Developing a Fog Computing-based AI Framework for Real-time Traffic Management and Optimization

Abstract

This research is based on fog computing studies, in which we explored, designed, and developed fog-computing AI-based real-time traffic management and optimization frameworks.  To detect the interaction between fog computing technologies and artificial intelligence. In this research, the proposed framework operates at the edge of networks to facilitate rapid data collection and preprocessing to enhance the decision-making ability of systems. In a given architecture, there are various layers, like fog, edge, cloud, and AI layers. Every layer plays a vital role in data gathering and processing, from analytics to traffic optimization. They integrated the data from various devices and sources, like traffic sensors (ultrasonic), cameras, and GPS-signal data. This framework enables effective traffic prediction and manages and optimizes their system. For the real-time data preprocess in fog nodes, combined with optimizing techniques such as the genetic algorithm, particle swarm optimization (PSO), and ant colony optimization (ACO), these technologies allow dynamic traffic signal control and route optimization. They are used to ensure their scalability and reliability when employing deployment strategies. Experimental evaluations are conducted to reduce congestion and quick response times with various traffic scenarios. At the end of the comparative analysis, previous studies are highlighted to be compared with their techniques, results, and limitations with our proposed model. This real-time AI framework for fog computing to optimize traffic and manage it gives the easiest solution for addressing urban traffic and identifies the challenges to enhancing the transportation system to make it more effective and efficient in the future.