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.

The explosive computation demands in the Internet of Things (IoT) have triggered the research interests on unmanned aerial vehicle (UAV) assisted mobile-edge computing (MEC) systems even though there are still many challenges, such as computing delay requirement, multi-UAV cooperation, and resource management. This letter focuses on the computing delay issue in MEC systems assisted by multiple UAVs with the goal of task completion time minimization. In particular, both the partial offloading and binary offloading modes are considered by jointly optimizing time slot size, terminal devices scheduling, computation resource allocation, and UAVs’ trajectories. Particularly, an non-LoS channel model is adopted for UAV-ground communication. To handle the formulated problems, we develop alternating optimization algorithms by invoking the successive convex approximation method, Karush-Kuhn-Tucker conditions and penalized method. Numerical results show that the completion time is significantly decreased by the proposed algorithms.

We propose an unmanned aerial vehicle (UAV) enabled integrated sensing, computing, and communication (U-ISCC) framework, where a treble-functional UAV performs local computation and also delivers its computational tasks to ground access points. Meanwhile, this UAV is employed to sense a target by using the unified beam waves. Our goal is to characterize the Pareto boundary between the computation capacity and the sensing beampattern gain, by which the fundamental trade-off of them is unveiled. To solve the challenging problem, we propose a joint optimization algorithm by applying the semidefinite programming method and the concave-convex procedure technique, which ensures the rank-1 constraint with a low complexity. Numerical results finally verify that the proposed algorithm explicitly depicts the performance region, and demonstrate that the UAV boosts the ability of sensing, computing, and communication to strike a balance by fully exploring its functions.

Mobile-edge computing (MEC) networks are facing limited coverage and harsh wireless transmission environments that severely hinder the computation capacity of the Internet-of-Things (IoT) devices. To overcome these issues, this article proposes a novel MEC framework empowered by an unmanned aerial vehicle (UAV) relay and a reconfigurable intelligence surface (RIS). To fully exploit the potentials in terms of computation enhancement brought by the joint UAV and RIS design, we formulate a max–min computation capacity problem via determining the uplink signal detection, active beamforming of UAV, passive beamforming of RIS, time slot partition, computation bits of UAV, and UAV’s trajectory. We develop a concave–convex procedure (CCCP)-based algorithm in an alternating optimization manner over three subproblems to solve the formulated problem. It finds that the CCCP-based algorithm is conducive to decouple the intractable expressions by converting them into new but tractable second-order cone (SOC) constrains. To evaluate the performance of the proposed CCCP-based algorithm, we later design a direct algorithm by exploiting the implicit convexity of the problem. Simulation results demonstrate that the proposed CCCP-based algorithm derives a comparable performance as the direct algorithm, and achieves about 2.57-Mb max-min computation capacity higher compared with the straight flight case, and 8.08-Mb max–min computation capacity higher compared with the case without RIS, which validate the superiority of the joint UAV and RIS design for computation enhancement.

In the Internet-of-Things scenarios, unmanned aerial vehicle (UAV), as a popular aerial platform, is calling for ever-increasing computing support. This letter proposes a novel mobile edge computing (MEC) framework for UAV with the assistance of the reconfigurable intelligence surface (RIS), where a UAV offloads the computation tasks to ground access points (APs) with the assistance of an RIS, during which non-orthogonal multiple access (NOMA) scheme is employed. We maximize the UAV’s computation capacity by jointly optimizing the reflecting phase shift, communication and computation (2C) resource allocation, decoding order, and UAV’s deployment. Specifically, we first derive the reflecting phase shift by invoking the concave-convex procedure (CCCP) method and the semidefinite relaxation technique. Next, we obtain the 2C resource allocation by using the CCCP method. The decoding order and the UAV’s deployment are finally solved via proposing a grid search (GS) method. Numerical results demonstrate that: 1) the computation capacity is greatly improved by the design of RIS; 2) NOMA scheme outperforms orthogonal multiple access scheme; 3) the proposed GS method achieves significant performance gains, as compared with the traditional convex approximation method.