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.
|Number of pages||11|
|Journal||Journal of Ambient Intelligence and Humanized Computing|
|Publication status||Published - Feb 14 2019|
All Science Journal Classification (ASJC) codes
- Computer Science(all)