Cloud and edge computing collaboration for IoT-enabled traffic monitoring

Abstract

This paper explores a collaborative cloud and edge computing framework to enhance Internet of Things (IoT)-based traffic monitoring systems, vital for managing congestion in growing urban areas. With increasing vehicle numbers and urban density, traffic congestion is a significant challenge, leading to delays, pollution, and lower quality of life. IoT-enabled devices, like sensors and cameras, provide real-time monitoring for improved traffic management. However, the large data volume demands a hybrid infrastructure for efficient, secure handling. Our framework leverages cloud and edge computing to address these requirements. Processing data locally at the edge reduces latency, optimizes bandwidth, and boosts data security—essential for IoT in busy urban environments. Meanwhile, the cloud component enables advanced analytics and storage, supporting historical data analysis, predictive modeling, and long-term storage. Our study shows that this collaborative approach lowers latency and data transfer costs and improves the scalability and efficiency of smart traffic management. This research demonstrates the potential of a cloud-edge hybrid framework to transform traffic systems, offering a more adaptable, responsive, and sustainable solution for modern cities.