  Various approaches based on supervised or unsupervised machine learning (ML)
have been proposed for evaluating IoT data trust. However, assessing their
real-world efficacy is hard mainly due to the lack of related
publicly-available datasets that can be used for benchmarking. Since obtaining
such datasets is challenging, we propose a data synthesis method, called random
walk infilling (RWI), to augment IoT time-series datasets by synthesizing
untrustworthy data from existing trustworthy data. Thus, RWI enables us to
create labeled datasets that can be used to develop and validate ML models for
IoT data trust evaluation. We also extract new features from IoT time-series
sensor data that effectively capture its auto-correlation as well as its
cross-correlation with the data of the neighboring (peer) sensors. These
features can be used to learn ML models for recognizing the trustworthiness of
IoT sensor data. Equipped with our synthesized ground-truth-labeled datasets
and informative correlation-based feature, we conduct extensive experiments to
critically examine various approaches to evaluating IoT data trust via ML. The
results reveal that commonly used ML-based approaches to IoT data trust
evaluation, which rely on unsupervised cluster analysis to assign trust labels
to unlabeled data, perform poorly. This poor performance can be attributed to
the underlying unsubstantiated assumption that clustering provides reliable
labels for data trust, a premise that is found to be untenable. The results
also show that the ML models learned from datasets augmented via RWI while
using the proposed features generalize well to unseen data and outperform
existing related approaches. Moreover, we observe that a semi-supervised ML
approach that requires only about 10% of the data labeled offers competitive
performance while being practically more appealing compared to the
fully-supervised approaches.
