Computational Resources Allocation and Vehicular Application Offloading in VEC Networks
With the advances in wireless communications and the Internet of Things (IoT), various vehicular applications such as image-aided navigation and autonomous driving are emerging. These vehicular applications require a significant number of computation resources and a lower processing delay. However,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/14/2130 |
| _version_ | 1797433685385936896 |
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| author | Fan Gu Xiaoying Yang Xianwei Li Haiquan Deng |
| author_facet | Fan Gu Xiaoying Yang Xianwei Li Haiquan Deng |
| author_sort | Fan Gu |
| collection | DOAJ |
| description | With the advances in wireless communications and the Internet of Things (IoT), various vehicular applications such as image-aided navigation and autonomous driving are emerging. These vehicular applications require a significant number of computation resources and a lower processing delay. However, these resource-limited and power-constrained vehicles may not meet the requirements of processing these vehicular applications. By offloading these vehicular applications to the edge cloud, vehicular edge computing (VEC) is deemed a novel paradigm for improving vehicular performance. However, how to optimize the allocation of computation resources of both vehicles and VEC servers to reduce the energy and delay is a challenging issue when deploying the VEC systems. In this article, we try to address this issue and propose a vehicular application offloading and computational resources allocation strategy. We formulate an optimization problem and present an efficient offloading scheme for vehicular applications. Extensive simulation results are offered to analyze the performances of the proposed scheme. In comparison with the benchmark schemes, the proposed scheme can outperform them in terms of computation cost. |
| first_indexed | 2024-03-09T10:20:31Z |
| format | Article |
| id | doaj.art-a705ae81ee92474197506c1d541b47a4 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T10:20:31Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a705ae81ee92474197506c1d541b47a42023-12-01T22:05:05ZengMDPI AGElectronics2079-92922022-07-011114213010.3390/electronics11142130Computational Resources Allocation and Vehicular Application Offloading in VEC NetworksFan Gu0Xiaoying Yang1Xianwei Li2Haiquan Deng3Department of Electronic Commerce, Anhui Institute of International Business, Hefei 230000, ChinaSchool of Information Engineering, Suzhou University, Suzhou 234000, ChinaSchool of Computer and Information Engineering, Bengbu University, Bengbu 233000, ChinaChina Mobile Group Hunan Company Limited Chenzhou Branch, Chenzhou 423000, ChinaWith the advances in wireless communications and the Internet of Things (IoT), various vehicular applications such as image-aided navigation and autonomous driving are emerging. These vehicular applications require a significant number of computation resources and a lower processing delay. However, these resource-limited and power-constrained vehicles may not meet the requirements of processing these vehicular applications. By offloading these vehicular applications to the edge cloud, vehicular edge computing (VEC) is deemed a novel paradigm for improving vehicular performance. However, how to optimize the allocation of computation resources of both vehicles and VEC servers to reduce the energy and delay is a challenging issue when deploying the VEC systems. In this article, we try to address this issue and propose a vehicular application offloading and computational resources allocation strategy. We formulate an optimization problem and present an efficient offloading scheme for vehicular applications. Extensive simulation results are offered to analyze the performances of the proposed scheme. In comparison with the benchmark schemes, the proposed scheme can outperform them in terms of computation cost.https://www.mdpi.com/2079-9292/11/14/2130IoTvehicular applicationsVECMINP |
| spellingShingle | Fan Gu Xiaoying Yang Xianwei Li Haiquan Deng Computational Resources Allocation and Vehicular Application Offloading in VEC Networks Electronics IoT vehicular applications VEC MINP |
| title | Computational Resources Allocation and Vehicular Application Offloading in VEC Networks |
| title_full | Computational Resources Allocation and Vehicular Application Offloading in VEC Networks |
| title_fullStr | Computational Resources Allocation and Vehicular Application Offloading in VEC Networks |
| title_full_unstemmed | Computational Resources Allocation and Vehicular Application Offloading in VEC Networks |
| title_short | Computational Resources Allocation and Vehicular Application Offloading in VEC Networks |
| title_sort | computational resources allocation and vehicular application offloading in vec networks |
| topic | IoT vehicular applications VEC MINP |
| url | https://www.mdpi.com/2079-9292/11/14/2130 |
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