Trajectory-Based Task Allocation for Reliable Mobile Crowd Sensing Systems

Mobile crowd sensing (MCS) is as a promising people-centric sensing paradigm which allows ordinary citizens to contribute sensing data using mobile communication devices. In this paper we study correlation between users' mobility and their role as contributors in MCS applications. We propose a new trajectory-based approach for task allocation in MCS environments and model participants' spatio-temporal competences by analyzing their mobile traces. By allocating MCS tasks only to participant who are familiar with the target location we significantly increase the reliability of contributed data and reduce total communication cost. We introduce novel metric to estimate participants' competence to conduct MCS tasks and propose fair ranking approach allowing newcomers to compete with experienced senior contributors. Additionally, we group similar expert contributors and thus open up new possibilities for physical collaboration between them. We evaluate our work using GeoLife trajectory dataset and the experimental results show the advantages of our approach.