Affiliation:
1. University of Houston, TX, Houston
2. McMaster University, Canada
3. Microsoft Research, WA
Abstract
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 nonparametric, profiling-free, yet lightweight and energy-efficient inference framework, to identify recurring and new places that mobile users visit indoor. Combining WiFi scans and accelerometer readings, GreenLocs can accurately decide a new place and a revisited place with just a few radio signal strength (RSS) samples. GreenLocs 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 the system. GreenLocs is shown to be robust to signal variations and missing data through experimental evaluations using traces collected from mobile phones of different brands/models.
Funder
US NSF CNS-0905556, CNS-0910461, CNS-0953377, ECCS-1028782, CNS-0832089, and CNS-1117560
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
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