Greenlocs: An energy-efficient indoor place identification framework

Understanding indoor mobility patterns of people is important in applications such as targeted advertisement, microclimate control, and delivery of anticipatory notifications. In this article, we devise GreenLocs, a profiling-free, yet lightweight and energy-efficient inference framework, to identify recurring new places that mobile users visit indoor. Combining WiFi scans and accelerometer readings, GreenLocs accurately decide a new place and a revisited place with just a few radio signal strength (RSS) samples. consists of three major building blocks, namely, missing data handling algorithms, a nonparametric Bayesian inference model, and a stopping rule, which significantly increases the energy efficiency of system. GreenLocs is shown to be robust to signal variations and missing data through experimental using traces collected from mobile phones of different brands/models.