Integrity of Data in a Mobile Crowdsensing Campaign

Abstract

Mobile crowdsensing (MCS) has a huge potential to provide societal benefits by effectively utilizing the sensing, computing, and networking capability of mobile devices, which have become ubiquitous especially in the developed world. However, many challenges come along with MCS such as energy cost, privacy issues, and the data integrity of crowdsensed data. In this paper, we focus on analyzing the data integrity of mobile crowdsensed data in a user study of 60 people who participate in a crowdsensing campaign that collects barometric pressure data from various locations of our campus. Each set of 20 users runs one of the three different crowdsensing frameworks, one of which, called SENSE-AID, is an energy-efficient framework designed by us. We analyze the characteristics of the data with respect to their integrity. From our analysis, we find that for 90 MCS tasks there is a surprisingly high number of outlier percentage (close to 20%), only about 10% of the participants report data for the entire duration of 7 days of the MCS campaign, and using a reputation system to filter out spurious data values helps in getting within 0.8% of the ground truth barometric pressure, whereas not using the reputation system leads to a discrepancy of about 20%. We hope our analysis will provide a useful reference to MCS researchers and developers in their future work running MCS campaigns.