Ensuring High-Quality Data Collection For Mobile Crowd Sensing

Mobile Crowd sensing is a new paradigm that encourages ordinary people to collect and share sensing data with their smart devices. However, the uncontrollable data quality is one of the critical problems that is potentially harmful to guarantee the availability and preciseness of mobile crowd sensing based services. In this paper, we propose a quality aware sensing data collection mechanism based on a realistic scenario where participants arrive and report their conditions sequentially one by one. When a participant arrives, the mechanism first forecasts the amount of high quality data he/she may contribute by employing the expectation of Binomial-Poisson distribution, and then a two-level iterative algorithm is employed to calculate its parametric values. After, our designed mechanism decides to select the participant or not by combining with his/her requested reward. Extensive simulation results well justify the effectiveness and robustness of our approach, compared with another schemes.