Section: New Results
Adaptive sampling frequency of human mobility
Participants : Panagiota Katsikouli, Aline Carneiro Viana, Marco Fiore [CNR - IEIIT (Italy)] , Diego Madariaga.
The problem we address here is the design of a location sampling system for smartphones and handheld devices that reduces the energy consumed by the continuous activation of the GPS, it reduces the space required to store recorded locations, while reliably capturing the movements of the tracked user. The applications here are related to a number of fields relevant to ubiquitous computing, such as energy-efficient mobile computing, location-based service operations, active probing of subscribers' positions in mobile networks and trajectory data compression.
To this end, we propose an adaptive sampling system without the use of any assisting sensors for the activation of GPS, such as accelerometer, or GSM information. Our system captures the mobility of a user with high accuracy and reliably adjusts the sampling frequency depending on the user’s movement. During high mobility, our system densely samples the locations of the tracked user, but at a rate at most the usual rate found today in most applications (e.g., 1 sample per minute). During low mobility, we sample sparsely at much lower rate than usual. As a result, the recorded trace contains much less samples than it would contain if we sampled with the fixed pre-defined sampling rate, requiring less storage space and less energy to activate the GPS.
Our first quest for a response led to the discovery of (i) seemingly universal spectral properties of human mobility, and (ii) a linear scaling law of the localization error with respect to the sampling interval. Our findings were based on the analysis of fine-grained GPS trajectories of 119 users worldwide. This work was published at the IEEE Globecom 2017 international conference.
We have improved the published sampling approach by incorporating human behavioral features at the sampling decisions to make it more adaptive. This is an on-going work with Panagiota Katsikouli, who spent 5 months in our team working as an internship and is currently doing a Post-Doc at the AGORA Inria team, and Diego Madariaga who spent 3 months in our team working as an internship and is going to start a PhD in co-tutelle with Aline C. Viana. Diego has implemented an Android application to sample mobility data of users according to our adaptive system described here above. The application is currently under deployment and 8 volunteers are running it in their smartphones. The collected data will allow us validating the correctness and performance of our adaptive sampling system. A patent discussion is also on-going with Inria, currently performing a marked/business study.