Comparing Synthetic Data Generation Tools for IoT Data

[This post is based on the Bachelor Information Sciences project of Darin Pavlov and reuses text from his thesis. The research is part of VU’s effort in the InterConnect project and was supervised by Roderick van der Weerdt]

The concepts and technologies behind the Internet of Things (IoT) make it possible to establish networks of interconnected smart devices. Such networks can produce large volumes of data transmitted through sensors and actuators. Machine Learning can play a key role in processing this data towards several use cases in specific domains automotive, healthcare, manufacturing, etc. However, access to data for developing and testing Machine Learning is often hindered due to sensitivity of data, privacy issues etc.

One solution for this problem is to use synthetic data, resembling as much as possible real data. In his study, Darin Pavlov conducted a set of experiments, investigating the effectiveness of synthetic IoT data generation by three different tools:

This table shows the results of one of the two Machine Learning detection tests showing how difficult it is to differentiate the synthetic data from the real one with a Machine Learning model. For two datasets, the result is calculated as 1 minus the average ROC AUC score

Darin compared the tools on various distinguishability metrics. He observed that Mostly AI outperforms the other two generators, although Gretel.ai shows similar satisfactory results on the statistical metrics. The output of SDV on the other hand is poor on all metrics. Through this study we aim to encourage future research within the quickly developing area of synthetic data generation in the context of IoT technology.

More details can be found in Darin’s thesis.

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