The three-dimensional deployment of Unmanned Aerial Vehicles (UAVs) has attracted extensive attention, especially for the Internet of Vehicles (IoV) in an emergency or to help the overloaded edge servers in traffic peaks. However, most existing works assume a two-dimensional road to simplify the design and modeling, while ignoring the interchange bridges scenario. In this scenario, UAVs deployment will face new challenges: the line-of-sight (LoS) transmission between the vehicles and UAVs is weakened due to the occlusion of the bridge body and vehicle movement. Meanwhile, energy consumption and the quantity of UAVs also need to be considered. In this paper, we propose an energy-aware 3D-deployment of UAVs, named 3D-UAV, to guarantee a high uplink rate with a minimized number of UAVs in IoV with Highway Interchange. First, considering the channel gain over bridges, 3D-UAV divides vehicles into several clusters. In each time slot, the number of clusters is iteratively optimized. Based on the clustering result, the flight altitude of the UAV is optimized in a stochastic gradient ascent (SGA) way aiming at maximizing the average uplink rate of transmission. Numerical results show that the proposed 3D-UAV can cover all vehicles on the highway interchange with the number of UAVs close to the theoretical lower bound. Meanwhile, it outperforms SOA, DRL, and HOLD methods in terms of the uplink rate and energy.

For massive access to the Internet of Things, edge computing servers are installed on cellular ground base stations (GBSs) with fixed geographical locations, which easily suffer from traffic overload of the end user (EU) with high density and mobility. To provide reliable and flexible offloading service, unmanned aerial vehicles (UAV) are explored to assist edge computing, which relieves the computation offloading pressure of both EUs and GBS. However, most existing UAV researches focus on trajectory design to reduce offloading delay, which ignoring the variability of user distribution and the energy limitation of UAV. This article proposes a novel UAV-assisted edge computing framework, named as HOTSPOT, which locates the UAV in 3-D space according to the time-varying hot spot of user distribution and provides the corresponding edge computing offloading assistance. By formulating the UAV positioning problem into a maximum clique problem, a light-weighted deterministic algorithm is proposed based on stochastic gradient descent to search the optimal location of UAV. With the elaborate UAV position, HOTSPOT further gives an opportunistic offloading balanced scheme to reach low latency. Simulation results show that when the GBS load is 75%, HOTSPOT reduces the average offloading delay by 33%. When the GBS load reaches 90%, the average delay reduction is up to 80%.