Task and Bandwidth Allocation for UAV-Assisted Mobile Edge Computing with Trajectory Design

Hu, Xiaoyan; Wong, Kai-Kit; Yang, Kun; Zheng, Zhongbin

doi:10.1109/globecom38437.2019.9014282

Cited by 12 publications

(4 citation statements)

References 23 publications

(41 reference statements)

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“…The EC of the UAVs determines the period of service in the system. Hu et al [16] maximized UAV energy efficiency by optimizing UAV flight trajectories and offload/cache decisions. Offloading tasks to other UAVs using collaboration between UAVs can reduce the EC of individual UAVs [17,18].…”

Section: Related Workmentioning

confidence: 99%

Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing

Luo

Qiao

et al. 2023

Drones

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With the development of the Internet of Things (IoT), IoT devices are increasingly being deployed in scenarios with large footprints, remote locations, and complex geographic environments. In these scenarios, base stations are usually not easily deployed and are easily destroyed, so unmanned aerial vehicle (UAV)-based edge computing is a good solution. However, the UAV cannot accomplish the computing tasks and efficiently achieve better resource allocation considering the limited communication and computing resources of the UAV. In this paper, a multi-UAV-assisted mobile edge computing (MEC) system is considered where multiple UAVs cooperate to provide a service to IoT devices. We formulate an optimization function to minimize the energy consumption of a multi-UAV-assisted MEC system. The optimization function is a complex problem with non-convex and multivariate coupling. Thus, a joint UAV deployment and task scheduling optimization algorithm are designed to achieve optimal values of UAV numbers, the hovering position of each UAV, and the best strategy for offloading and resource allocation. Experimental results demonstrate that the algorithm has positive convergence performance and can accomplish more tasks under the constraint of delay compared to the two benchmark algorithms. The proposed algorithm can effectively reduce the system energy consumption compared to the two state-of-the-art algorithms.

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Section: Related Workmentioning

confidence: 99%

Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing

Luo

Qiao

et al. 2023

Drones

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“…Zhou et al [22] develop an edge computing system that enables wireless power transfer to UAVs and address the causality between computing and energy transfer under power minimization constraints. Hu et al [23] utilize UAVs as computing servers to provide services to ground terminal devices and act as relays to offload tasks from terminal devices to access node computing. By employing an alternating optimization algorithm, they achieve the minimization of the total energy consumption of devices and UAVs.…”

Section: State Of Artmentioning

confidence: 99%

PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks

Liu,

Zhang,

2023

Applied Sciences

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In next-generation mobile communication scenarios, more and more user terminals (UEs) and edge computing servers (ECSs) are connected to the network. To ensure the experience of edge computing services, we designed an unmanned aerial vehicle (UAV)-assisted edge computing network application scenario. In the considered scenario, the UAV acts as a relay node to forward edge computing tasks when the performance of the wireless channel between UEs and ECSs degrades. In order to minimize the average delay of edge computing tasks, we design the optimization problem of joint UE–ECS matching and UAV three-dimensional hovering position deployment. Further, we transform this mixed integer nonlinear programming into a continuous-variable decision process and design the corresponding Proximal Policy Optimization (PPO)-based joint optimization algorithm. Sufficient data pertaining to latency demonstrate that the suggested algorithm can obtain a seamless reward value when the number of training steps hits three million. This verifies the algorithm’s desirable convergence property. Furthermore, the algorithm’s efficacy has been confirmed through simulation in various environments. The experimental findings ascertain that the PPO-based co-optimization algorithm consistently attains a lower average latency rate and a minimum of 8% reduction in comparison to the baseline scenarios.

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“…• Task offloading and resource allocation in UAV networks: Several studies have focused on the task offloading and resource allocation problems in UAV networks, which can be roughly divided into two types based on the role that the UAV plays: 1) UAV serves as resource owner [13]- [16], and 2) UAV acts as resource requestor [1], [17]- [19]. In terms of UAVs possessing rather powerful capabilities, [13]- [16] studied the task offloading problem under a UAV-assisted edge computing network architecture where UAVs acted as computing servers that assisted on-ground mobile devices with computation-intensive tasks. With respect to UAVs as computing service requestors, Bai et al [1] devised an energy-efficient computation offloading technique for UAV-MEC systems with an emphasis on physical-layer security via convex optimization techniques.…”

Section: A Related Workmentioning

confidence: 99%

Let' s Trade in The Future! A Futures-Enabled Fast Resource Trading Mechanism in Edge Computing-Assisted UAV Networks

Liwang

Gao

Wang

2021

Preprint

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Mobile edge computing (MEC) has emerged as one of the key technical aspects of the fifthgeneration (5G) networks. The integration of MEC with resource-constrained unmanned aerial vehicles (UAVs) could enable flexible resource provisioning for supporting dynamic and computation-intensive UAV applications. Existing resource trading could facilitate this paradigm with proper incentives, which, however, may often incur unexpected negotiation latency and energy consumption, trading failures and unfair pricing, due to the unpredictable nature of the resource trading process. Motivated by these challenges, an efficient futures-based resource trading mechanism for edge computing-assisted UAV network is proposed, where a mutually beneficial and risk-tolerable forward contract is devised to promote resource trading between an MEC server (seller) and a UAV (buyer). Two key problems i.e. futures contract design before trading and power optimization during trading are studied. By analyzing historical statistics associated with future resource supply, demand, and air-to-ground communication quality, the contract design is formulated as a multi-objective optimization problem aiming to maximize both the seller's and the buyer's expected utilities, while estimating their acceptable risk tolerance.Accordingly, we propose an efficient bilateral negotiation scheme to help players reach a trading consensus on the amount of resources and the relevant price. For the power optimization problem, we develop a practical algorithm that enables the buyer to determine its optimal transmission power via convex optimization techniques. Comprehensive simulations demonstrate that the proposed mechanism offers both players considerable utilities, while outperforming the onsite trading mechanism on trading failures and fairness, negotiation latency, and cost.

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Task and Bandwidth Allocation for UAV-Assisted Mobile Edge Computing with Trajectory Design

Cited by 12 publications

References 23 publications

Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing

Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing

PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks

Let' s Trade in The Future! A Futures-Enabled Fast Resource Trading Mechanism in Edge Computing-Assisted UAV Networks

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