Mobile Crowdsensing and Crowdsourcing

Mobile crowdsensing and crowdsourcing treat the collective activity of networked individuals — their smartphones, their labor, their attention — as a scientific instrument for gathering data at scales no single research team could achieve alone. Platforms like Amazon Mechanical Turk have made it possible to recruit thousands of participants for behavioral experiments or annotation tasks within hours, while participatory sensing turns everyday devices into distributed sensors that map noise, air quality, or traffic in real time. The central technical and social challenges are ensuring that the resulting data is trustworthy — since contributors vary in skill, honesty, and motivation — and designing incentive mechanisms that sustain participation without distorting the behavior being measured. Active research is pushing toward better algorithms for reconciling conflicting reports from many sources, as well as a more rigorous understanding of how worker demographics and payment structures shape what online labor markets actually capture about human behavior.

Works

23,115

Total citations

349,028

Keywords

CrowdsourcingMechanical TurkMobile SensingData QualityIncentive MechanismsOnline Labor Markets