On Feasibility of Crowdsourced Mobile Sensing for Smarter City Life

  • Kenro AiharaEmail author
  • Piao Bin
  • Hajime Imura
  • Atsuhiro Takasu
  • Yuzuru Tanaka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9749)


This paper introduces the ongoing project that aims to develop a mobile sensing framework to collect sensor data reflecting personal-scale, or microscopic, roadside phenomena by crowdsourcing and also using social big data, such as traffic, climate, and contents of social network services like Twitter. To collect them, smartphone applications are provided. One of the typical applications is a driving recorder that collects not only sensor data but also recorded videos from the driver’s view. To extract specific roadside phenomena, collected data are integrated and analyzed at the service platform.

The proposed smartphone application can be replaced with appliances because of its advantages: (1) ordinary appliances work stand-alone, which means that local storage is limited; the application is connected to the cloud, (2) appliances are not cheep, at least users must pay for it; the application is free, (3) appliances only store driving records; the application can get feedback from the service. The authors expect that these advantages can be accepted by citizen as an incentive to use it. To reveal how effective such function is for users’ motivation, an experiment and a survey are conducted with our prototyped service. As a result, most of the users accepted the function as attractive to use.


Road Segment Smart City Road Condition Traffic Information Service Platform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank City of Sapporo, Hokkaido Government, Hokkaido Chuo Bus Co., Ltd. for their cooperation with this research.

Part of this research was supported by the CPS-IIP Project in the research promotion program “Research and Development for the Realization of Next-Generation IT Platforms” of the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT), “Research and Development on Fundamental and Utilization Technologies for Social Big Data” of the Commissioned Research of National Institute of Information and Communications Technology (NICT), Japan.


  1. 1.
    Amichai-Hamburger, Y.: Potential and promise of online volunteering. Comput. Human Behav. 24(2), 544–562 (2008)CrossRefGoogle Scholar
  2. 2.
    Conti, M., Das, S.K., Bisdikian, C., Kumar, M., Ni, L.M., Passarella, A., Roussos, G., Tröster, G., Tsudik, G., Zambonelli, F.: Looking ahead in pervasive computing: challenges and opportunities in the era of cyber-physical convergence. Pervasive Mob. Comput. 8(1), 2–21 (2012)CrossRefGoogle Scholar
  3. 3.
    Grossi, R., Gupta, A., Vitter, J.S.: High-order entropy-compressed text indexes. In: Proceedings of the 14th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 841–850, Philadelphia, PA, USA. Society for Industrial and Applied Mathematics (2003)Google Scholar
  4. 4.
    Howe, J.: Crowdsourcing: A definition. Tracking the Rise of the Amateur, Crowdsourcing (2006)Google Scholar
  5. 5.
    Howe, J.: The rise of crowdsourcing. Wired Mag. 14(6), 1–4 (2006)MathSciNetGoogle Scholar
  6. 6.
    Howe, J.: Crowdsourcing: How the Power of the Crowd is Driving the Future of Business. Random House, New York (2008)Google Scholar
  7. 7.
    King, S.F., Brown, P.: Fix my street or else: using the internet to voice local public service concerns. In: Proceedings of the 1st International Conference on Theory and Practice of Electronic Governance, pp. 72–80 (2007)Google Scholar
  8. 8.
    Kinoshita, A., Takasu, A., Adachi, J.: Traffic incident detection using probabilistic topic model. In: the Workshop Proceedings of the EDBT/ICDT 2014 Joint Conference, pp. 323–330 (2014)Google Scholar
  9. 9.
    Poovendran, R.: Cyber-physical systems: close encounters between two parallel worlds. Proc. IEEE 98(8), 1363–1366 (2010)CrossRefGoogle Scholar
  10. 10.
    Schuurman, D., Baccarne, B., De Marez, L., Mechant, P.: Smart ideas for smart cities: investigating crowdsourcing for generating and selecting ideas for ICT innovation in a city context. J. Theor. Appl. Electron. Commer. Res. 7(3), 49–62 (2012)CrossRefGoogle Scholar
  11. 11.
    Stembert, N., Mulder, I.J.: Love your city! an interactive platform empowering citizens to turn the public domain into a participatory domain. In: International Conference Using ICT, Social Media and Mobile Technologies to Foster Self-Organisation in Urban and Neighbourhood Governance (2013)Google Scholar
  12. 12.
    Tanaka, Y., Sjöbergh, J., Moiseets, P., Kuwahara, M., Imura, H., Yoshida, T.: Geospatial visual analytics of traffic and weather data for better winter road management. In: Cervone, G., Lin, J., Waters, N. (eds.) Data Mining for Geoinformatics, pp. 105–126. Springer, New York (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Kenro Aihara
    • 1
    • 2
    Email author
  • Piao Bin
    • 1
  • Hajime Imura
    • 3
  • Atsuhiro Takasu
    • 1
    • 2
  • Yuzuru Tanaka
    • 3
  1. 1.National Institute of InformaticsTokyoJapan
  2. 2.The Graduate University for Advanced StudiesHayamaJapan
  3. 3.Hokkaido UniversitySapporoJapan

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