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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. [ABSTRACT FROM AUTHOR]

In this letter, we investigate an unmanned aerial vehicle (UAV) enabled wireless powered communication network (WPCN), in which the UAV first employs the radio frequency (RF) signal to power ground users, and then the users communicate with the UAV by using the harvested energy. The sum rate maximization problem in WPCN suffers doubly near-far effect caused by the double signal propagation loss in both the energy harvesting and communication stage. It is generally believed that this effect can be alleviated by integrating UAV into WPCN, thanks to its flexible deployment. However, due to the lack of analytical solution in state-of-art research, it is still unclear how to quantify the advantage that the mobility of UAV brings to migrating the doubly near-far effect. To address this issue, by showing that the optimal UAV trajectory has a multi-location hovering structure, we first derive the closed-form optimal solution of resource allocation, as a function of the UAV hovering location for downlink wireless power transfer (WPT). We then prove that the UAV in our case only needs to hover at a single location for WPT. More importantly, it is explicitly shown that the doubly near far effect can be alleviated into near-far effect. Simulation results validate our analysis. [ABSTRACT FROM AUTHOR]

This letter considers a solar-powered unmanned aerial vehicle (UAV) system, where the UAV collects data from Internet of Things Devices (IoTDs) on the ground while also charging them using laser charging technology. Both the linear and nonlinear laser charging models are considered. We aim to maximize the residual energy of the UAV while satisfying the requirements of IoTDs by jointly optimizing the three-dimensional (3D) trajectory of UAV and scheduling for IoTDs. The optimization problem is formulated as a mixed integer non-convex problem which is difficult to solve in general. We apply the successive convex approximation and block coordinate descent algorithm to address this problem successfully. Simulation results verify the effectiveness of our algorithm compared with two benchmark methods. [ABSTRACT FROM AUTHOR]

Due to the small size, low cost, and autonomous nature, unmanned aerial vehicles (UAVs) have attracted growing attention in improving traditional networks’ performance. This paper considers a multi-UAV enabled large-scale wireless recharge networks, where a group of UAVs is dispatched as mobile wireless power transfer, and information collection systems to serve a set of ground low-power sensor nodes. Specifically, the UAVs employ radio frequency (RF) wireless power transfer (WPT) to supply energy towards the sensor nodes, and the communication services are available via wireless information transfer (WIT) systems. To achieve balanced energy consumption among UAVs, we maximize the energy utilization efficiency of UAVs, and minimize the communication delay by optimizing the trajectory jointly with constraints of the energy capacity, and the area of the target region. The formulated problem is a mixed-integer programming problem that is a variation of multiple traveling salesman problem. Thus we present a heuristic algorithm that combines the evolutionary algorithm, and variable neighborhood search to achieve the optimal visited sequence of the sensor nodes. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithm. It draws new insights on the estimation of the feasibility of the given UAVs whose energy capacity is limited. [ABSTRACT FROM AUTHOR]