Mobile Crowdsensing and Crowdsourcing

Mobile crowdsensing and crowdsourcing research examines how large numbers of ordinary people, equipped with smartphones or connected through online platforms like Amazon Mechanical Turk, can collectively gather data and complete tasks at a scale no single organization could manage alone. The central challenge is that contributions vary widely in reliability, so a significant portion of the work focuses on how to design incentive structures that attract genuine effort and how to algorithmically separate accurate signal from noise across thousands of submissions. Researchers are also investigating how sensor-equipped mobile devices can passively or actively collect environmental, behavioral, and geographic data in ways that are both privacy-preserving and scientifically valid. Open questions include how to sustain fair, long-term participation in these systems and how findings from controlled crowdsourcing experiments translate to real-world deployments where worker motivation and data conditions are far less predictable.

Works

22,708

Total citations

346,441

Keywords

CrowdsourcingMechanical TurkMobile SensingData QualityIncentive MechanismsOnline Labor Markets