoperability issues, and the need for efficient orchestration mechanisms. Future research directions are proposed to address these challenges and further advance the field of edge computing for real-time data analytics in IoT applications.