Wasserstein GAN-Based Digital Twin-Inspired Model for Early Drift Fault Detection in Wireless Sensor Networks

Abstract

In this Internet of Things (IoT) era, the number of devices capable of sensing their surroundings is increasing day by day. Based on the data from these devices, numerous services and systems are now offered where critical decisions depend on the data collected by sensors. Therefore, error-free data are most desirable, but due to extreme operating environments, the possibility of faults occurring in sensors is high. So, detecting faults in data obtained by sensors is important. In this article, a digital twin (DT)-inspired detection approach is proposed, and its ability to detect a single type of fault in several sensors is analyzed. The digital equivalent of the sensor is developed using a generative adversarial network (GAN). As GANs inherently perform well with images, Gramian angular field (GAF) encoding is used to convert time series data to images. The GAF encoding preserves the temporal relations of the time series data. The GAN is trained with the GAF images. The trained GAN model acts as the virtual representation of the sensor, and the discriminator network of the GAN model, once it has learned the pattern of normal data, is used as the fault detector. The performance of the virtual sensor is promising because it successfully generates data for normal conditions. The best fault detection accuracy achieved by the proposed model is 98.7%, which makes this GAN-based DT-inspired approach a promising candidate for sensor fault detection.