IoT-Enabled Smart Air Traffic Control for Drones in Urban Areas: AI-Driven Route Optimization and Energy Management
Khondaker Iffti Hasan Turjo, Mustakim Ahmed, Kazi Redwan, Md. Faruk Abdullah Al Sohan, Afroza Nahar, Mohammad Sakib Mahmood
Abstract
The growth of drone and Unmanned Aerial Vehicle (UAV) use in cities brings a new set of challenges to safely manage air traffic in crowded urban skies. This research proposes an innovative IoT-enabled and AI-driven air traffic control system designed specifically to meet the unique demands of urban drone applications such as deliveries, surveillance and disaster response. The proposed system integrates advanced AI techniques, namely Particle Swarm Optimization (PSO) for decentralized control, Ant Colony Optimization (ACO) for adaptive routing and Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. These methodologies enable real-time route optimization, collision avoidance and energy efficiency. A combination of three-dimensional (3D) urban navigation and Voronoi-based geofencing helps in avoiding obstacles and restricted areas. This proposed system also incorporates IoT-based battery monitoring for real-time energy management and utilizes hybrid communication networks to maintain seamless operations in high-density urban airspaces. Cellular, satellite and local access make for effective and dependable delivery of the communication network. Weather conditions are used in optimization to achieve adaptive routing. Case studies illustrate the practical implementation of this scalable framework, highlighting its ability to enhance safety, operational efficiency and sustainability in urban UAV operations.