On-Demand Transmission Interval Control Method for Spatio-Temporal Data Retention

  • Shumpei YamasakiEmail author
  • Daiki Nobayashi
  • Kazuya Tsukamoto
  • Takeshi Ikenaga
  • Myung Lee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1035)


With the development and the spread of Internet of Things (IoT) technologies, various types of data are generated for IoT applications anywhere and anytime. We defined such data that depends heavily on generation time and location as Spatio-Temporal Data (STD). In the previous works, we have proposed the data retention system using vehicular networks to achieve the paradigm of “local production and consumption of STD.” The system can provide STDs quickly for users within a specific location by retaining the STD within the area. However, the system does not consider that each STD has different requirements for the data retention. In particular, the lifetime of the STD and the diffusion time to the whole area directly influence to the performance of data retention. Therefore, we propose a dynamic control of data transmission interval for the data retention system by considering the requirements. Through the simulation evaluation, we found that our proposed method can satisfy the requirements of STD and maintain a high coverage rate in the area.



This work was partially supported by JSPS KAKENHI Grant Number 18H03234, NICT Grant Number 19304, and USA Grant number NSF 17-586.


  1. 1.
    Wang, S., Zhang, X., Zhang, Y., Wang, L., Yang, J., Wang, W.: A survey on mobile edge networks: convergence of computing, caching and communications. IEEE Access 5, 1–3 (2017)CrossRefGoogle Scholar
  2. 2.
    Beuchert, M., Jensen, S.H., Sheikh-Omar, O.A., Svendsen, M.B., Yang, B.: aSTEP: Aau’s Spatio-TEmporal data analytics platform. In: 2018 19th IEEE International Conference on Mobile Data Management (MDM), Aalborg, pp. 278–279 (2018)Google Scholar
  3. 3.
    Teshiba, H., Nobayashi, D., Tsukamoto, K., Ikenaga, T.: Adaptive data transmission control for reliable and efficient spatio-temporal data retention by vehicles. In: The Sixteenth International Conference on Networks, pp. 46–52 (2017)Google Scholar
  4. 4.
    Higuchi, T., Onishi, R., Altintas, O., Nobayashi, D., Ikenaga, T., Tsukamoto, K.: Regional infohubs by vehicles: balancing spatio-temporal coverage and network load. In: Proceedings of IoV-VoI 2016, pp. 25–30 (2016)Google Scholar
  5. 5.
    Li, F., Wang, Y.: Routing in vehicular ad hoc net-works: a survey. IEEE Veh. Technol. Mag. 2(2), 12–22 (2007)CrossRefGoogle Scholar
  6. 6.
    Leontiadis, I., Costa, P., Mascolo, C.: Persistent content based information dissemination in hybrid vehicular networks. In: Proceedings of IEEE PerCom, pp. 1–10 (2009)Google Scholar
  7. 7.
    Ott, J., Hyyti, E., Lassila, P., Vaegs, T., Kangasharju, J.: Floating content: information sharing in urban areas. In: Proceedings of IEEE PerCom, pp. 136–146 (2011)Google Scholar
  8. 8.
    Thompson, N., Crepaldi, R., Kravets, R.: Locus: a location-based data overlay for disruption-tolerant networks. In: Proceedings of ACM CHANTS, pp. 47–54 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Shumpei Yamasaki
    • 1
    Email author
  • Daiki Nobayashi
    • 1
  • Kazuya Tsukamoto
    • 1
  • Takeshi Ikenaga
    • 1
  • Myung Lee
    • 2
  1. 1.Kyushu Institute of TechnologyFukuokaJapan
  2. 2.CUNY, City CollegeNew YorkUSA

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