Holistic approach to energy efficiency in context-aware mobile sensing

Abstract

Mobile devices and sensors have limited battery lifespan thus limiting their feasibility in context recognition applications. As a result, there is a need to provide mechanisms for energy-efficient operation of sensors in settings where multiple contexts are monitored simultaneously. Past methods for efficient sensing operation have been hierarchical by first selecting the sensors with least energy consumption, then devising individual sensing schedules that trade off energy and delays. The main limitation of the hierarchical approach is that it does not consider the combined impact of sensor scheduling and sensor selection. This paper aims at addressing this limitation by considering the problem holistically and devising an optimization formulation that can simultaneously select the group of sensors while also considering the impact of their triggering schedule. The optimization solution is framed as a Viterbi algorithm and providing mathematical representations for multi-sensor reward function, the user's behavior. Experiment results showed an average improvement of 60percent compared to the state of the art hierarchical approach.