Quality of Coverage: A Novel Approach to Coverage for Mobile Crowd Sensing Systems

Mobile Crowd Sensing (MCS) is an effective paradigm that utilizes the crowd as an extended instrument for the purpose of collecting data. However, utilizing the crowd comes with the risks stemming from the crowd's heterogeneity. Thus, the MCS administrator must carefully recruit and evaluate MCS participants for the reliable execution of MCS tasks. In this paper, we tackle some of the criteria required for the proper characterization of an Area of Interest $(AoI)$. We propose a coverage metric aimed at MCS systems that takes into consideration the global view of the AoI as a whole, as well as a local picture with regards to the subdivisions with the AoI. The developed coverage metric allows the MCS administrator to identify which regions within the AoI are lacking, in terms of quality, and how they can be compensated by moving participants from neighboring regions. We demonstrate the performance of the presented metric by means of a computer simulation.