Joint Communication, Computation, and Control for Computational Task Offloading in Vehicle-Assisted Multi-Access Edge Computing
Future generation of Electric Vehicles (EVs) equipped with modern technologies will impose a significant burden on computation and communication to the network due to the vast extension of onboard infotainment services. To overcome this challenge, multi-access edge computing (MEC) or Fog Computing c...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9940624/ |
| _version_ | 1811322061135544320 |
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| author | Tri Nguyen Dang Aunas Manzoor Yan Kyaw Tun S. M. Ahsan Kazmi Rim Haw Sang Hoon Hong Zhu Han Choong Seon Hong |
| author_facet | Tri Nguyen Dang Aunas Manzoor Yan Kyaw Tun S. M. Ahsan Kazmi Rim Haw Sang Hoon Hong Zhu Han Choong Seon Hong |
| author_sort | Tri Nguyen Dang |
| collection | DOAJ |
| description | Future generation of Electric Vehicles (EVs) equipped with modern technologies will impose a significant burden on computation and communication to the network due to the vast extension of onboard infotainment services. To overcome this challenge, multi-access edge computing (MEC) or Fog Computing can be employed. However, the massive adoption of novel infotainment services such as Augmented Reality, Virtual Reality-based services will make the MEC and Fog resources insufficient. To cope with this issue, we propose a system model with onboard computation offloading, where an EV can utilize its neighboring EVs resources that are not resource-constrained to enhance its computing capacity. Then, we propose to solve the problem of computational task offloading by jointly considering the communication, computation, and control in a mobile vehicular network. We formulate a mixed-integer non-linear problem (MINLP) to minimize the trade-off between latency and energy consumption subject to the network resources and the mobility of EVs. The formulated problem is solved via the block coordination descent (BCD) method. In such a way, we decompose the original MINLP problem into three subproblems which are resource block allocation (RBA), power control and interference management (PCP), and offload decision problem (ODP). We then alternatively obtain solutions of RBA and PCP via the duality theory, and the third sub-problem is solvable via the relaxation method and alternating direction Lagrangian multiplier method (ADMM). Numerical results reveal that the proposed solution BCD-based algorithm performs a fast convergence rate. |
| first_indexed | 2024-04-13T13:28:35Z |
| format | Article |
| id | doaj.art-bacab530e3ba42d6982c6c257d6434b6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T13:28:35Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bacab530e3ba42d6982c6c257d6434b62022-12-22T02:45:02ZengIEEEIEEE Access2169-35362022-01-011012251312252910.1109/ACCESS.2022.32202519940624Joint Communication, Computation, and Control for Computational Task Offloading in Vehicle-Assisted Multi-Access Edge ComputingTri Nguyen Dang0https://orcid.org/0000-0003-0188-1535Aunas Manzoor1https://orcid.org/0000-0003-4998-9748Yan Kyaw Tun2https://orcid.org/0000-0002-8557-0082S. M. Ahsan Kazmi3https://orcid.org/0000-0001-7138-8258Rim Haw4Sang Hoon Hong5https://orcid.org/0000-0001-7239-1301Zhu Han6https://orcid.org/0000-0002-6606-5822Choong Seon Hong7https://orcid.org/0000-0003-3484-7333Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Republic of KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Yongin-si, Republic of KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Yongin-si, Republic of KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Yongin-si, Republic of KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Yongin-si, Republic of KoreaDepartment of Electronic Engineering, Kyung Hee University, Yongin-si, Republic of KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Yongin-si, Republic of KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Yongin-si, Republic of KoreaFuture generation of Electric Vehicles (EVs) equipped with modern technologies will impose a significant burden on computation and communication to the network due to the vast extension of onboard infotainment services. To overcome this challenge, multi-access edge computing (MEC) or Fog Computing can be employed. However, the massive adoption of novel infotainment services such as Augmented Reality, Virtual Reality-based services will make the MEC and Fog resources insufficient. To cope with this issue, we propose a system model with onboard computation offloading, where an EV can utilize its neighboring EVs resources that are not resource-constrained to enhance its computing capacity. Then, we propose to solve the problem of computational task offloading by jointly considering the communication, computation, and control in a mobile vehicular network. We formulate a mixed-integer non-linear problem (MINLP) to minimize the trade-off between latency and energy consumption subject to the network resources and the mobility of EVs. The formulated problem is solved via the block coordination descent (BCD) method. In such a way, we decompose the original MINLP problem into three subproblems which are resource block allocation (RBA), power control and interference management (PCP), and offload decision problem (ODP). We then alternatively obtain solutions of RBA and PCP via the duality theory, and the third sub-problem is solvable via the relaxation method and alternating direction Lagrangian multiplier method (ADMM). Numerical results reveal that the proposed solution BCD-based algorithm performs a fast convergence rate.https://ieeexplore.ieee.org/document/9940624/Multi-access edge computing (MEC)collaborative V2Vs-assisted MEC systemtasks offloadingresource allocationalternating direction method of multipliers (ADMM)interference management |
| spellingShingle | Tri Nguyen Dang Aunas Manzoor Yan Kyaw Tun S. M. Ahsan Kazmi Rim Haw Sang Hoon Hong Zhu Han Choong Seon Hong Joint Communication, Computation, and Control for Computational Task Offloading in Vehicle-Assisted Multi-Access Edge Computing IEEE Access Multi-access edge computing (MEC) collaborative V2Vs-assisted MEC system tasks offloading resource allocation alternating direction method of multipliers (ADMM) interference management |
| title | Joint Communication, Computation, and Control for Computational Task Offloading in Vehicle-Assisted Multi-Access Edge Computing |
| title_full | Joint Communication, Computation, and Control for Computational Task Offloading in Vehicle-Assisted Multi-Access Edge Computing |
| title_fullStr | Joint Communication, Computation, and Control for Computational Task Offloading in Vehicle-Assisted Multi-Access Edge Computing |
| title_full_unstemmed | Joint Communication, Computation, and Control for Computational Task Offloading in Vehicle-Assisted Multi-Access Edge Computing |
| title_short | Joint Communication, Computation, and Control for Computational Task Offloading in Vehicle-Assisted Multi-Access Edge Computing |
| title_sort | joint communication computation and control for computational task offloading in vehicle assisted multi access edge computing |
| topic | Multi-access edge computing (MEC) collaborative V2Vs-assisted MEC system tasks offloading resource allocation alternating direction method of multipliers (ADMM) interference management |
| url | https://ieeexplore.ieee.org/document/9940624/ |
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