Edge Computing in IoT: Enhancing Real-Time Data Processing and Decision Making in Cyber-Physical Systems

Abstract

The rapid proliferation of Internet of Things (IoT) devices has led to an exponential increase in data generation, necessitating new approaches to data processing and analysis. This manuscript explores the integration of edge computing within IoT frameworks, specifically focusing on its role in enhancing real-time data processing and decision-making in cyber-physical systems (CPS). By decentralizing data processing to the edge of the network, we can mitigate the latency, bandwidth, and privacy challenges associated with cloud-based solutions. The paper investigates various edge computing architectures and their applications across diverse sectors, including smart manufacturing, healthcare, and smart cities. Through case studies and empirical data, we demonstrate how edge computing not only improves response times and resource efficiency but also supports state-of-the-art machine learning algorithms designed for deployment in resource-constrained environments. The findings suggest that adopting edge computing in IoT ecosystems significantly enhances the operational capabilities of CPS, ultimately leading to smarter, more resilient systems adapted to real-time demands. This research lays the groundwork for future innovations in IoT and CPS integration, fostering advancements that leverage the full potential of connected environments.