Mobility Coordination of Participants in Mobile CrowdSensing Platforms with Spatio-Temporal Tasks

With the increased adoption of Mobile CrowdSensing platforms in urban environments, the mobility of participating self-motivated crowds already roaming in the field can be coordinated to assist in completing spatio-temporal sensing tasks. In this work, we propose to utilize the mobility predictability within such platforms, coupled with the availability of spatio-temporal tasks on the mobility field, to recommend routes for participants in real-time. In this paper, we define the graph-theoretic measure of Temporal Coverage Centrality, as well as adopt techniques from distributed systems to define three online route recommendation mechanisms, which can be used to coordinate the mobility of participants within MCS platforms to optimize system performance. Moreover, we empirically evaluate the efficiency of the proposed mechanisms with an insight on their benefits.