Semi-Markovian State Estimation and Policy Optimization for Energy Efficient Mobile Sensing
User context monitoring on mobile devices benefits end-users by providing information support to various kinds of mobile applications. A pervasive question, however, is how the sensors on the mobile device could be scheduled energy efficiently without sacrificing too much detection accuracy. In this paper, the authors 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. Their performance are shown to significantly outperform Markovian algorithms on simulated two-state processes and real user state traces.