Data Trustworthiness for UWB Ranging in IoT

Abstract

UWB is one of the main technologies for localization in IoT applications. For range-based localization, it is crucial to secure UWB ranging by a suitable mechanism. Thereby, trustworthiness measures appear to be specifically attractive for constraints posed by IoT applications. In this work, a measure for data trustworthiness of the double-sided two-way-ranging estimate is proposed. The measure relies on features obtained from the channel impulse response and applies two machine learning techniques, namely a modified k nearest neighbour and a modified random forest, to infer an error correction term together with a trust value. To increase the number of trusted measurements, a more accurate stepwise labeling of the training data is used, and an optimum combination scheme of the resulting stepwise trust values is proposed. The results on experimental data show an improvement of 34% RMSE on the test set with 61% of the measurements considered trustworthy.