Location-based Social Networks Data for Mobile Crowdsensing

Participatory Sensing (PS) can be seen as a computational task that requires crowds of people in order to have success. This task relies on the willingness of mobile users to participate in the collection and reporting of data using a variety of sensors either embedded or integrated into their cellular phones. Several mobility models have been proposed to model the participant's mobility patterns, however, recent research shows that peoples movement behaviors may be affected by social interaction. In this preliminary work, we compare the performance of a system that uses real location data from Brightkite a real Location Base Social Network (LBSN), and a system that uses random walk as a mobility model. A Reverse Auction based Dynamic Price (RADP) mechanism is used as incentive mechanism for participatory sensing. Through extensive simulations, we evaluate the performance of both systems in terms of area coverage, and the active number of participants, both important metric in the evaluation of PS systems.