The work was performed by ECE PhD student Heng Zhang together with a then undergraduate student Michael Roth (now at Google). The collaborating industrial researcher is Rajesh Panta from AT&T Labs Research.

Mobile crowdsensing (MCS) is a technique by which sensor data about the physical environment is collected from a large group of individuals with mobile devices capable of computing and sensing. This data can be used to extract information that is of public interest, such as weather conditions, traffic information, and sound pollution. By leveraging the powerful sensing capacity and ubiquity of smartphones, MCS can provide information about our environment while lowering the cost of running data collection campaigns.

Saurabh Bagchi, Professor of Electrical and Computer Engineering and Computer Science (by courtesy) and PI on the project, said “Low coverage of the physical area of interest and high energy consumption on mobile devices are two of the main challenges in MCS. We address these in our work. Further, in this paper, we discuss a third factor, scheduling fairness, which is correlated with the other two factors and has a significant impact on the success of MCS.”

Fairness in MCS refers to how equitably the overall task load is divided among all the participating devices. In the short term, unfairness will deplete the device energy of some users who frequently receive tasks and cause them to leave MCS campaign. In the long term it will harm task coverage because of fewer participants. To understand if the scheduling fairness affects MCS users’ willingness to participate, the team surveyed 96 individuals from 11 countries. The survey found that all three payment models commonly used for MCS, fairness is an important factor to increase participation.