Exploiting human state information to improve GPS sampling

A large collection of mobile sensing applications depend on the knowledge of the user´s whereabouts and are heavily based on GPS location measurements. Although knowledge of location is very desirable, in many mobile applications excessive GPS sampling is very energy taxing thus posing a barrier to application sustainability. To mitigate this problem, in this paper we examine how to reduce GPS sensing redundancies by extracting the state of a person and using it to drive GPS sampling on mobile phones. Using a GPS dataset we first describe how to extract the spatio-temporal states of the user. We then use the knowledge of the user´s state to reduce GPS sampling rate, helping to make mobile applications more sustainable.