This paper investigates the application of cooperative relaying systems with non-orthogonal multiple access (NOMA) in low-altitude intelligent networking-enabled medical Internet of Things (IoT) and analyzes their transmission performance. First, to enhance the communication quality of remote base stations, we deploy a relaying unmanned aerial vehicle (UAV). A two-slot NOMA cooperative transmission mechanism is proposed accordingly. Next, for the NOMA-enhanced UAV-relayed smart healthcare system under Rician fading channels, an exact closed-form expression for the achievable rate is derived using the incomplete Gamma function. Then, to improve computational efficiency, a low-complexity approximation method based on Gauss–Chebyshev quadrature is designed, overcoming the high complexity of the exact expression. Finally, the simulation results validate a close match between the proposed approximation and the exact values (average approximation error below 6.17%), and demonstrate superior achievable rate performance compared to three state-of-the-art schemes.

Ultra-dense networking (UDN) in air–ground integrated Internet of Vehicles (IoV) networks promises substantial gains in network capacity. However, the mobility of both unmanned aerial vehicle (UAV) base stations and ground vehicles makes handovers increasingly frequent and drives up signaling overhead, which compromises the low-latency and high-reliability requirements of IoV applications. To address this issue, this paper proposes an active mobility management scheme based on virtual air–ground cells (VAGC) for air–ground integrated IoV networks. We construct a long short-term memory (LSTM) neural network to predict vehicle trajectories, and we enhance the prediction accuracy by incorporating distance and azimuth information. Within the software-defined networking (SDN) controller, we design a multifunctional coordination module that creates VAGCs according to the prediction results and optimizes UAV deployment, thereby enabling timely updates of vehicle-specific UAV activation sets. Simulation results show that the proposed scheme reduces handover latency and lowers signaling overhead by approximately 35% compared with conventional passive mobility management schemes. The handover failure rate (HFR) decreases as the capacity-tolerance parameter $\delta$ increases. The performance metric reaches its maximum value of 0.53 when the prediction period is 3 s.