In recent years, unmanned aerial vehicles (UAVs) have gained popularity in various applications and services in both the military and civilian domains. Compared with the single-UAV scenario, flying ad hoc networks (FANETs) consisting of ground stations (GSs) and UAVs have the advantages of flexible configuration and wide coverage. However, due to significant mobility and highly dynamic topology, designing reliable and efficient routing protocols for FANETs is a challenging task. In this paper, we consider a network that comprises multiple flying UAVs and GSs to transfer messages by multi-hop relaying. We propose a routing protocol, named course-aware opportunistic routing for FANETs (CORF). The UAVs cooperatively exchange aeronautical data with others. The source UAV node (SUN) calculates the transfer probabilities to different neighbors by jointly considering the positions of its neighbors and the destination node. Based on the direction information and the transfer probabilities, the SUN selects the next-hop relay nodes among the neighbor UAVs and GSs. This process continues until the destination node receives the message. The simulation results demonstrate that, the proposed CORF protocol achieves significant performance superiority as compared with the traditional protocols in terms of message delivery rate and network latency.INDEX TERMS Course information, routing protocol, transfer probability, UAV.

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.

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In this paper, we propose a high-altitude platform (HAP) and unmanned aerial vehicles (UAVs) collaboration framework in non-orthogonal multiple access (NOMA)-enabled Internet of Things (IoT) networks with the presence of an eavesdropping UAV. For the considered framework, we investigate the uplink secure transmission by optimizing channel allocation from UAVs to HAP, users’ power, and UAVs’ three-dimensional (3D) position. To solve this non-convex problem, we adopt the K-means cluster pair algorithm to divide paired users into different groups and each cluster can be served by a corresponding UAV. Then, the formulated optimization problem is decoupled into three subproblems and tackled iteratively based on the block coordinate descent (BCD) algorithm. Finally, simulation results verify that the proposed network architecture can achieve a higher secure rate, faster convergence evolution, and lower complexity in comparison with the current works.

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