Unmanned aerial vehicles (UAVs) have a great potential for improving the performance of wireless communication systems due to their high mobility. In this correspondence, we study a UAV-enabled data collection system, where a UAV is dispatched to collect a given amount of data from a ground terminal (GT) at fixed location. Intuitively, if the UAV flies closer to the GT, the uplink transmission energy of the GT required to send the target data can be more reduced. However, such UAV movement may consume more propulsion energy of the UAV, which needs to be properly controlled to save its limited on-board energy. As a result, the transmission energy reduction of the GT is generally at the cost of higher propulsion energy consumption of the UAV, which leads to a new fundamental energy tradeoff in ground-to-UAV wireless communication. To characterize this tradeoff, we consider two practical UAV trajectories, namely circular flight and straight flight. In each case, we first derive the energy consumption expressions of the UAV and GT and then find the optimal GT transmit power and UAV trajectory that achieve different Pareto optimal tradeoffs between them. Numerical results are provided to corroborate our study.

Unmanned aerial vehicle (UAV) has been widely applied in internet-of-things (IoT) scenarios while the security for UAV communications remains a challenging problem due to the broadcast nature of the line-of-sight (LoS) wireless channels. This paper investigates the security problems for dual UAVassisted mobile edge computing (MEC) systems, where one UAV is invoked to help the ground terminal devices (TDs) to compute the offloaded tasks and the other one acts as a jammer to suppress the vicious eavesdroppers. In our framework, minimum secure computing capacity maximization problems are proposed for both the time division multiple access (TDMA) scheme and non-orthogonal multiple access (NOMA) scheme by jointly optimizing the communication resources, computation resources, and UAVs’ trajectories. The formulated problems are non-trivial and challenging to be solved due to the highly coupled variables. To tackle these problems, we first transform them into more tractable ones then a block coordinate descent based algorithm and a penalized block coordinate descent based algorithm are proposed to solve the problems for TDMA and NOMA schemes, respectively. Finally, numerical results show that the security computing capacity performance of the systems is enhanced by the proposed algorithms as compared with the benchmarks. Meanwhile, the NOMA scheme is superior to the TDMA scheme for security improvement.

With the high altitude and flexible mobility, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) is becoming a promising technology to cope with the computation-intensive and latency-critical task in prospective Internet of Things. In this paper, we propose a novel MEC system with several ground servers at access points and one aerial server carried by UAV. To balance the vital metrics of the MEC system, computation bits and energy consumption, we aim to maximize the weighted computation efficiency of the system, subject to the constraints on communication and computation resources, minimum computation requirement and UAV’s mobility. To this end, a joint optimization problem with the goal of weighted computation efficiency maximization is formulated. First, we analyze the problem and transform it into an equivalent tractable form. Then, we solve the challenging non-convex problem by jointly optimizing the computation task assignment, time slot partition, transmission bandwidth and CPU frequency allocation, transmit power allocation, and UAV’s trajectory, based on the Dinkelbach’s method, Lagrange duality and successive convex approximation technique. Furthermore, we propose an alternative computation efficiency maximization algorithm, followed by the convergence and complexity analysis. Finally, numerical simulations show that our proposed algorithm significantly improves the computation efficiency compared to benchmark schemes. It is also validated that the proposed algorithm effectively obtains a good tradeoff between the computation task bits and energy consumption of the system.

This paper investigates the secrecy energy efficiency (SEE) maximization problem for unmanned aerial vehicle (UAV) enabled mobile relaying system, where a high-mobility UAV is exploited to assist transmitting confidential information from a ground source to a legitimate ground destination, in the presence of a potential eavesdropper. We aim to maximize the SEE of the UAV by jointly optimizing the communication scheduling, power allocation, and UAV trajectory over a given time horizon. The formulated problem is non-convex that is challenging to be optimally solved. To make the problem more tractable, we decompose the problem into three subproblems, and propose an efficient iterative algorithm that alternately optimizes each block of the variables with the others fixed. Moreover, the practical scenario with uncertain eavesdropper location is investigated to evaluate the performance of the proposed solution. Double circular flight and running track shape flight cases are considered to drawn more insights. Simulation results show that the proposed design significantly improves the SEE of the UAV, as compared to the benchmark schemes.

Inthis paper, we investigate an unmanned aerial vehicle (UAV)-assisted edge computing network in a sophisticated three-dimensional (3D) scenario, e.g., a post-disaster urban area with multiple mobile ground terminals (GTs) for resilience. The mission of the UAV is to first collect a set of computation tasks from multiple GT sources, then process them and make joint decisions (computation results) which are finally transmitted to another set of GT destinations. An efficient design is provided aiming at minimizing the total time cost including costs for the task offloading, computations, and decision transmissions, via jointly designing the UAV's trajectory and the scheduling of communications from and to different GTs. In particular, we take into account the practical assumptions on the GTs' mobility, on the obstacle avoidance to 3D buildings, and on that the UAV is possible to fly among buildings, i.e., the flying altitude can be lower than the buildings' heights. To address the established complex and non-convex problem, we first provide a problem transformation by exploiting Markov decision process (MDP), and then propose a deep reinforcement learning (DRL) approach-based solution with a multi-step dueling DDQN (D3QN) method. As a result, an efficient solution is achieved, where the UAV acts as the agent to continuously explore and improve its mobile strategy during interacting with the environment under the proposed method. The simulation results show the advantages of the proposed 3D trajectory design in comparison to a 2D one. In addition, the results confirm the robustness of the proposed design in terms of different GTs' mobility models and UAV flying height/area limits, and illustrate the superiority of unrestricted feasible domains for the UAV flight.