Optimized UAV-Based Connectivity Solutions for Urban IoT Networks

The rapid growth of the Internet of Things (IoT) in cities has resulted in a dense deployment of devices such as sensors and smart meters. These devices necessitate a steady and reliable internet connection. Traditional cellular networks, such as NB-IoT and LTE Cat-M, need help to meet these demands where the performance of current infrastructure has severely degraded due to high device density. As an alternative to existing cell towers, Unmanned Aerial Vehicle Base Stations (UAV-BSs) are proposed in this paper to address this challenge. These UAV-BSs act as mobile cell towers and are strategically placed in regions with a high concentration of IoT devices to improve network coverage and capacity. Furthermore, Tethered UAVs (TUAVs) powered by robotic Cellular-on-Wheels (COW) units support a robust backhaul link between the UAV-BSs and the network core. The study used the K-means algorithm, a machine-learning technique that clusters IoT devices according to their spatial distribution, to select the optimal places for the UAV-BSs. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are then used to refine the positions, systematically investigating multiple UAV-BSs placements to discover the most effective arrangement. Finally, the paper utilizes a new data transmission technique, the ‘Mini slots’ scheduling algorithm, to improve data exchange efficiency between IoT devices and UAV-BSs.