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V2V Online Data Offloading Method Based on Vehicle Mobility

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Data Science (ICPCSEE 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1257))

Abstract

As people are accustomed to getting information in the vehicles, mobile data offloading through Vehicular Ad Hoc Networks (VANETs) becomes prevalent nowadays. However, the impacts caused by the vehicle mobility (such as the relative speed and direction between vehicles) have great effects on mobile data offloading. In this paper, a V2V online data offloading method is proposed based on vehicle mobility. In this mechanism, the network service process was divided into continuous and equal-sized time slots. Data were transmitted in a multicast manner for the sake of fairness. The data offloading problem was formalized to maximize the overall satisfaction of the vehicle users. In each time slot, a genetic algorithm was used to solve the maximizing problem to obtain a mobile data offloading strategy. And then, the performance of the algorithm was enhanced by improving the algorithm. The experiment results show that vehicle mobility has a great effect on mobile data offloading, and the mobile data offloading method proposed in the paper is effective.

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References

  1. Zhu, C.: Social sensor cloud: framework, greenness, issues, and outlook. IEEE Netw. 32(5), 100–105 (2018)

    Article  Google Scholar 

  2. Zhu, C.: Toward big data in green city. IEEE Commun. Mag. 55(11), 14–18 (2017)

    Article  Google Scholar 

  3. Khazraeian, S., Hadi, M.: Intelligent transportation systems in future smart cities. In: Amini, M.H., Boroojeni, K.G., Iyengar, S.S., Pardalos, P.M., Blaabjerg, F., Madni, A.M. (eds.) Sustainable Interdependent Networks II. SSDC, vol. 186, pp. 109–120. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-98923-5_6

    Chapter  Google Scholar 

  4. Zhou, H.: A time-ordered aggregation model-based centrality metric for mobile social networks. IEEE Access 6, 25588–25599 (2018)

    Article  Google Scholar 

  5. Chen, H.: Probabilistic detection of missing tags for anonymous multicategory RFID systems. IEEE Trans. Veh. Technol. 66(12), 11295–11305 (2017)

    Article  Google Scholar 

  6. Liu, D.: User association in 5G networks: a survey and an outlook. IEEE Commun. Surv. Tutor. 18(2), 1018–1044 (2016)

    Article  Google Scholar 

  7. Sun, Y.: Traffic offloading for online video service in vehicular networks: a cooperative approach. IEEE Trans. Veh. Technol. 67(8), 7630–7642 (2018)

    Article  Google Scholar 

  8. Zhu, X.: Contact-aware optimal resource allocation for mobile data offloading in opportunistic vehicular networks. IEEE Trans. Veh. Technol. 66(8), 7384–7399 (2017)

    Article  Google Scholar 

  9. Wang, N., Wu, J.: Optimal cellular traffic offloading through opportunistic mobile networks by data partitioning. In: 2018 IEEE International Conference on Communications (ICC), pp. 1–6 (2018)

    Google Scholar 

  10. Yuan, Q., Li, J., Liu, Z., et al.: Space and time constrained data offloading in vehicular networks. In: 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 398–405 (2016)

    Google Scholar 

  11. Luoto, P., Bennis, M., Pirinen, P., et al.: Vehicle clustering for improving enhanced LTE-V2X network performance. In: 2017 European Conference on Networks and Communications (EuCNC), pp. 1–5 (2017)

    Google Scholar 

  12. Vigneri, L., Spyropoulos, T., Barakat, C.: Storage on wheels: offloading popular contents through a vehicular cloud. In: 2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–9 (2016)

    Google Scholar 

  13. Hanggoro, A., Sari, R.F.: Performance evaluation of the manhattan mobility model in vehicular ad-hoc networks for high mobility vehicle. In: 2013 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), pp. 31–36 (2013)

    Google Scholar 

  14. Gowrishankar, S., Sarkar, S., Basavaraju, T.: Simulation based performance comparison of community model, GFMM, RPGM, Manhattan model and RWP-SS mobility models in MANET. In: 2009 First International Conference on Networks & Communications, pp. 408–413 (2009)

    Google Scholar 

  15. Perdana, D., Nanda, M., Ode, R., et al.: Performance evaluation of PUMA routing protocol for Manhattan mobility model on vehicular ad-hoc network. In: 2015 22nd International Conference on Telecommunications (ICT), pp. 80–84 (2015)

    Google Scholar 

  16. Shrestha, A.P., Won, J., Yoo, S.-J., et al.: Genetic algorithm based sensing and channel allocation in cognitive ad-hoc networks. In: 2016 International Conference on Information and Communication Technology Convergence (ICTC), pp. 109–111 (2016)

    Google Scholar 

  17. Bhattacharjee, S., Konar, A., Nagar, A.K.: Channel allocation for a single cell cognitive radio network using genetic algorithm. In: 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 258–264 (2011)

    Google Scholar 

  18. Sun, W., Xie, W., He, J.: Data link network resource allocation method based on genetic algorithm. In: 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 1875–1880 (2019)

    Google Scholar 

  19. Khan, A., Sadhu, S., Yeleswarapu, M.: A comparative analysis of DSRC and 802.11 over Vehicular Ad hoc Networks. Project Report, University of California, Santa Barbara, pp. 1–8 (2009)

    Google Scholar 

  20. Kaabi, F., Cataldi, P., Filali, F., et al.: Performance analysis of IEEE 802.11 p control channel. In: 2010 Sixth International Conference on Mobile Ad-hoc and Sensor Networks, pp. 211–214 (2010)

    Google Scholar 

  21. Neves, F., Cardote, A., Moreira, R., et al.: Real-world evaluation of IEEE 802.11 p for vehicular networks. In: Proceedings of the Eighth ACM International Workshop on Vehicular Inter-Networking, pp. 89–90 (2011)

    Google Scholar 

  22. Vinel, A., Belyaev, E., Lamotte, O., et al.: Video transmission over IEEE 802.11 p: real-world measurements. In: 2013 IEEE International Conference on Communications Workshops (ICC), pp. 505–509 (2013)

    Google Scholar 

  23. Gozálvez, J.: IEEE 802.11 p vehicle to infrastructure communications in urban environments. IEEE Commun. Mag. 50(5), 176–183 (2012)

    Google Scholar 

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Acknowledgement

This work is supported by the System Architecture Project (No. 61400040503), the Natural Science Foundation of China (No. 61872104), the Natural Science Foundation of Heilongjiang Province in China (No. F2016028), the Fundamental Research Fund for the Central Universities in China, and Tianjin Key Laboratory of Advanced Networking (TANK) in College of Intelligence and Computing of Tianjin University.

Author information

Authors and Affiliations

  1. Jiangsu Automation Research Institute, Lianyungang, 222002, China

    Xianlang Hu & Dafang Zhang

  2. College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China

    Shizhao Zhao, Guangsheng Feng & Hongwu Lv

Authors
  1. Xianlang Hu
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  2. Dafang Zhang
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  3. Shizhao Zhao
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  4. Guangsheng Feng
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  5. Hongwu Lv
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Corresponding author

Correspondence to Guangsheng Feng .

Editor information

Editors and Affiliations

  1. North University of China, Taiyuan, China

    Jianchao Zeng

  2. Northeast Forestry University, Harbin, China

    Weipeng Jing

  3. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  4. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Hu, X., Zhang, D., Zhao, S., Feng, G., Lv, H. (2020). V2V Online Data Offloading Method Based on Vehicle Mobility. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_4

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  • DOI: https://doi.org/10.1007/978-981-15-7981-3_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7980-6

  • Online ISBN: 978-981-15-7981-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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