Monitoring the status of iBeacons with crowd sensing

Since Apple introduced the iBeacons in Worldwide Developers Conference (WWDC) 2013, the iBeacon has been rapidly accepted and generalized in the market. For the deployed iBeacons, it is necessary to monitor their status. In this paper, we design a crowd sensing based monitoring framework which combines the moving and static schemas of participants to monitor the real status of iBeacons. In such a system, the inaccuracy and conflict of the collected signal information, commonly caused by the error rate of participants or the differences of sensing context, have received more and more attention. Estimating the real status of iBeacons according to the uploaded signal information becomes a big challenge for our monitoring system. Towards this end, we propose a context-aware estimation approach in this paper. We first model the effects of sensing context, and then propose an iterative method to infer the error rate of participants and estimate the real status of iBeacons with high precision. Our method is tested via extensive simulations, and verified by our monitoring system which has been applied in the teaching building. The results demonstrate that the proposed estimation approach outperforms recent popular three-estimates algorithm and OtO EM algorithm. At last, we develop the review mechanism, which ensures the efficiency of our monitoring system.