Abstract
The application of the Internet of Things (IoT) in a smart city improves its efficiency in terms of communication and installation costs by scaling geographical distance through intelligent devices and digital information. Different applications in a smart city, including health care, road safety, industry and home automation, rely on the IoT. Considering the significance of the IoT in smart city road applications, this manuscript introduces a classifier matrix recognition system (CMRS) for improving real-time traffic optimization. This classifier matrix system performs an independent and matching analysis of the real-time traffic images and constructs a decision factor for deriving its conclusion. The conclusion is served as responding notifications through the connected IoT systems for the users employing roadside-communication-assisted applications. CMRS exploits the advantages of block classification and matrix operations for improving the correlation accuracy and similarity index. The experimental results indicate that the proposed CMRS improves correlation accuracy with a high similarity index and less processing time and dissimilarity rate.