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作者:

Yuan, Haitao (Yuan, Haitao.) | Wang, Meijia (Wang, Meijia.) | Bi, Jing (Bi, Jing.) | Shi, Shuyuan (Shi, Shuyuan.) | Yang, Jinhong (Yang, Jinhong.) | Zhang, Jia (Zhang, Jia.) | Zhou, MengChu (Zhou, MengChu.) | Buyya, Rajkumar (Buyya, Rajkumar.)

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EI Scopus SCIE

摘要:

Mobile edge computing (MEC) paradigm supports cloud-like computing capabilities at the edge of the network and offers low-latency services. Proxy servers of MEC with mobility and limited computing, e.g., flying unmanned aerial vehicles (UAVs) have emerged as competitors in providing services. This work considers a task offloading problem for an UAV-assisted MEC system and designs an integrated cloud-edge network with multiple mobile users (MUs) and layered UAVs to improve MEC with a network of UAVs. In our system, edge UAVs (EUAVs) and the cloud collaborate to provide caching and computing services for MUs. We consider static and dynamic applications that support task offloading. Our proposed approach minimizes the weighted cost of latency and energy consumption by jointly optimizing caching and offloading, deployment of EUAVs, and allocation of computation resources. Simultaneously, this work also considers UAVs' caching and computation capacities while meeting MUs' latency and energy constraints. Thus, a constrained mixed integer nonlinear program for a layered UAV-assisted hybrid cloud-edge system is formulated. To solve it, this work designs a hybrid metaheuristic algorithm named adaptive and genetic simulated annealing (SA)-based particle swarm optimization (AGSP). Experimental results with a real-life dataset verify that the AGSP's system energy consumption and task latency are reduced by at least 7.4% and 8.46%, respectively, compared with the state-of-the-art algorithms, thus proving that AGSP greatly enhances the energy and latency of the system.

关键词:

Autonomous aerial vehicles Servers mobile edge computing (MEC) Cloud computing Computation offloading Task analysis wireless caching Computer architecture Relays Trajectory unmanned aerial vehicles (UAVs) particle swarm optimization (PSO)

作者机构:

  • [ 1 ] [Yuan, Haitao]Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
  • [ 2 ] [Wang, Meijia]Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
  • [ 3 ] [Bi, Jing]Beijing Univ Technol, Fac Informat Technol, Sch Software Engn, Beijing 100124, Peoples R China
  • [ 4 ] [Shi, Shuyuan]Beihang Univ, MIIT Key Lab Spintron, Fert Beijing Inst, Beijing 100191, Peoples R China
  • [ 5 ] [Shi, Shuyuan]Beihang Univ, Sch Integrated Circuit Sci & Engn, Beijing 100191, Peoples R China
  • [ 6 ] [Yang, Jinhong]CSSC Syst Engn Res Inst, Beijing 100036, Peoples R China
  • [ 7 ] [Zhang, Jia]Southern Methodist Univ, Dept Comp Sci, Dallas, TX 75206 USA
  • [ 8 ] [Zhou, MengChu]New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA
  • [ 9 ] [Buyya, Rajkumar]Univ Melbourne, Sch Comp & Informat Syst, Cloud Comp & Distributed Syst CLOUDS Lab, Melbourne, VIC 3010, Australia

通讯作者信息:

  • [Yuan, Haitao]Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China;;

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来源 :

IEEE INTERNET OF THINGS JOURNAL

ISSN: 2327-4662

年份: 2024

期: 19

卷: 11

页码: 30496-30509

1 0 . 6 0 0

JCR@2022

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SCOPUS被引频次: 13

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