Stochastic Computation Offloading and Trajectory Scheduling for UAV-Assisted Mobile Edge Computing

Zhang, Jiao; Zhou, Li; Tang, Qi; Ngai, Edith C.-H.; Hu, Xiping; Zhao, Haitao; Wei, Jibo

doi:10.1109/jiot.2018.2890133

Cited by 211 publications

(70 citation statements)

References 30 publications

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“…It is pointed out that it is critical to apply a precise energy consumption model while exploring the energy consumption of the computation system. In [99], a similar scenario is considered, but it simulates that offloading tasks arrive at UAV stochastically. In each mobile user, there is a local queue to store tasks to send.…”

Section: ) Offloading To Uavmentioning

confidence: 99%

Air-Ground Integrated Mobile Edge Networks: A Survey

Zhang

et al. 2020

IEEE Access

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With proliferation of smart devices and wireless applications, the recent few years have witnessed data surge. These massive data needs to be stored, transmitted, and processed in time to exploit their value for decision making. Conventional cloud computing requires transmission of massive amount of data in and out of core network, which can lead to longer service latency and potential traffic congestion. As a new platform, mobile edge computing (MEC) moves computation and storage resources to edge network in proximity to the data source. With MEC, data can be processed locally, and thus mitigate issues of latency and congestion. However, it is very challenging to reap the benefits of MEC everywhere due to geographic constraints, expensive deployment cost, and immoveable base stations. Because of easy deployment and high mobility of unmanned aerial vehicles (UAVs), air-ground integrated mobile edge networks (AGMEN) is proposed, where UAVs are employed to assist the MEC network. Such an AGMEN expects to provide MEC services ubiquitously and reliably. In this article, we first introduce the characteristics and components of UAV. Then, we will review the applications, key challenges, and current research technologies of AGMEN, from perspectives of communication, computation, and caching, respectively. Finally, we will discuss some essential research directions for AGMEN.

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Section: ) Offloading To Uavmentioning

confidence: 99%

Air-Ground Integrated Mobile Edge Networks: A Survey

Zhang

et al. 2020

IEEE Access

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“…Most studies assume that the UAV plays the role of an intermediate node that connects the user equipment (UE) to the BS. To ensure that the UAVs perform their required functions smoothly, several studies have attempted to increase the stability of the network in terms of energy consumption, packet delay, and queuing [15][16][17][18][19][20][21].…”

Section: Uav Network Optimizationmentioning

confidence: 99%

Combined time bound optimization of control, communication, and data processing for FSO‐based 6G UAV aerial networks

Seo

Chung

2020

ETRI Journal

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Because of the rapid increase of mobile traffic, flexible broadband supportive unmanned aerial vehicle (UAV)‐based 6G mobile networks using free space optical (FSO) links have been recently proposed. Considering the advancements made in UAVs, big data processing, and artificial intelligence precision control technologies, the formation of an additional wireless network based on UAV aerial platforms to assist the existing fixed base stations of the mobile radio access network is considered a highly viable option in the near future. In this paper, a combined time bound optimization scheme is proposed that can adaptively satisfy the control and communication time constraints as well as the processing time constraints in FSO‐based 6G UAV aerial networks. The proposed scheme controls the relation between the number of data flows, input data rate, number of worker nodes considering the time bounds, and the errors that occur during communication and data processing. The simulation results show that the proposed scheme is very effective in satisfying the time constraints for UAV control and radio access network services, even when errors in communication and data processing may occur.

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“…Moreover, some works studied the optimization of the EC of UAVs or the weighted EC sum of the UEs and the UAV. In [24], Zhang et al considered the task queues of the UEs and the UAV, and minimized the weighted EC sum of the UEs and UAV by optimizing resources allocations and UAV's trajectory. In [25], Hu et al studied the UAV-assisted relaying and edge computing system and minimized weighted EC sum of UEs and UAV by optimizing the computing rates, bandwidth allocation and UAV's trajectory.…”

Section: Introductionmentioning

confidence: 99%

Fairness-Aware Offloading and Trajectory Optimization for Multi-UAV Enabled Multi-Access Edge Computing

et al. 2020

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Multiple unmanned aerial vehicles (UAVs) can compensate for the performance deficiencies of a single UAV in multi-access edge computing (MEC) systems, thus providing improved offloading services to user equipments (UEs). In multi-UAV enabled MEC systems, the offloading strategy and UAVs' trajectories affect the fairness of both UEs and UAVs, which affects the UE experience and UAVs' existence durations. Therefore, we investigate fairness-aware offloading and trajectory optimization in the system. To ensure fairness of energy consumptions (ECs) for both UEs and UAVs, we minimize the weighted sum of the maximum EC among UEs and the maximum EC among UAVs subject to the task delay, the offloading strategy and UAVs' trajectories constraints. Despite the non-convexity of the original formulated joint optimization problem, we transform the problem into two sub-problems and solve them one by one. Finally, an iterative optimization algorithm is proposed to alternately optimize the offloading strategies and the UAVs' trajectories. Simulation results show that the proposed algorithm can effectively reduce both the maximum EC among UEs and the maximum EC among UAVs and ensure the fairness of both the UEs and UAVs. INDEX TERMS Fairness, multi-access edge computing, multiple UAVs, trajectory optimization, offloading optimization.

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Stochastic Computation Offloading and Trajectory Scheduling for UAV-Assisted Mobile Edge Computing

Cited by 211 publications

References 30 publications

Air-Ground Integrated Mobile Edge Networks: A Survey

Air-Ground Integrated Mobile Edge Networks: A Survey

Combined time bound optimization of control, communication, and data processing for FSO‐based 6G UAV aerial networks

Fairness-Aware Offloading and Trajectory Optimization for Multi-UAV Enabled Multi-Access Edge Computing

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