Smartphones enable the collection of mobility data using various sensors. The key challenge in the collection of continuous data is to overcome the limited battery capacity of the device. While extensive research has been conducted to solve energy issues in continuous mobility learning, we argue that previous works have not reached optimal performance. In this paper, we propose an energy-efficient mobility monitoring system, FreeTrack, to collect place-centric mobility data with minimum energy consumption in everyday life. We first analyzed the regularity of life patterns, cellular connection patterns, and battery charging behaviors of 94 smartphone users to examine important features related to human mobility. Based on our findings, we design an adaptive duty cycling scheme that uses zero-cost information (i.e., regular mobility, cell connection, and battery state) as low-level sensing to infer location change without the need to activate sensors. We model the location inference on the Hidden Markov Model and optimize the sensing schedule of individual smartphones for real-time operation. Our extensive experiment with 48 smartphone users shows that the proposed system achieves an energy saving of about 68% over previous works, yet still correctly traces 97% of mobility with (0.2±0.5) places misses in a day.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Electrical and Electronic Engineering