Data Trustworthiness for UWB Ranging in IoT.

The computation of data trustworthiness during double-sided two-way-ranging with ultra-wideband signals between IoT devices is proposed. It relies on machine learning based ranging error correction, in which the certainty of the correction value is used to quantity trustworthiness. In particular, the trustworthiness score and error correction value are calculated from channel impulse response measurements, either using a modified k-nearest neighbor (KNN) or a modified random forest (RF) algorithm. The proposed scheme is easily implemented using commercial ultra-wideband transceivers and it enables real time surveillance of malicious or unintended modification of the propagation channel. The results on experimental data show an improvement of 47% RMSE on the test set when only trustworthy measurements are considered.