a framework of energy efficient mobile sensing for automatic user state recognition
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, continuously capturing this contextual information on mobile devices consumes huge amount of energy. In this paper, we 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 using appropriate sensor duty cycles 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.
energy energy efficiency mobility mobile sensing senses user user recognition participatory sensing activation activity recognition contextual information mobile application social network location-based service mobile device device paper design systems eemss hierarchic sensor management strategies transit batteries implementation evaluation smart phone accuracy latency end-user  framework  real-time human state recognition users