Mobile Crowdsensing and Crowdsourcing

Mobile crowdsensing and crowdsourcing recruit large numbers of ordinary people—often through platforms like Amazon Mechanical Turk or via the sensors built into their smartphones—to collect data, complete tasks, or label information at a scale and cost that traditional methods cannot match. Because contributors vary widely in attention, expertise, and motivation, researchers spend considerable effort on data quality: designing truth-discovery algorithms that reconcile conflicting reports and building incentive mechanisms that keep participants engaged without distorting their behavior. The approach has found use across domains from urban traffic monitoring and environmental sensing to behavioral experiments that once required a physical laboratory. Open questions center on how to fairly compensate workers in these online labor markets, how to protect contributor privacy when sensor data is continuously collected, and how to maintain reliable output as task complexity grows beyond simple annotation into richer, more subjective judgments.

Works

23,800

Total citations

351,713

Keywords

CrowdsourcingMechanical TurkMobile SensingData QualityIncentive MechanismsOnline Labor Markets