Joint computation and communication design for UAV-assisted mobile edge computing in IoT

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 an optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV 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 propose to solve the problem by two parts, and obtain the near optimal solution by the Lagrangian duality method and successive convex approximation (SCA) technique, respectively. By analysis, the proposed algorithm can be guaranteed to converge 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

UAV-assisted time-efficient data collection via uplink NOMA

Due to the mobility and line-of-sight conditions, unmanned aerial vehicle (UAV) is deemed as a promising solution to sensor data collection. On the other hand, it is vital to guarantee the timeliness of information for UAV-assisted data collection. In this paper, we propose a time-efficient data collection scheme, in which multiple ground devices upload their data to the UAV via uplink non-orthogonal multiple access (NOMA). The total flight time of the UAV is equally divided into N time slots. The duration of each time slot is minimized by jointly optimizing the straight-line trajectory, device scheduling, and transmit power. To solve this mixed integer non-convex optimization problem, we decompose it into two steps. In the first step, we study the device scheduling strategy based on the UAV trajectory and the channel gains between the UAV and ground devices, through which the original problem can be greatly simplified. In the second step, the duration of each time slot is minimized by optimizing the transmit power and the UAV trajectory. An iterative algorithm based on alternating optimization is proposed, where each subproblem can be alternatively solved by applying successive convex approximation with the device scheduling updated at the end of each iteration. Numerical results are presented to evaluate the effectiveness of the proposed scheme

Device association and trajectory planning for UAV-assisted MEC in IoT: a matching theory-based approach

Availability of data and materials: Not applicable.Copyright © The Author(s) 2023. Unmanned aircraft vehicles (UAVs)-enabled mobile edge computing (MEC) can enable Internet of Things devices (IoTD) to offload computing tasks to them. Considering this, we study how multiple aerial service providers (ASPs) compete with each other to provide edge computing services to multiple ground network operators (GNOs). An ASP owning multiple UAVs aims to achieve the maximum profit from providing MEC service to the GNOs, while a GNO operating multiple IoTDs aims to seek the computing service of a certain ASP to meet its performance requirements. To this end, we first quantify the conflicting interests of the ASPs and GNOs by using different profit functions. Then, the UAV scheduling and resource allocation is formulated as a multi-objective optimization problem. To address this problem, we first solve the UAV trajectory planning and resource allocation problem between one ASP and one GNO by using the Lagrange relaxation and successive convex optimization (SCA) methods. Based on the obtained results, the GNOs and ASPs are then associated in the framework based on the matching theory, which results in a weak Pareto optimality. Simulation results show that the proposed method achieves the considerable performance.National Natural Science Foundation of China under Grant 61971421 and Grant 62132004; Quzhou Government under Grant 2021D003; Sichuan Major Research and Development Project under Grant 22QYCX0168