Exploring the Use of Ambient WiFi Signals to Find Vacant Houses

  • Shin’ichi Konomi
  • Tomoyo Sasao
  • Simo Hosio
  • Kaoru Sezaki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10217)

Abstract

In many countries, the population is either declining or rapidly concentrating in big cities, which causes problems in the form of vacant houses in many local communities. It is often challenging to keep track of the locations and the conditions of vacant houses, and for example in Japan, costly manual field studies are employed to map the occupancy situation. In this paper, we propose a technique to infer the locations of occupied houses based on ambient WiFi signals. Our technique collects RSSI (Received Signal Strength Indicator) data based on opportunistic smartphone sensing, constructs hybrid networks of WiFi access points, and analyzes their geospatial patterns based on statistical shape modeling. We show that the technique can successfully infer occupied houses in a suburban residential community, and argue that it can substantially reduce the cost of field surveys to find vacant houses as the number of potential houses to be inspected decreases.

Keywords

Ambient WiFi signals Vacant houses Civic computing Localization 

References

  1. 1.
    Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: Proceedings IEEE INFOCOM 2000, pp. 775–784 (2000)Google Scholar
  2. 2.
    Chi, G., Liu, Y., Wu, H.: “Ghost Cities” analysis based on positioning data in China (2014). arXiv:1510.08505
  3. 3.
    Ji, M., Kim, J., Cho, Y., Lee, Y., Park, S.: A novel Wi-Fi AP localization method using Monte Carlo path-loss model fitting simulation. In: Proceedings IEEE PIMRC, pp. 3487–3491 (2013)Google Scholar
  4. 4.
    Koo, J., Cha, H.: Unsupervised locating of WiFi access points using smartphones. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(6), 1341–1353 (2012)CrossRefGoogle Scholar
  5. 5.
    LaMarca, A., et al.: Place lab: device positioning using radio beacons in the wild. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) Pervasive 2005. LNCS, vol. 3468, pp. 116–133. Springer, Heidelberg (2005). doi:10.1007/11428572_8 CrossRefGoogle Scholar
  6. 6.
    Nomura Research Institute: News release, 7 June 2016. http://www.nri.com/Home/jp/news/2016/160607_1.aspx. (in Japanese)
  7. 7.
    Wigle.net (2017). https://wigle.net/. Accessed 3 Jan 2017
  8. 8.
    Wu, D., Liu, Q., Zhang, Y., McCann, J., Regan, A., Venkatasubramanian, N.: CrowdWiFi: efficient crowdsensing of roadside WiFi networks. In: Proceedings International Middleware Conference, pp. 229–240 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shin’ichi Konomi
    • 1
  • Tomoyo Sasao
    • 1
  • Simo Hosio
    • 2
  • Kaoru Sezaki
    • 1
  1. 1.Center for Spatial Information ScienceThe University of TokyoKashiwaJapan
  2. 2.Center for Ubiquitous ComputingThe University of OuluOuluFinland

Personalised recommendations