Computation Offloading and Resource Allocation for Vehicle-Assisted Edge Computing Networks With Joint Access and Backhaul

The edge computing utilizes vehicles as resources to assist in computational offloading can shorten the distance between users and computing servers, thereby improving the reliability of communication between them. In this paper, we investigate a Vehicle-assisted Edge Computing (VEC) model by jointl...

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出版年:IEEE Access
主要な著者: Bo Huang, Yan Zhou, Xu Zhang, Jie Chen, Li Shang
フォーマット: 論文
言語:英語
出版事項: IEEE 2024-01-01
主題:
オンライン・アクセス:https://ieeexplore.ieee.org/document/10630514/
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author Bo Huang
Yan Zhou
Xu Zhang
Jie Chen
Li Shang
author_facet Bo Huang
Yan Zhou
Xu Zhang
Jie Chen
Li Shang
author_sort Bo Huang
collection DOAJ
container_title IEEE Access
description The edge computing utilizes vehicles as resources to assist in computational offloading can shorten the distance between users and computing servers, thereby improving the reliability of communication between them. In this paper, we investigate a Vehicle-assisted Edge Computing (VEC) model by jointly considering wireless access and backhaul links, and formulate an optimization problem that combines computational offloading and resource allocation, aiming at minimizing system delay. Further, the formulated problem is decomposed into two subproblems, e.g., computation offloading and resource allocation. In particular, we propose a new computational offloading approach that models the offloading decision for joint wireless access and backhaul as a potential game. The Nash equilibrium is guaranteed by the rational design of potential function, and the corresponding solution is solved by a backward induction method. On the other hand, the resource allocation subproblem is transformed from a nonconvex to a convex optimization problem based on equivalent transformation with successive convex approximation methods and finally derives the optimal solution satisfying Karush-Kuhn-Tucker (KKT) conditions. Simulation results show that the proposed algorithm have near-optimal performance and is superior to the state-of-the-art over a wide range of parameter settings.
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spelling doaj-art-d1306ec4d5fc41e98f1946edbc7be2ff2025-08-20T00:51:14ZengIEEEIEEE Access2169-35362024-01-011211024811025910.1109/ACCESS.2024.344000010630514Computation Offloading and Resource Allocation for Vehicle-Assisted Edge Computing Networks With Joint Access and BackhaulBo Huang0https://orcid.org/0009-0007-3870-3368Yan Zhou1Xu Zhang2https://orcid.org/0009-0004-5500-134XJie Chen3https://orcid.org/0009-0004-7496-5558Li Shang4School of Electronic Information Engineering, Suzhou Vocational University, Suzhou, ChinaSchool of Electronic Information Engineering, Suzhou Vocational University, Suzhou, ChinaSchool of Electronic Information Engineering, Suzhou Vocational University, Suzhou, ChinaSchool of Electronic Information Engineering, Suzhou Vocational University, Suzhou, ChinaSchool of Electronic Information Engineering, Suzhou Vocational University, Suzhou, ChinaThe edge computing utilizes vehicles as resources to assist in computational offloading can shorten the distance between users and computing servers, thereby improving the reliability of communication between them. In this paper, we investigate a Vehicle-assisted Edge Computing (VEC) model by jointly considering wireless access and backhaul links, and formulate an optimization problem that combines computational offloading and resource allocation, aiming at minimizing system delay. Further, the formulated problem is decomposed into two subproblems, e.g., computation offloading and resource allocation. In particular, we propose a new computational offloading approach that models the offloading decision for joint wireless access and backhaul as a potential game. The Nash equilibrium is guaranteed by the rational design of potential function, and the corresponding solution is solved by a backward induction method. On the other hand, the resource allocation subproblem is transformed from a nonconvex to a convex optimization problem based on equivalent transformation with successive convex approximation methods and finally derives the optimal solution satisfying Karush-Kuhn-Tucker (KKT) conditions. Simulation results show that the proposed algorithm have near-optimal performance and is superior to the state-of-the-art over a wide range of parameter settings.https://ieeexplore.ieee.org/document/10630514/Computation offloadingaccess/backhaul linkresource allocationedge computing
spellingShingle Bo Huang
Yan Zhou
Xu Zhang
Jie Chen
Li Shang
Computation Offloading and Resource Allocation for Vehicle-Assisted Edge Computing Networks With Joint Access and Backhaul
Computation offloading
access/backhaul link
resource allocation
edge computing
title Computation Offloading and Resource Allocation for Vehicle-Assisted Edge Computing Networks With Joint Access and Backhaul
title_full Computation Offloading and Resource Allocation for Vehicle-Assisted Edge Computing Networks With Joint Access and Backhaul
title_fullStr Computation Offloading and Resource Allocation for Vehicle-Assisted Edge Computing Networks With Joint Access and Backhaul
title_full_unstemmed Computation Offloading and Resource Allocation for Vehicle-Assisted Edge Computing Networks With Joint Access and Backhaul
title_short Computation Offloading and Resource Allocation for Vehicle-Assisted Edge Computing Networks With Joint Access and Backhaul
title_sort computation offloading and resource allocation for vehicle assisted edge computing networks with joint access and backhaul
topic Computation offloading
access/backhaul link
resource allocation
edge computing
url https://ieeexplore.ieee.org/document/10630514/
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AT xuzhang computationoffloadingandresourceallocationforvehicleassistededgecomputingnetworkswithjointaccessandbackhaul
AT jiechen computationoffloadingandresourceallocationforvehicleassistededgecomputingnetworkswithjointaccessandbackhaul
AT lishang computationoffloadingandresourceallocationforvehicleassistededgecomputingnetworkswithjointaccessandbackhaul