Abstract
In many countries, the population is either declining or rapidly concentrating in big cities, which causes problems in the form of vacant houses. 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 discuss a technique to infer the locations of occupied and vacant houses based on ambient WiFi signals. Our technique collects Received Signal Strength Indicator (RSSI) data based on opportunistic smartphone sensing, constructs hybrid networks of WiFi access points, and analyzes their geospatial patterns based on statistical shape modeling. In situ experiments in two residential neighborhoods show that the proposed technique can successfully detect occupied houses and substantially outperform a simple triangulation-based method in one of the neighborhoods. We also argue that the proposed technique can significantly reduce the cost of field surveys to find vacant houses as the number of potential houses to be inspected decreases.
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Acknowledgements
We thank Ryohei Suzuki, the members of the Urban Housing Policy Division of Kashiwa City, and the members of the local communities for providing valuable feedback at different stages of this project.
Funding
This work was supported by JSPS KAKENHI Grant numbers JP17KTT0154, JP17K00117, JST CREST Grant number JPMJCR1411, and MLIT Pioneering Countermeasure Models for Vacant Houses Project, Japan, and Academy of Finland grant 286386-CPDSS.
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Konomi, S., Sasao, T., Hosio, S. et al. Using ambient WiFi signals to find occupied and vacant houses in local communities. J Ambient Intell Human Comput 10, 779–789 (2019). https://doi.org/10.1007/s12652-018-0899-8
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DOI: https://doi.org/10.1007/s12652-018-0899-8