IoT-Enabled Deployment Optimization for Urban Low-Altitude Airspace Surveillance Networks

In the context of widespread applications of unmanned aerial vehicles (UAVs), low-altitude airspace surveillance has emerged as a critical task for public security. However, current low-altitude surveillance systems mainly rely on single-point device deployment, which provide limited coverage and lack the capability to ensure reliable UAV detection and tracking. Therefore, the development of an efficient Internet of Things (IoT)-enabled low-altitude surveillance network has become an urgent necessity. This paper proposes a new deployment optimization approach for low-altitude airspace surveillance network (LASN). A detector signal-UAV accessibility strategy is proposed to explicitly account for urban structural characteristics. Then, a novel metric, calledUAV-Exposure, is introduced to quantify surveillance effectiveness within the LASN. Finally, a hub–spoke-based network deployment model is formulated to determine the optimal solution of LASN nodes under realistic constraints. Computational results show that compared with traditional methods, the planned LASN solution has substantially enhanced the monitoring performance of UAVs and improved low-altitude security level.