Internet of Things-based Fog and Cloud Computing Technology for Smart Traffic Monitoring

Abstract Internet of Things (IoT) is changing the world by connecting billions of physical and virtual objects with distinctive identities to the Internet. This fusion results in generating huge volumes of data that might not be manageable using today's storage and data analytics technologies. Although cloud computing offers services to tackle this issue at infrastructural level, its efficiency for time sensitive applications (e.g. oil, gas, and traffic monitoring) is still questionable. Arguably, transferring massive amount of data to the cloud for storage and processing may lead to cloud overloading and saturation of network bandwidth. In this study, an integrated fog and cloud computing framework is introduced to overcome the limitations of real-time analytics, latency and network congestion of basic cloud services for traffic monitoring. The proposed approach is implemented to prototype a smart traffic monitoring system (STMS). The proposed monitoring system is designed for congestion monitoring and traffic light management. It can also be tuned to detect traffic incidents that requires immediate assistance during congestion. In this framework, a tiny computer-on-module serves as a fog node to collect real-time data from geographically distributed sensors and to transfer it to the cloud for storage and processing. The results show the efficiency of the fog network in improving the performance of the cloud platform in terms of reducing the response time and increasing the bandwidth. Furthermore, the proposed integrated fog and cloud framework is interfaced with Tweeter to send alerts about traffic congestion to be subscribed users in the form of Tweet messages.