research-article

On Leveraging the Computational Potential of Fog-Enabled Vehicular Networks

Authors:

Ibrahim Sorkhoh,

Dariush Ebrahimi,

Sanaa Sharafeddine,

Chadi AssiAuthors Info & Claims

DIVANet '19: Proceedings of the 9th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications

Pages 9 - 16

https://doi.org/10.1145/3345838.3356009

Published: 25 November 2019 Publication History

Abstract

The advent of autonomous vehicles demands powerful processing capabilities of on-board units to handle the dramatic increase of sensor data used to make safe self-driving decisions. Those com- putational resources, being constantly available on the highways, represent valuable assets that can be leveraged to serve as fog computing facility to computational tasks generated from other vehicles or even from different networks. In this paper, we propose a fog-enabled system scheme that can be deployed on a road side unit (RSU) to schedule and offload requested computational tasks over the available vehicles' on-board units (OBUs). The goal is to maximize the weighted sum of the admitted tasks. We model the problem as a Mixed Integer Linear Programming (MILP), and due to NP-hardness, we propose a Dantzig-Wolfe decomposition method to provide a scalable solution. The experiment shows that our ap- proach has a sufficient effectiveness in terms of both computational complexity and tasks acceptance rate.

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Cited By

Tripathy SBebortta SGadekallu T(2024)Sustainable Fog-Assisted Intelligent Monitoring Framework for Consumer Electronics in Industry 5.0 ApplicationsIEEE Transactions on Consumer Electronics10.1109/TCE.2023.333245470:1(1501-1510)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3332454
Li C(2020)Information processing in Internet of Things using big data analyticsComputer Communications10.1016/j.comcom.2020.06.020160(718-729)Online publication date: Jul-2020
https://doi.org/10.1016/j.comcom.2020.06.020

Index Terms

On Leveraging the Computational Potential of Fog-Enabled Vehicular Networks
1. Networks
  1. Network algorithms
    1. Control path algorithms
      1. Network resources allocation
2. Theory of computation
  1. Computational complexity and cryptography
    1. Problems, reductions and completeness
  2. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming
    2. Mathematical optimization
      1. Mixed discrete-continuous optimization
        Integer programming

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Published In

cover image ACM Conferences

DIVANet '19: Proceedings of the 9th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications

November 2019

119 pages

ISBN:9781450369077

DOI:10.1145/3345838

General Chair:
Mirela Notare
Technology University on Fly Transportation, Brazil
,
Program Chair:
Peng Sun
University of Ottawa, Canada

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 November 2019

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MSWiM '19

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MSWiM '19: 22nd Int'l ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 25 - 29, 2019

FL, Miami Beach, USA

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Overall Acceptance Rate 70 of 308 submissions, 23%

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Cited By

Tripathy SBebortta SGadekallu T(2024)Sustainable Fog-Assisted Intelligent Monitoring Framework for Consumer Electronics in Industry 5.0 ApplicationsIEEE Transactions on Consumer Electronics10.1109/TCE.2023.333245470:1(1501-1510)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3332454
Li C(2020)Information processing in Internet of Things using big data analyticsComputer Communications10.1016/j.comcom.2020.06.020160(718-729)Online publication date: Jul-2020
https://doi.org/10.1016/j.comcom.2020.06.020

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