Sequential IoT Data Augmentation using Generative Adversarial Networks
This work addresses the small data challenge in machine learning for industrial IoT applications, but it is incremental as it applies existing GAN methods to a new domain.
The paper tackled the problem of generating sequential IoT data from limited ground truth by using Generative Adversarial Networks (GANs), with an example showing subjectively similar household energy consumption data and introducing a quantitative evaluation technique that supports the feasibility of this approach.
Sequential data in industrial applications can be used to train and evaluate machine learning models (e.g. classifiers). Since gathering representative amounts of data is difficult and time consuming, there is an incentive to generate it from a small ground truth. Data augmentation is a common method to generate more data through a priori knowledge with one specific method, so called generative adversarial networks (GANs), enabling data generation from noise. This paper investigates the possibility of using GANs in order to augment sequential Internet of Things (IoT) data, with an example implementation that generates household energy consumption data with and without swimming pools. The results of the example implementation seem subjectively similar to the original data. Additionally to this subjective evaluation, the paper also introduces a quantitative evaluation technique for GANs if labels are provided. The positive results from the evaluation support the initial assumption that generating sequential data from a small ground truth is possible. This means that tedious data acquisition of sequential data can be shortened. In the future, the results of this paper may be included as a tool in machine learning, tackling the small data challenge.