Unmanned aerial vehicle (UAV)-enabled mobile edge computing has been recognized as a promising technology to flexibly and efficiently handle computation-intensive and latency-sensitive tasks in the era of fifth generation (5G) and beyond. In this paper, we study the problem of Service Provisioning for UAV-enabled mobile edge computiNg (SPUN). Specifically, under task latency requirements and various resource constraints, we jointly optimize the service placement, UAV movement trajectory, task scheduling, and computation resource allocation, to minimize the overall energy consumption of all terrestrial user equipments (UEs). Due to the non-convexity of the SPUN problem as well as complex coupling among mixed integer variables, it is a non-convex mixed integer nonlinear programming (MINLP) problem. To solve this challenging problem, we propose two alternating optimization-based suboptimal solutions with different time complexities. In the first solution with relatively high complexity in the worst case, the joint service placement and task scheduling subproblem, and UAV trajectory subproblem are iteratively solved by the Branch and Bound (BnB) method and successive convex approximation (SCA), respectively, while the optimal solution to the computation resource allocation subproblem is efficiently obtained in the closed form. To avoid the high complexity caused by BnB, in the second solution, we propose a novel approximation algorithm based on relaxation and randomized rounding techniques for the joint service placement and task scheduling subproblem, while the other two subproblems are solved in the same way as that of the first solution. Extensive simulations demonstrate that the proposed solutions achieve significantly lower energy consumption of UEs compared to three benchmarks.

Recently, federated learning (FL) has become a promising distributed learning paradigm that caters to the recent trend of pushing intelligence from the cloud to the edge. Nevertheless, communication bottlenecks and device dropout can lead to inefficient FL in the large network scale, where massive devices cannot be accessed with severely limited network resources. Inspired by the Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing (MEC), we propose the multi-UAV assisted FL design to provide the intermediate model aggregation in the sky. Specifically, we study the problem of joint UAV dePloyment and edge aSsociation (UPS) to minimize the overall energy consumption, which concerns UAV deployment, edge association, and resource allocation. Unfortunately, solving this problem is non-trivial, due to its infinite search space and the complex coupling among mixed optimization variables. To tackle this difficulty, we exploit the FL bundle generation method to reduce candidate locations of UAVs from infinite to finite. Then, we decompose the initial problem and devise an alternating optimization-based algorithm to achieve the optimal resource allocation in the closed form. On this basis, we design a greedy-based approximation algorithm with logN performance guarantee for UAV deployment and edge association. Extensive simulations are conducted to validate the effectiveness of our proposed solution. Compared with five benchmarks, our proposed algorithm can significantly reduce the overall training energy consumption under the training time constraint, while always maintaining better training performance under different parameter settings.

Unmanned aerial vehicles (UAVs) have been widely used to perform search and tracking tasks in military and civil fields. To perform these tasks autonomously, a swarm of multiple UAVs need to be endowed with intelligence through machine learning (ML). However, the traditional centralized ML cannot be directly applied in UAV networks, since it is challenging to transmit raw data with limited bandwidth and energy budget. As a distributed manner, federated learning (FL) is more suitable for UAV networks than traditional ML schemes in order to boost edge intelligence for UAVs. Considering the limited energy supply of UAVs, we study how to minimize UAVs’ overall training energy consumption by jointly optimizing the local convergence threshold, local iterations, computation resource allocation, and bandwidth allocation, subject to the FL global accuracy guarantee and maximum training latency constraint. The formulated nonconvex mixed-integer programming problem is solved by a joint training and resource allocation optimization algorithm. In addition, we also study how to solve the problem considering fairness among different UAVs by changing the objective to minimizing the maximum energy consumption of UAVs, and extend the aforementioned approach to this problem. Our simulation results show that while satisfying both the training accuracy and latency constraints, the proposed algorithm can reduce more UAVs’ overall training energy consumption and the maximum energy consumption in the UAV swarm than four baseline schemes.

As a way of providing users flexible computing services, networks exist that can make full use of air and ground computing resources. Such networks are called air-ground collaborative mobile edge computing (AGC-MEC) networks. AGC-MEC supports numerous emerging real-time applications for which timely computed results are critical. Researchers have developed a novel metric “age of information (AoI)” that can capture the freshness of computed results. This is the first paper to study the problem of AoI-aware scheduling for Air-ground Collaborative mobile Edge computing ( i.e ., IACE). So as to minimize the weighted AoI of all the terrestrial user equipments (UEs), we have jointly optimized task scheduling, computing resource allocation, and unmanned aerial vehicle (UAV) trajectory taking into account the constraints on the computing resources and the available energy of the UAV. The formulated problem, which is a challenge to solve, is a mixed-integer nonlinear programming (MINLP) problem. To obtain an effective solution, we propose an iterative algorithm based on the alternating optimization approach, which entails dividing the considered problem into three subproblems. Extensive simulations show that the proposed algorithm can achieve lower weighted AoI than five benchmark algorithms, while satisfying the resource constraints. Furthermore, simulation results demonstrate two interesting insights. First, the introduction of an aerial MEC server facilitates a flexible offloading design of the UEs which is critical to guaranteeing the freshness of computed results. Second, by optimizing the scheduling, the proposed design can unlock performance gains, especially in the resource-limited regime.

In this article, we study the problem of task selection and scheduling in unmanned aerial vehicle (UAV)-enabled multiaccess edge computing for reconnaissance (ASSUMER). Specifically, taking into account the time-varying priorities of reconnaissance tasks, we investigate how to maximize the overall reconnaissance utility by selecting an appropriate set of tasks and scheduling their execution sequence in the multiaccess edge computing server of the UAV. The ASSUMER problem is a mixed-integer nonlinear programming (MINLP) problem, which includes both integer and continuous variables and is proved to be NP-hard. To address this challenging problem, we first model the task scheduling subproblem as a single machine scheduling problem with the deterioration effect. We find out that the optimal task scheduling can be solved efficiently given any task selection variables and propose an optimal scheduling algorithm. Second, using the proposed scheduling algorithm, the ASSUMER problem is equivalent to a binary integer programming problem with respect to the task selection variable only. We prove that the objective function falls into the category of the submodular function and transform the original problem into the problem of maximizing submodular function with the energy constraint. Third, combining the proposed scheduling algorithm with submodularity, we design an effective approximation algorithm for the ASSUMER problem and prove that the algorithm has $(1 - {e^{ - 1}})/2$ bicriterion approximation guarantee. Finally, simulation results show that the proposed algorithm can improve the overall reconnaissance utility and energy efficiency compared to five benchmark algorithms.