Using micro-climate sensing to enhance RF localization in assisted living environments

In this paper, we propose micro-climate sensing as an effective means of enhancing conventional RF-based localization. Our system targets people-tracking applications in dynamic indoor environments, such as nursing homes, hospitals and office spaces that require simple deployment and where conventional RF-based tracking using signal strengths alone is very likely to suffer from time-varying signal attenuation and inevitable changes in the environment over time such as new furniture arrangements, people traffic, changing obstacle patterns etc. To help mitigate these effects, we use time-synchronized windows of sensor samples to dynamically associate a mobile node with its nearest beacon nodes. Comparisons and matches are always relative to the ambient attributes at the time of localization, and hence our technique automatically evolves with environmental changes. We consider this property of localization techniques to be a significant contribution and a necessary requirement for any long- lived localization system. In assisted-living environments, sensor networks likely already have basic sensors to collect contextual information about users and to monitor the environment. We propose using light, humidity, temperature and audio data samples over a short window of time to model the micro-climate of a beacon node. Using microclimate matching in conjunction with RF signal strength decreases the worst-case localization error significantly by a factor of more than 3 (from 25 m to 8 m) while making the system more resilient to environment changes. Microclimate data helps ensure at least room level location tracking even in buildings like hospitals with many rooms in close proximity.