research-article

Centrally Controlled Mass Data Offloading Using Vehicular Traffic

Authors:

Benjamin Baron,

Prométhée Spathis,

Marcelo Dias de Amorim,

Yannis Viniotis,

Mostafa H. AmmarAuthors Info & Claims

IEEE Transactions on Network and Service Management, Volume 14, Issue 2

Pages 401 - 415

https://doi.org/10.1109/TNSM.2017.2672878

Published: 01 June 2017 Publication History

Abstract

With over 300 billion vehicle trips made in the United States and 64 billion in France per year, network operators have the opportunity to utilize the existing road and highway network as an alternative data network to offload large amounts of delay-tolerant traffic. To enable the road network as a large-capacity transmission system, we exploit the existing mobility of vehicles equipped with wireless and storage capacities together with a collection of <italic>offloading spots</italic>. An offloading spot is a data storage equipment located where vehicles usually park. Data is transloaded from a conventional data network to the closest offloading spot and then shipped by vehicles along their line of travel. The subsequent offloading spots act as data relay boxes where vehicles can drop off data for later pick-up by other vehicles, depending on their direction of travel. The main challenges of this offloading system are how to compute the road path matching the performance requirements of a data transfer and how to configure the sequence of offloading spots involved in the transfer. We propose a scalable and adaptive centralized architecture built on software-defined networking that maximizes the utilization of the flow of vehicles connecting consecutive offloading spots. We simulate the performance of our system using real roads traffic counts for France. Results show that the centralized controlled offloading architecture can achieve an efficient and fair allocation of concurrent data transfers between major cities in France.

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Cited By

Ahmed MMirza MRaza SAhmad HXu FKhan WLin QHan Z(2023)Vehicular Communication Network Enabled CAV Data Offloading: A ReviewIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.326364324:8(7869-7897)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3263643
Kar BYahya WLin YAli A(2023)Offloading Using Traditional Optimization and Machine Learning in Federated Cloud–Edge–Fog Systems: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.323957925:2(1199-1226)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3239579
Wang EWang HDong PXu YYang Y(2022)Distributed Game-Theoretical D2D-Enabled Task Offloading in Mobile Edge ComputingJournal of Computer Science and Technology10.1007/s11390-022-2063-337:4(919-941)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s11390-022-2063-3
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Centrally Controlled Mass Data Offloading Using Vehicular Traffic

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cover image IEEE Transactions on Network and Service Management

IEEE Transactions on Network and Service Management Volume 14, Issue 2

June 2017

252 pages

ISSN:1932-4537

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1932-4537 © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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IEEE Press

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Published: 01 June 2017

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Cited By

Ahmed MMirza MRaza SAhmad HXu FKhan WLin QHan Z(2023)Vehicular Communication Network Enabled CAV Data Offloading: A ReviewIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.326364324:8(7869-7897)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3263643
Kar BYahya WLin YAli A(2023)Offloading Using Traditional Optimization and Machine Learning in Federated Cloud–Edge–Fog Systems: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.323957925:2(1199-1226)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3239579
Wang EWang HDong PXu YYang Y(2022)Distributed Game-Theoretical D2D-Enabled Task Offloading in Mobile Edge ComputingJournal of Computer Science and Technology10.1007/s11390-022-2063-337:4(919-941)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s11390-022-2063-3
Saki MAbolhasan MLipman JJamalipour A(2021)Mobility Model for Contact-Aware Data Offloading Through Train-to-Train Communications in Rail NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.301458823:1(597-609)Online publication date: 27-Dec-2021
https://dl.acm.org/doi/10.1109/TITS.2020.3014588
Boukerche ASoto V(2020)Computation Offloading and Retrieval for Vehicular Edge ComputingACM Computing Surveys10.1145/339206453:4(1-35)Online publication date: 20-Aug-2020
https://dl.acm.org/doi/10.1145/3392064
Ruan YJoe-Wong C(2020)On the Economic Value of Mobile CachingIEEE INFOCOM 2020 - IEEE Conference on Computer Communications10.1109/INFOCOM41043.2020.9155336(984-993)Online publication date: 6-Jul-2020
https://dl.acm.org/doi/10.1109/INFOCOM41043.2020.9155336
Ren HLiu KDai PLi YXie RGuo S(2020)Adaptive Task Scheduling via End-Edge-Cloud Cooperation in Vehicular NetworksWireless Algorithms, Systems, and Applications10.1007/978-3-030-59016-1_34(407-419)Online publication date: 13-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-59016-1_34

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