short-paper

Towards Data VSN Offloading in VANETs Integrated into the Cellular Network

Authors:

Douglas L. L. Moura,

Andre L. L. Aquino,

Antonio A. F. LoureiroAuthors Info & Claims

MSWIM '19: Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

Pages 235 - 239

https://doi.org/10.1145/3345768.3355937

Published: 25 November 2019 Publication History

Abstract

Vehicular sensing network consists of a promising remote sensing paradigm, in which a variety of new applications will be possible through the processing of data periodically collected by vehicles. However, the acquisition of a large amount of sensing data and the increasing demand for traffic cellular requires mechanisms to decongest the cellular network infrastructure. Thus, this work uses a centrality measure to propose an offloading scheme in which certain vehicles will collect data from their neighbors and transmit it in the cellular uplink. The presented article models the problem as a minimum d-hop dominating set and create a greedy algorithm to solve it. The evaluation considers a realistic dataset to evaluate the proposed approach. When compared with the non-offloading scheme, it shows a cost reduction of up to 82.09% in the best-case and 13.45% in the worst-case.

References

[1]

Sherin Abdelhamid, Hossam S. Hassanein, and Glen Takahara. 2014. Vehicle as a mobile sensor. Procedia Computer Science, Vol. 34 (2014), 286--295.

[2]

A. D. Amis, R. Prakash, T. H. P. Vuong, and D. T. Huynh. 2000. Max-min d-cluster formation in wireless ad hoc networks. In Proceedings IEEE INFOCOM 2000, Vol. 1. IEEE, Tel Aviv, Israel, Israel, 32--41 vol.1.

[3]

S. Ancona, R. Stanica, and M. Fiore. 2014. Performance boundaries of massive Floating Car Data offloading. In Conference on Wireless On-demand Network Systems and Services. IEEE, Obergurgl, Austria, 89--96.

[4]

Alessandro Bazzi, Barbara M Masini, Alberto Zanella, and Gianni Pasolini. 2015. IEEE 802.11p for cellular offloading in vehicular sensor networks. Computer Communications, Vol. 60 (2015), 97--108.

Digital Library

[5]

Amit Dua, Neeraj Kumar, and Seema Bawa. 2017. Game theoretic approach for real-time data dissemination and offloading in vehicular ad hoc networks. Journal of Real-Time Image Processing, Vol. 13, 3 (01 Sep 2017), 627--644.

Digital Library

[6]

Cisco VNI Forecast. 2019. Global Mobile Data Traffic Forecast Update, 2017--2022. https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11--738429.html. [Accessed: 2019-08--26].

[7]

Linton C. Freeman. 1978. Centrality in social networks conceptual clarification. Social Networks, Vol. 1, 3 (1978), 215 -- 239.

[8]

MEMS Journal. 2017. Automotive Sensors and Electronics Expo 2017. http://www.automotivesensors2017.com/. [Accessed: 2019-04-05].

[9]

Uichin Lee, Eugenio Magistretti, Mario Gerla, Paolo Bellavista, Pietro Liò, and Kang-Won Lee. 2008. Bio-Inspired Multi-agent Collaboration for Urban Monitoring Applications. In Bio-Inspired Computing and Communication. Springer Berlin Heidelberg, Berlin, Heidelberg, 204--216.

[10]

Guoqiang Mao, Zijie Zhang, and Brian D O Anderson. 2016. Cooperative Content Dissemination and Offloading in Heterogeneous Mobile Networks. IEEE Transactions on Vehicular Technology, Vol. 65, 8 (2016), 6573--6587.

[11]

Farouk Mezghani, Riadh Dhaou, Michele Nogueira, and Andre-Luc Beylot. 2016. Offloading Cellular Networks Through V2V Communications - How to Select the Seed-Vehicles?. In IEEE International Conference on Communications. IEEE, Kuala Lumpur, Malaysia, 1--6.

[12]

Paulo H. Rettore, Guilherme Maia, Leandro A. Villas, and Antonio A. F. Loureiro. 2019. Vehicular Data Space: The Data Point of View. IEEE Communications Surveys & Tutorials, Vol. 21, 3 (2019), 2392--2418.

[13]

Paulo H. L. Rettore, André B. Campolina, Artur Souza, Guilherme Maia, Leandro A. Villas, and Antonio A. F. Loureiro. 2018. Driver Authentication in VANETs based on Intra-Vehicular Sensor Data. In Proceedings of the 23rd IEEE International Symposium on Computers and Communications (ISCC '18). 1--6.

[14]

P. Salvo, I. Turcanu, F. Cuomo, A. Baiocchi, and I. Rubin. 2016. LTE floating car data application off-loading via VANET driven clustering formation. In Wireless On-demand Network Systems and Services. IEEE, Cortina d'Ampezzo, Italy, 1--8.

[15]

R. Stanica, M. Fiore, and F. Malandrino. 2013. Offloading Floating Car Data. In IEEE WoWMoM. IEEE, Madrid, Spain, 1--9.

[16]

Sandesh Uppoor, Oscar Trullols-Cruces, Marco Fiore, and Jose M. Barcelo-Ordinas. 2014. Generation and analysis of a large-scale urban vehicular mobility dataset. IEEE Transactions on Mobile Computing, Vol. 13, 5 (2014), 1061--1075.

Digital Library

[17]

J. Wang, C. Jiang, K. Zhang, T. Q. S. Quek, Y. Ren, and L. Hanzo. 2018. Vehicular Sensing Networks in a Smart City: Principles, Technologies and Applications. IEEE Wireless Communications, Vol. 25, 1 (February 2018), 122--132.

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: Aug-2023
https://doi.org/10.1109/TITS.2023.3263643
Nasr Azadani MBoukerche A(2022)DriverRep: Driver identification through driving behavior embeddingsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.01.010162(105-117)Online publication date: Apr-2022
https://doi.org/10.1016/j.jpdc.2022.01.010
Boukerche ATao YSun P(2020)Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systemsComputer Networks10.1016/j.comnet.2020.107484(107484)Online publication date: Aug-2020
https://doi.org/10.1016/j.comnet.2020.107484

Index Terms

Towards Data VSN Offloading in VANETs Integrated into the Cellular Network
1. Networks
  1. Network types
    1. Ad hoc networks
      1. Mobile ad hoc networks

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Information & Contributors

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Published In

cover image ACM Conferences

MSWIM '19: Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 2019

340 pages

ISBN:9781450369046

DOI:10.1145/3345768

General Chair:
Antonio A. F. Loureiro
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Salil Kanhere
University of New South Wales, Australia
,
Paolo Bellavista
University of Bologna, Italy

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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Short-paper

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CAPES
FAPEMIG
CNPq
Sao Paulo Research Foundation (FAPESP)
FAPEAL

Conference

MSWiM '19

Sponsor:

SIGSIM

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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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: Aug-2023
https://doi.org/10.1109/TITS.2023.3263643
Nasr Azadani MBoukerche A(2022)DriverRep: Driver identification through driving behavior embeddingsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.01.010162(105-117)Online publication date: Apr-2022
https://doi.org/10.1016/j.jpdc.2022.01.010
Boukerche ATao YSun P(2020)Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systemsComputer Networks10.1016/j.comnet.2020.107484(107484)Online publication date: Aug-2020
https://doi.org/10.1016/j.comnet.2020.107484

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