Exploring the use of ambient WiFi signals to find vacant houses

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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

3 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルAmbient Intelligence - 13th European Conference, AmI 2017, Proceedings
編集者Reiner Wichert, Andreas Braun, Antonio Mana
出版社Springer Verlag
ページ130-135
ページ数6
ISBN(印刷版)9783319569963
DOI
出版ステータス出版済み - 1 1 2017
外部発表はい
イベント13th European Conference on Ambient Intelligence, AmI 2017 - Malaga, スペイン
継続期間: 4 26 20174 28 2017

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10217 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他13th European Conference on Ambient Intelligence, AmI 2017
国/地域スペイン
City Malaga
Period4/26/174/28/17

All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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