Indoor localization in home environments using appearance frequency information

Recognizing the location of an individual in a home environment is crucial in order to enable various context-aware home applications such as elderly health monitoring and in home appliance automation. However, due to the limited number of dedicated Wi-Fi access points (APs), it is challenging to guarantee the reliable localization performance in a home environment by using the traditional Wi-Fi fingerprinting (WF) technique. In this paper, we propose a room-level localization system for the typical residential home environments which comprise of a living room, a kitchen, a bathroom, and a bedroom. Specifically, we make use of appearance frequency (AF) information of APs at each location in order to narrow down the number of candidate locations before performing the Wi-Fi Fingerprinting scheme. Our system improves the localization performance by up to 17.5 % (11.29 % on average) over that of the traditional WF-based approach which does not exploit AF information. We achieved the room-level positioning accuracy of 84.76% on the dataset of 6 home environments.