Exploring the use of ambient WiFi signals to find vacant houses

Shin’Ichi Konomi, Tomoyo Sasao, Simo Hosio, Kaoru Sezaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAmbient Intelligence - 13th European Conference, AmI 2017, Proceedings
EditorsReiner Wichert, Andreas Braun, Antonio Mana
PublisherSpringer Verlag
Number of pages6
ISBN (Print)9783319569963
Publication statusPublished - Jan 1 2017
Externally publishedYes
Event13th European Conference on Ambient Intelligence, AmI 2017 - Malaga, Spain
Duration: Apr 26 2017Apr 28 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10217 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other13th European Conference on Ambient Intelligence, AmI 2017
City Malaga

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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