Mobile devices and sensors have limited battery lifespans, limiting their feasibility for context recognition applications. As a result, there is a need to provide mechanisms for energyefficient 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 the least energy consumption, and 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. We aimed 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 that includes mathematical representations for multi-sensor reward functions and modeling of user behavior. Experiment results showed an average improvement of 31% compared to a hierarchical approach.
An illustration of the triggering decision interval, sensing schedules, and synchronization procedure of 2 sensor groups used to recognize different contexts while having a common sensor.
System description. The inputs are the user’s current context states, the behavior model, and a context recognition knowledge base (CRM KB). The outputs are the selected sensors and the sensing schedules for each context. The notation presented is described in Section 3.1.
The context recognition model knowledge base (CRM KB), containing the information relevant to context recognition: (1) context; (2) sensors and specifications; (3) recognition model; (4) the associations of the three together to recognize a context.
