Exploiting human state information to improve GPS sampling

Abstract

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.