Robust estimation based crowd sensing enhancement for iBeacon map construction

For crowd sensing applications, the inaccuracy and conflict of the reported samples, commonly caused by untrained participants and heterogeneous mobile devices, have received more and more attention. Given this, constructing a high-quality radio map of iBeacons via crowd sensing is very challenging. In this paper, we resort to the family of robust estimation and design a robust radio map construction scheme, named by RrMCS, to enhance the quality of iBeacon map construction via crowd sensing. For heterogeneous devices, we use a linear robust estimation technique to map the signal strength scanned by different users into a uniform space. Moreover, we alleviate the negative influence of outliers and detect the abnormal participants by utilizing the multivariate robust statistics on the signal information uploaded by different users. Finally, we implement an Android-based system for constructing the iBeacon radio map and conduct extensive experiments under indoor environment. The experimental results validate the enhancement of the constructed iBeacon radio map by the proposed RrMCS.