In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on the UAV due to the limited computation ability. To counter the problems above, we first model and analyze the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV. Then, the vehicle offloading problem is formulated as a multi-objective optimization problem by jointly considering the task offloading, the resource allocation, and the security assurance. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed scheme achieves significant performance superiority compared with other schemes in terms of the successful task processing ratio and the task processing delay.

Unmanned aerial vehicle mounted base stations (UAV-BSs) are expected to become an integral component of future intelligent transportation systems, which can provide seamless coverage for vehicles on highways with poor cellular infrastructures. Motivated by the above, this paper proposes an aerial-ground integrated vehicular networking architecture, based on which a UAV-vehicle collaboration perspective is proposed. Specifically, an emerging vehicle-to-UAV (V2U) and vehicle-to-vehicle (V2V) collaboration framework is first presented to facilitate diverse vehicular applications. Next, we investigate the coverage radius maximization problem by optimizing the UAV-BS altitude. Meanwhile, by taking the channel state information (CSI) feedback delay into account, we formulate a V2U communication sum rate maximization problem by optimizing the power control and spectrum allocation, which is constrained by the capacity and reliability requirements. Then, we derive the closed-form expression of optimal UAV-BS altitude. Afterwards, we decouple the formulated sum rate maximization problem, and devise an efficient algorithm with polynomial complexity, where the optimal power control and spectrum sharing are solved. Finally, simulation results demonstrate that the maximum coverage radius and optimal UAV-BS altitude can be achieved by our proposed scheme in different urban environments. In addition, our designed scheme can effectively improve the V2U communication sum rate in comparison with the current works.

In the agricultural Internet of Things (Agri-IoT), the uncrewed aerial vehicle (UAV) can serve as a mobile relay to facilitate line-of-sight transmissions for agricultural sensors, especially in farmlands with complex terrain or numerous obstructions. Additionally, the integration of techniques such as simultaneous wireless information and power transfer (SWIPT) and multiple-input single-output (MISO), combined with non-orthogonal multiple access (NOMA) communications, not only supports a higher number of device connections but also provides an essential power supply to cell-edge sensors. Motivated by the above, we propose a collaborative MISO-NOMA communication mechanism in UAV-assisted Agri-IoT. Specifically, the UAV functions as a static relay, and the NOMA-enhanced decode-and-forward relay protocol and SWIPT technique are used in the cell-center relaying UAV. The selection combining technique is employed for cell-edge sensors to obtain the optimal quality signal from multiple antennas of the base station (BS). To further improve the channel capacity, we propose a transmit antenna selection (TAS) strategy for the base station equipped with multiple antennas. Different from existing strategies (such as maximizing harvested energy or direct-link performance), the proposed TAS strategy aims to achieve optimal outage performance at cell-edge sensors, rather than suboptimal performance. Then, we derive closed-form and approximate solutions for the outage probability of cell-edge sensors. These solutions can provide significant insights into the impact of MISO-NOMA communications in UAV-assisted Agri-IoT. Finally, the simulation results indicate that the proposed TAS strategy outperforms current state-of-the-art schemes in reducing the outage probability. Moreover, our simulation experiments verify that the derived approximate solution closely aligns with the closed-form solution.

In emergency rescue scenarios, air-to-ground (A2G) integrated mobile ad hoc networks (MANETs) face challenges such as dynamic topology, limited resources, and unstable communication opportunities. To address these issues, we propose a position-probability-resource cooperative routing (PP-RCR) algorithm. First, Kalman filtering is applied to predict the future positions of nodes and accurately identify effective communication opportunities, which can mitigate link disruptions caused by topology dynamics. Then, a multi-dimensional relay selection criterion is designed by considering the transmission reliability, node resources, and message attributes, enabling adaptive relay selection among heterogeneous nodes. Based on this, a dynamic message replication and deletion strategy is introduced according to the urgency of messages, and the transmission path is optimized. Finally, the simulation results show that, in typical rescue scenarios, the proposed PP-RCR algorithm achieves higher delivery ratio and lower average delivery delay compared with state-of-the-art algorithms.

The rapid advancement of intelligent transportation systems necessitates reliable vehicular communication, yet traditional infrastructure struggles with coverage limitations in dynamic environments. To address the above issue, this letter investigates the uplink outage probability in cooperative communication between the autonomous aerial vehicle (AAV) and intelligent connected vehicles (ICVs) using the full-duplex multiple-input multiple-output (MIMO) technique. First, for non-static MIMO scenarios, we develop a joint effect model by jointly considering the self-interference, co-channel interference and noise, aiming to maximize channel gain. Next, we derive a closed-form expression for the uplink outage probability by employing the Laplace transform of the interference. To reduce complexity, an approximate expression for the uplink outage probability is proposed according to a dynamic truncation criterion. Based on this, we derive a theoretical upper bound for the approximation error. Finally, the simulation results demonstrate that the proposed scheme significantly reduces the outage probability compared to state-of-the-art schemes, with the approximate solution showing less than 6% error and requiring only 7% of the computational time of the closed-form solution. Additionally, the impact of transmission power, antenna configurations, and retained higher-order terms on the outage probability is thoroughly analyzed.

This paper investigates the integration of the non-orthogonal multiple access (NOMA) technique and autonomous aerial vehicles (AAVs) in Internet of Vehicles (IoV), aiming to provide flexible access and improve communication coverage for vehicle platooning. The goal is to accurately analyze performance and reasonably optimize network design for AAV-assisted vehicle platooning in NOMA-enhanced IoV. To achieve this, an analytical solution is derived for the average achievable rate from the lead vehicle to follower vehicles over Rician fading channels. Leveraging this analytical solution, the Gauss-Chebyshev integration is employed to obtain the approximate solution. Then, we formulate a problem of maximizing the sum of secure rates by optimizing the trajectory and spectrum allocation. The formulated problem is constrained by the security requirement and imperfect channel state information. Addressing the NP-hard nature of this problem, an iterative optimization algorithm is developed, incorporating Q-learning and the graph theory to alternately adjust the trajectory and spectrum allocation. Finally, the simulation results show that the approximate solution matches well with the analytical solution, and the gap is less than 6%. Moreover, the proposed scheme has a significant performance improvement in the sum of secure rates compared with the state-of-the-art schemes.