OPTIMIZED RESOURCE ANALYSIS IN VANET CLOUD USING FOG ENABLED REAL-TIME TRAFFIC CONTROL SYSTEM

Abstract

The rapid rise in vehicular traffic has led to a significant demand for efficient, work and economically feasible intelligent traffic management systems. This research work introduces a novel solution that integrates fog computing (FC) with Internet of Things (IoT) to manage real-time traffic congestion. Utilizing a two-tiered architecture consisting of data gathering mechanisms, real-time processing fog computing nodes, the system offers predictive insights into traffic behavior. Through real-time traffic data processing, selective routing for emergency vehicles, and event-based traffic management, it aims to alleviate urban traffic congestion. The system’s edge computing capabilities enable faster response times and lower latency. This intelligent system not only prioritizes emergency vehicles but also predicts traffic flows for special events, enhancing urban mobility. Cloud Computing (CC) supports system scalability, while FC optimizes energy efficiency. Furthermore, the proposed system presents commercial potential for municipalities and private sectors, as it can be monetized through licensing or Software as a Service (SaaS) models. Results from simulations demonstrate efficient resource utilization and reduced traffic congestion, positioning this solution as a cutting-edge tool for smart city initiatives.