Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with a MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things (IoT) in a finite period. In this paper, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point (AP) via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV's flight energy by optimizing the bits allocation, time slot scheduling and power allocation as well as UAV trajectory design. As the formulated problem is nonconvex and difficult to find the optimal solution, we solve the problem by two parts, and obtain the near optimal solution with within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.

This paper investigates the secrecy energy efficiency (SEE) maximization problem for unmanned aerial vehicle enabled mobile relaying system, where a high-mobility UAV is exploited to assist delivering the confidential information from a ground source to a legitimate ground destination with the direct link blocked, in the presence of a potential eavesdropper. We aim to maximize SEE of the UAV by jointly optimizing the communication scheduling, power allocation, and UAV trajectory over a finite time horizon. The formulated problem is a mixed-integer non-convex optimization problem that is challenging to be solved optimally. To make the problem tractable, we decompose the problem into three subproblems, and propose an efficient iterative algorithm that alternately optimizes the subproblems. In addition, two special cases are considered as benchmarks in this paper. Simulation results show that the proposed design significantly improves the SEE of the UAV, as compared to the benchmarks. Index TermsUAV communication, physical layer security, mobile relaying, secrecy energy efficiency, trajectory design. Recently, due to the high mobility, on-demand deployment/placement and line-of-sight (LoS) link, unmanned aerial vehicle (UAV) has attracted significant research interests in wireless communications [1], such as for traffic offloading, aerial BSs, mobile relaying [2]-[6], information broadcasting and data collection [7]-[10]. Compared to the traditional terrestrial communications, UAV-enabled communications have more flexible mobility and potentially reduced cost. For one thing, UAV-enabled communication systems is especially suitable to be applied for on-demand coverage or unexpected events due to the swift and flexible deployment of UAV. For another, there is more likely to have line-of-sight (LoS) link between UAV-ground link, which can significantly improve link capacity. In addition, UAV-enabled communications provide a new degree of freedom for performance enhancement by trajectory design. Generally speaking, UAV-enabled communications can best suit the communication requirement by trajectory optimization, where the UAV is subject to practical mobility constraints, such as initial/final locations, maximum speed, and maximum acceleration. However, the limited on-board energy of a UAV is one of the biggest challenges in UAV-enabled communications since a UAV needs much additional propulsion energy to maintain aloft. As a result, the authors in [11] obtained the analytical UAVs energy consumption model for fixed-wing UAVAs, which was expressed as a function with respect to UAVs speed and acceleration. Based on this, the work [6] studied the spectrum and energy efficiency maximization issues in a UAV-enable communication system, in which the UAV trajectory and transmit power are jointly optimized. In particular, the UAVs trajectory is needed to be optimized to achieve a high-rate communication with the ground nodes, while the energy consumption of the UAV is expected to be lower as much as possible [12]. One particula...

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