Quantifying Worker Reliability for Crowdsensing Applications: Robust Feedback Rating and Convergence

Worker reliability estimation is a fundamental problem in crowdsensing applications. This paper proposes a robust feedback rating approach to estimate worker reliability explicitly. In this approach, the requester provides a feedback rating to reflect the quality of the sensor data submitted by each worker. The aggregation of each worker's historical feedback ratings serves as a reliability estimate. The challenges are: (1) Feedback ratings are subjected to noise; (2) Workers’ cognitive bias in task selection leads to sensor data quality variations. We develop a mathematical model to quantify rating noise from requesters and the degree of cognitive bias of workers in task selection. We derive sufficient conditions, under which the aggregate rating is asymptotically accurate in estimating worker reliability, via stochastic approximation techniques. These conditions identify a class of asymptotically accurate rating aggregation rules for crowdsensing applications. We further derive the minimum number of ratings needed to guarantee a given reliability estimation accuracy, via martingale theory. Via extensive experiments: (1) We reveal fundamental understandings on how various factors such as rating noise influence the minimum number of ratings needed to achieve certain accuracy; (2) We show that our feedback rating approach improves air quality index estimation accuracy by as high as 50 percent over the a typical baseline algorithm.