Semi-Markov state estimation and policy optimization for energy efficient mobile sensing

User/environmental context detection on mobile devices benefits end-users by providing information support to various kinds of applications. A pervasive question, however, is how the sensors on the mobile device should be sampled energy efficiently without sacrificing too much detection accuracy. In this paper, we formulate the user state sensing problem as the intermittent sampling of a semi-Markov process, a model that provides general and flexible capturing of realistic data with any type of state sojourn distributions. We propose (a) a semi-Markov state estimation mechanism that selects the most likely user state while observations are missing, and (b) a semi-Markov optimal sensing policy u_s* which minimizes the expected state estimation error while maintaining a given energy budget. Their performance are shown to significantly outperform Markov algorithms on simulated two-state processes and real user state traces pertaining to different types of state distributions. Finally, in order to evaluate the performance of u_s*, we implement a client-server based basic human activity recognition system on N95 smartphones and desktops which automatically computes user-specific optimal sensing policy based on historically collected data. We show that u_s* improves the estimation accuracy by 27.8% and 48.6% respectively over Markov-optimal policy and uniform sampling through a set of experiments.