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Heterogeneous UAVs assisted mobile edge computing for energy consumption minimization of the edge side.

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Title: Heterogeneous UAVs assisted mobile edge computing for energy consumption minimization of the edge side.
Authors: Tang, Qiang1 (AUTHOR) tangqiang@csust.edu.cn, Li, Linjiang1 (AUTHOR) jiangzero@stu.csust.edu.cn, Jin, Caiyan1 (AUTHOR) caiyan_jin@stu.csust.edu.cn, Liu, Lixin1 (AUTHOR) 20208051436@stu.csust.edu.cn, Wang, Jin1 (AUTHOR) jinwang@csust.edu.cn, Liao, Zhuofan1 (AUTHOR), Luo, Yuansheng1 (AUTHOR)
Source: Computer Communications. Oct2022, Vol. 194, p268-279. 12p.
Subjects: Mobile computing, Edge computing, Energy consumption, Heuristic algorithms, Heterogeneous computing, Integer programming
Abstract: The research on the Internet of Things (IoT) and edge computing, especially Unmanned Aerial Vehicles assisted Mobile Edge Computing (UAV-assisted MEC) attracts more and more interests of researchers. Nowadays, MEC systems with multiple UAVs have great research value, especially for emergency communication scenarios. In this paper, a heterogeneous UAVs assisted MEC system was proposed, and with the goal of minimization the edge side energy consumption, a parallel processing was involved to jointly optimize the communication scheduling, forwarding power, offloading data size, computing frequency and the trajectory of the UAV. The problem is formulated as a Mixed Integer Non-Linear Programming (MINLP), hard to solve. Therefore, we divided the MINLP into two sub-problems by applying the Block Coordinate Descent (BCD) method, and solved it using the Lagrangian Duality (LD) method and Successive Convex Optimization (SCO). The UAV's trajectory was initialized as a circular, and then it was optimized by using the standardized convex solver design algorithm. In addition, a heuristic algorithm was put forward to obtain the optimization solution, while we set other benchmarks and the Monte Carlo (MC) algorithm to justify its validity and rationality. The simulation results showed that our strategy has better performance at minimizing energy consumption compared with other algorithms. [ABSTRACT FROM AUTHOR]
Copyright of Computer Communications is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
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Header DbId: egs
DbLabel: Engineering Source
An: 159141853
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
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  Data: Heterogeneous UAVs assisted mobile edge computing for energy consumption minimization of the edge side.
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  Data: <searchLink fieldCode="AR" term="%22Tang%2C+Qiang%22">Tang, Qiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tangqiang@csust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Linjiang%22">Li, Linjiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jiangzero@stu.csust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Jin%2C+Caiyan%22">Jin, Caiyan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> caiyan_jin@stu.csust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Lixin%22">Liu, Lixin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 20208051436@stu.csust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jin%22">Wang, Jin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jinwang@csust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liao%2C+Zhuofan%22">Liao, Zhuofan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Yuansheng%22">Luo, Yuansheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Computer+Communications%22">Computer Communications</searchLink>. Oct2022, Vol. 194, p268-279. 12p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Mobile+computing%22">Mobile computing</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic+algorithms%22">Heuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Heterogeneous+computing%22">Heterogeneous computing</searchLink><br /><searchLink fieldCode="DE" term="%22Integer+programming%22">Integer programming</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The research on the Internet of Things (IoT) and edge computing, especially Unmanned Aerial Vehicles assisted Mobile Edge Computing (UAV-assisted MEC) attracts more and more interests of researchers. Nowadays, MEC systems with multiple UAVs have great research value, especially for emergency communication scenarios. In this paper, a heterogeneous UAVs assisted MEC system was proposed, and with the goal of minimization the edge side energy consumption, a parallel processing was involved to jointly optimize the communication scheduling, forwarding power, offloading data size, computing frequency and the trajectory of the UAV. The problem is formulated as a Mixed Integer Non-Linear Programming (MINLP), hard to solve. Therefore, we divided the MINLP into two sub-problems by applying the Block Coordinate Descent (BCD) method, and solved it using the Lagrangian Duality (LD) method and Successive Convex Optimization (SCO). The UAV's trajectory was initialized as a circular, and then it was optimized by using the standardized convex solver design algorithm. In addition, a heuristic algorithm was put forward to obtain the optimization solution, while we set other benchmarks and the Monte Carlo (MC) algorithm to justify its validity and rationality. The simulation results showed that our strategy has better performance at minimizing energy consumption compared with other algorithms. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computer Communications is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.comcom.2022.07.023
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 12
        StartPage: 268
    Subjects:
      – SubjectFull: Mobile computing
        Type: general
      – SubjectFull: Edge computing
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Heuristic algorithms
        Type: general
      – SubjectFull: Heterogeneous computing
        Type: general
      – SubjectFull: Integer programming
        Type: general
    Titles:
      – TitleFull: Heterogeneous UAVs assisted mobile edge computing for energy consumption minimization of the edge side.
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            NameFull: Tang, Qiang
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            NameFull: Li, Linjiang
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            NameFull: Jin, Caiyan
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            NameFull: Liu, Lixin
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            NameFull: Wang, Jin
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            NameFull: Liao, Zhuofan
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            NameFull: Luo, Yuansheng
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          Dates:
            – D: 01
              M: 10
              Text: Oct2022
              Type: published
              Y: 2022
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              Value: 01403664
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              Value: 194
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            – TitleFull: Computer Communications
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