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Author:

Munawar, Suleman (Munawar, Suleman.) | Ali, Zaiwar (Ali, Zaiwar.) | Waqas, Muhammad (Waqas, Muhammad.) | Tu, Shanshan (Tu, Shanshan.) | Hassan, Syed Ali (Hassan, Syed Ali.) | Abbas, Ghulam (Abbas, Ghulam.)

Indexed by:

EI Scopus SCIE

Abstract:

Many advancements are being made in vehicular networks, such as self-driving, dynamic route scheduling, real-time traffic condition monitoring, and on-board infotainment services. However, these services require high computation power and precision and can be met using mobile edge computing (MEC) mechanisms for vehicular networks. MEC operates through the edge servers available at the roadside, also known as roadside units (RSU). MEC is very useful for vehicular networks because it has extremely low latency and supports operations that require near-real-time access to rapidly changing data. This paper proposes an efficient computational offloading, smart division of tasks, and cooperation among RSUs to increase service performance and decrease the delay in a vehicular network via MEC. The computational delay is further reduced by parallel processing. In the division of tasks, each task is divided into two sub-components which are fed to a deep neural network (DNN) for training. Consequently, this reduces the overall time delay and overhead. We also adopt an efficient routing policy to deliver the results through the shortest path to improve service reliability. The offloading, computing, division, and routing policies are formulated, and a model-based DNN approach is used to obtain an optimal solution. Simulation results prove that our proposed approach is suitable in a dynamic environment. We also compare our results with the existing state-of-the-art, showing that our proposed approach outperforms the existing schemes.

Keyword:

vehicular networks Servers computational offloading Computational modeling Delays Delay effects Deep neural network (DNN) routing policy mobile edge computing Task analysis Cloud computing Routing smart task division

Author Community:

  • [ 1 ] [Munawar, Suleman]Ghulam Ishaq Khan Inst Engn Sci & Technol, Fac Comp Sci & Engn, Topi 23460, Pakistan
  • [ 2 ] [Ali, Zaiwar]Ghulam Ishaq Khan Inst Engn Sci & Technol, Fac Comp Sci & Engn, Topi 23460, Pakistan
  • [ 3 ] [Abbas, Ghulam]Ghulam Ishaq Khan Inst Engn Sci & Technol, Fac Comp Sci & Engn, Topi 23460, Pakistan
  • [ 4 ] [Waqas, Muhammad]Univ Bahrain, Coll Informat Technol, Dept Comp Engn, Sakhir 32038, Bahrain
  • [ 5 ] [Waqas, Muhammad]Edith Cowan Univ, Sch Engn, Perth, WA 6027, Australia
  • [ 6 ] [Tu, Shanshan]Beijing Univ Technol, Fac Informat Technol, Engn Res Ctr Intelligent Percept & Autonomous Cont, Beijing 100021, Peoples R China
  • [ 7 ] [Hassan, Syed Ali]Natl Univ Sci & Technol, Sch Elect Engn & Comp Sci, Islamabad 44000, Pakistan

Reprint Author's Address:

  • [Waqas, Muhammad]Univ Bahrain, Coll Informat Technol, Dept Comp Engn, Sakhir 32038, Bahrain;;

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Related Keywords:

Source :

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY

ISSN: 0018-9545

Year: 2023

Issue: 3

Volume: 72

Page: 3376-3391

6 . 8 0 0

JCR@2022

ESI Discipline: ENGINEERING;

ESI HC Threshold:19

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 15

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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