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.
| Original language | English |
|---|---|
| Title of host publication | Ambient Intelligence - 13th European Conference, AmI 2017, Proceedings |
| Editors | Reiner Wichert, Andreas Braun, Antonio Mana |
| Publisher | Springer Verlag |
| Pages | 130-135 |
| Number of pages | 6 |
| ISBN (Print) | 9783319569963 |
| DOIs | |
| Publication status | Published - Jan 1 2017 |
| Event | 13th European Conference on Ambient Intelligence, AmI 2017 - Malaga, Spain Duration: Apr 26 2017 → Apr 28 2017 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10217 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 13th European Conference on Ambient Intelligence, AmI 2017 |
|---|---|
| Country | Spain |
| City | Malaga |
| Period | 4/26/17 → 4/28/17 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
Exploring the use of ambient WiFi signals to find vacant houses. / Konomi, Shinichi; Sasao, Tomoyo; Hosio, Simo; Sezaki, Kaoru.
Ambient Intelligence - 13th European Conference, AmI 2017, Proceedings. ed. / Reiner Wichert; Andreas Braun; Antonio Mana. Springer Verlag, 2017. p. 130-135 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10217 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Exploring the use of ambient WiFi signals to find vacant houses
AU - Konomi, Shinichi
AU - Sasao, Tomoyo
AU - Hosio, Simo
AU - Sezaki, Kaoru
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85017622330&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85017622330&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-56997-0_10
DO - 10.1007/978-3-319-56997-0_10
M3 - Conference contribution
AN - SCOPUS:85017622330
SN - 9783319569963
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 130
EP - 135
BT - Ambient Intelligence - 13th European Conference, AmI 2017, Proceedings
A2 - Wichert, Reiner
A2 - Braun, Andreas
A2 - Mana, Antonio
PB - Springer Verlag
ER -