Cloud and Edge Computing Integration: Transforming Advanced Computing Systems for IoT Applications

Abstract

The Internet of Things (IoT) has revolutionized industries by connecting billions of devices for real-time data collection and control in areas such as smart cities, healthcare, and autonomous vehicles. While cloud computing has been essential for managing and analyzing this data, its limitations—such as high latency, bandwidth constraints, and data privacy concerns—have become more apparent as IoT systems grow. For real-time applications like autonomous driving, cloud-based processing introduces delays that can affect performance. Additionally, the surge in IoT devices has increased network congestion and raised privacy risks due to the transmission of sensitive data over long distances. Edge computing addresses these challenges by processing data closer to its source, reducing latency, improving bandwidth efficiency, and enhancing privacy. It is particularly beneficial for applications requiring immediate decisions. However, edge computing lacks the scalability and processing power of the cloud, which remains crucial for tasks like big data analytics and machine learning. The integration of cloud and edge computing offers a hybrid solution that combines the low-latency benefits of edge with the cloud's scalability. This paper explores the evolution of cloud-edge integration, its technical architectures, and how this hybrid model is transforming IoT applications. The research highlights how cloud-edge integration is reshaping industries by enabling smarter, more efficient IoT systems that support real-time data processing and enhanced security.