Robust Localization with Crowd Sensors: A Data Cleansing Approach

Abstract

In this paper, the source localization problem with crowd of anchor nodes is investigated, under the circumstances that abnormal data could be sporadically and randomly produced for the reason of either accidental equipment failures or random malicious behaviors. To cope with the problem that abnormal data brings, we formulate a generalized modeling of abnormal data in localization problem, which involves the impacts of both unexpected equipment failures and malicious data falsifications. The corresponding Cramer-Rao lower bound (CRLB) of the specific localization problem is derived. For the localization enhancement, we propose a data cleansing-based robust localization algorithm which exploits the low occupancy of channel band by sources and the sparsity of abnormal data. The data cleansing approach achieves both the new sensing data matrix that cleansed out abnormal data component and the estimated abnormal data matrix, which are respectively used for the correct source detection process and the position estimation process of the final source localization. The root mean squared error (RMSE) is derived to assess the performance of the proposed robust localization algorithm. Computer simulations show that the proposed data cleansing-based robust localization algorithm can effectively eliminate the impairment of the abnormal data and hence improve the localization performance evidently.