Towards energy efficient mobile sensing

Mobile device based urban sensing, participatory sensing, and user activity recognition can provide rich contextual information for mobile applications such as social networking and location-based services. However, as the sensors on mobile devices consume significant amount of energy, the major bottleneck that restricts the continuous functioning of these mobile applications is the limited battery capacity on mobile devices.;In this thesis, we first present a novel design framework for an Energy Efficient Mobile Sensing System (EEMSS). EEMSS uses hierarchical sensor management strategy to recognize user states as well as to detect state transitions. By powering only a minimum set of sensors and managing sensors hierarchically, EEMSS significantly improves device battery life. We present the design, implementation, and evaluation of EEMSS that automatically recognizes a set of users' daily activities in real time using sensors on an off-the-shelf high-end smart phone. Evaluation of EEMSS with 10 users over one week shows that our approach increases the device battery life by more than 75% while maintaining both high accuracy and low latency in identifying transitions between end-user activities.;We then propose a computationally efficient algorithm to obtain the optimal sensor sampling policy under the assumption that the user state transition is Markovian. This Markov-optimal policy minimizes user state estimation error while satisfying a given energy consumption budget. The Markov-optimal policy is compared to uniform periodic sensing and performance improvement is obtained on both simulated and real user state traces, with approximately 20% average gain on empirically collected user data that pertains to user motion, inter-user contact status, and network connectivity.;Finally, we formulate the user state sensing problem as the intermittent sampling of a semi-Markov process, a model that provides more general and flexible capturing of realistic data. We propose (a) a semi-Markovian 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 is shown to significantly outperform Markovian algorithms on simulated two-state processes and real user state traces. In addition, we propose a novel client-server based energy efficient mobile sensing system architecture that automatically learns user dynamics and computes user-specific optimal sensing policy for mobile devices. A system prototype aiming to recognize basic human activity is implemented on Nokia N95 smartphones and desktop computers. We demonstrate the performance benchmark of u*s through a set of experiments, and show that it improves the estimation accuracy by 27.8% and 48.6% over Markov-optimal policy and uniform sampling, respectively.