Smart city traffic optimization using IoD and IoT integration

Urban traffic congestion, along with the resulting fuel waste and pollution, poses increasing challenges as city populations grow. There is a critical need for techniques that mitigate these effects while maintaining traffic efficiency. This study proposes an integrated framework that combines the Internet of Things (IoT) infrastructure and the Internet of Drones (IoD) to enhance urban traffic management. IoT sensors monitor real-time traffic conditions, while Roadside Units (RSUs) collect and process data to deliver timely updates to vehicles. Drones dynamically extend communication coverage by acting as mobile relay nodes, accelerating traffic information dissemination across wider areas, particularly those with sparse connectivity. Drone placement was optimized for maximum coverage using the Particle Swarm Optimization (PSO) algorithm. Experiments conducted in two urban scenarios-Dammam (Saudi Arabia) and Doha (Qatar)-used the SUMO simulator, Traffic Control Interface (TraCI), and Python to implement communication protocols and adaptive rerouting mechanisms. The findings suggest that integrating IoT and drones can substantially reduce travel delays and vehicular emissions while maintaining real-time operational efficiency. In Dammam, emissions and travel times decreased by up to 40.99% and 32.05%, respectively, and in Doha by up to 48.78% and 43.92%. These results indicate that coordinated IoT-IoD systems can support sustainable urban mobility by improving traffic flow and contributing to cleaner air.