Employing a deep learning technique to categorize Internet of Things (IoT) traffic in a smart city context

The exponential increase in quantity of cars on the roads of smart cities has led to traffic jams, air contamination, and delays in the delivery of essential goods. Every day road accidents continue to be a leading cause of death and serious injury. The Internet of Things-based Traffic Management System is utilized to manage traffic flow, ensure safe data transfer, and spot accidents. Autonomous vehicles and smart devices have sensors built into an IoT-based ITM system to help with identification, data collection, and transmission. Connectivity options for the Internet of Things are being severely tested by the proliferation of IoT devices and applications. This can lead to severe difficulties in the allocation of the spectrum, the resources needed to analyze the IoT data, and the allowable delay for important spare situations throughout the end-to-end communication chain. Therefore, accurate categorization or forecasting of the IoT devices' time-varying traffic characteristics is essential. However, the difficulty of making this sort of categorization remains open. Current approaches tend to be based on machine learning methods, which have a high computational cost despite not taking into account the traffic's fine-grained flow features. In this research, we address this issue by developing a two-stage categorization system based on deep learning for IoT devices in a smart city that draws on both connectivity and statistical information. In order to excerpt the internet and statistical characteristics set for various IoT strategies, we must do data cleaning and pre-processing. Based on the results of the tests, it appears that the proposed categorization has a 99% chance of being correct.