Edge Computing Pipelines for Distributed Analytics in IoT Systems

Abstract

The Internet of Things (IoT) systems generate vast amounts of data from numerous connected devices, posing significant challenges in terms of data processing, latency, and bandwidth utilization. Traditional cloud-based analytics systems face limitations, especially in real-time data processing. Edge computing, which brings computation closer to the data source, presents an ideal solution to overcome these challenges. This paper explores the design and implementation of edge computing pipelines for distributed analytics in IoT systems. It discusses the architecture of edge computing in IoT, the role of distributed analytics, and the flow of data through edge analytics pipelines. The paper also examines the benefits of edge computing, such as reduced latency, bandwidth efficiency, and enhanced real-time decision-making. Furthermore, we address the challenges in scaling, securing, and maintaining edge devices and propose various applications across smart cities, industrial IoT, healthcare, and agriculture. Finally, the paper highlights emerging trends and future directions for enhancing edge analytics capabilities in the ever-evolving IoT ecosystem.