Adaptive passive mobile sensing using reinforcement learning

Continuous passive sensing using smartphone embedded sensors can drain the battery quickly, interrupting other usages of the device. In order to improve the energy efficiency in continuous mobile sensing applications, we propose a new adaptive sensing framework using reinforcement learning to optimize the sensing timing. We model our adaptive sensing problem as a Markov Decision Process and dynamically change the sensing timing of targeted sensor(s) so that they are only operating in desired contexts (e.g. collect accelerometer data only when the phone is moving). Using accelerometer data continuously collected from 220 participants for over two weeks, we show that our approach is able to save the energy while attaining high accuracy and data coverage. Specifically, our strategy attains energy saving of 62.4 % at an accuracy of 80.9% and data coverage of 67.4%, which outperforms two baseline strategies, including a random strategy and a strategy using learning automata technique.