Using spatial interpolation in the design of a coverage metric for Mobile CrowdSensing systems

Mobile Crowd Sensing (MCS) is an emerging paradigm that exploits the ubiquity of smartphones and cheap sensor devices to collect data and thus contribute to the provision of useful services, especially in the domains of urban life. While many MCS implementations have been proposed for different applications, the lack of common performance metrics means that their efficiency cannot be easily compared. In this paper, we formalize a generic coverage model for the class of MCS systems sampling spatial phenomena before introducing a way to produce one such a metric by exploiting a spatio-temporal estimator. We avail of a large-scale dataset of users' mobility traces to demonstrate the use of the newly introduced metric in informing the resolution of a typical problem in MCS system design.