Selective Sampling Strategies to Conserve Power in Context Aware Devices

We analyze the use of selective sampling strategies to aid in power conservation in sensor platforms for context-aware systems. In particular, we study an activity-aware system based on the eWatch sensor and notification platform, developed at CMU. We collected 94 hours of self-annotated activity data from four subjects over several days each. We compare sampling strategies according to several metrics, each of which satisfies a different set of application needs. These metrics include: accuracy as the percentage of time between samples that sampled activity matches true activity, average latency of detecting a change in activity, the percentage of missed activities, and the percentage of redundant samples. We consider both the performance differences between strategies as well as differences between subjects. Accuracies of over 95% were achievable using only 3% of the samples.