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

Zhang, Jiao; Zhou, Li; Zhou, Fuhui; Seet, Boon‐Chong; Zhang, Haijun; Cai, Zixing; Wei, Jibo

doi:10.1109/tvt.2019.2960103

Cited by 160 publications

(102 citation statements)

References 37 publications

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“…2, where K = 8 UEs are randomly located in a squared area of size 1000 m ×1000 m, and the UAV is located at Fig. 2 The locations of the UEs and the UAV in x-y axes q u =[ 0, 0, 100] [23]. To reduce the decoding complexity and time delay in one NOMA group, the maximum number of the UEs supported by each subchannel is considered as two, i.e., M n = 2, ∀n ∈ N [34].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Finally, the UAV transmits the task results computed by the UAV-MEC sever to all the UEs in the third stage. Besides, each UE can perform its local computing throughout the entire time block duration T. Similar to [19][20][21][22][23], we only consider the first stage due to the strong computational capability of the UAV-MEC sever as well as the small data size of the computed results.…”

Section: Communication Modelmentioning

confidence: 99%

“…For the local computing at the UEs, the total CB L loc k and total energy consumption E loc k of the kth UE during the entire time block T can be, respectively, expressed as [19][20][21][22][23]]…”

Section: Local Computingmentioning

confidence: 99%

“…Against this background, it is critically important to consider green communication so as to improve the efficiency of resource utilization per joule. Particularly, it is suggested that future MEC pursue the maximization of computation efficiency (CE), i.e., the ratio of the total CB to the associated energy consumption [19][20][21][22][23]. In [19], the max-min CE optimization problem was studied in a MEC system powered by wireless energy.…”

Section: Introductionmentioning

confidence: 99%

“…While TDMA is considered in both [19] and [20,21] and [22] extended the CE maximization frameworks to MEC networks under NOMA and OFDMA, respectively. Furthermore, [23] aimed to maximize the CE of a multi-UAV based MEC system.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Computation efficiency optimization in UAV-enabled mobile edge computing system with multi-carrier non-orthogonal multiple access

Cai

et al. 2020

J Wireless Com Network

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In this paper, we study the issue of fair resource optimization for an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system with multi-carrier non-orthogonal multiple access (MC-NOMA). A computation efficiency (CE) optimization problem based on the max-min fairness principle under the partial offloading mode is formulated by optimizing the subchannel assignment, the local CPU frequency, and the transmission power jointly. The formulated problem belongs to the non-convex mixed integer nonlinear programming (MINLP), that is NP-hard to find the global optimal solution. Therefore, we design a polynomial-time algorithm based on the big-M reformulation, the penalized sequential convex programming, and the general Dinkelbach’s method, which can choose an arbitrary point as the initial point and eventually converge to a feasible suboptimal solution. The proposed algorithm framework can be also applied to computation offloading only mode. Additionally, we derive the closed-form optimal solution under the local computing only mode. Simulation results validate the convergence performance of the proposed algorithm. Moreover, the proposed partial offloading mode with the CE maximization scheme outperforms that with the computation bits (CB) maximization scheme with respect to CE, and it can achieve higher CE than the benchmark computing modes. Furthermore, the proposed MC-NOMA scheme can attain better CE performance than the conventional OFDMA scheme.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Communication Modelmentioning

confidence: 99%

Section: Local Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computation efficiency optimization in UAV-enabled mobile edge computing system with multi-carrier non-orthogonal multiple access

Cai

et al. 2020

J Wireless Com Network

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Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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SummaryIn today's era, Internet of Things (IoT) devices generate a vast amount of data, which is typically stored in the cloud environment and can be accessed by edge and IoT devices. The data generated by these devices are offloaded through computation offloading (CO) techniques in an edge/cloud computing environment. This paper conducts a systematic literature review (SLR) to review the state‐of‐the‐art CO techniques in edge computing (EC) in the context of energy, Quality of Service (QoS), authentication and traceability. In this SLR, the evolution of offloading techniques is analyzed in detail. A total of 138 articles, spanning from 2016 to 2023 (till date), have been classified into QoS, energy, and authentication and traceability‐based CO techniques. The optimization‐based techniques are the most preferred choices to improve the QoS and reduce energy in the research field of CO in EC. In addition, this paper explores the significant issues and challenges that require further investigation. For future research, energy, QoS, and data provenance for dependent or dynamic task offloading in mobility scenarios can be explored further.

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Computation‐efficiency‐oriented resource optimization in NOMA‐assisted multi‐UAV‐enabled MEC networks

Liu,

Tian,

Zhou

et al. 2024

Int J Communication

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SummaryUnmanned aerial vehicles (UAVs) can serve as aerial mobile edge computing (MEC) servers to provide computing services to Internet of Things smart devices (ISDs) with insufficient computing capacity on the ground. However, how to provide energy‐efficient computing services by designing appropriate resource optimization strategy is still a challenge issue in UAV‐enabled MEC networks. To this end, this paper proposes a computation efficiency (CE)‐oriented partial offloading framework for UAV‐enabled MEC networks, where the ISDs' computation bits and energy consumption are taken into account simultaneously. Specifically, we firstly divide the ISDs into multiple clusters and determine the deployment of UAVs by k‐means method. Meanwhile, ISDs occupying the same subchannel in the same cluster can offload data through non‐orthogonal multiple access (NOMA) technology. Then, the problem of maximizing the CE of the system is formulated by optimizing subchannel allocation, transmit power and computation resources of ISDs. To solve it, we propose a staged optimization approach by using matching theory, Dinkelbach and sequential convex programming (SCP) methods. Numerical results demonstrate the proposed scheme can achieve higher CE compared with other baseline schemes.

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

Cited by 160 publications

References 37 publications

Computation efficiency optimization in UAV-enabled mobile edge computing system with multi-carrier non-orthogonal multiple access

Computation efficiency optimization in UAV-enabled mobile edge computing system with multi-carrier non-orthogonal multiple access

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Computation‐efficiency‐oriented resource optimization in NOMA‐assisted multi‐UAV‐enabled MEC networks

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