IoT-Based Monitoring of Temperature and Humidity Using Infrared Thermography for Cryptocurrency Mining Room

Abstract

Nowadays, as modern cryptocurrency machines operate faster and hotter, the fans are insufficient to cool constant CPU and GPU usage. The researchers built specialized Mining Rig Cases for these machines to withstand excessive heat. Aside from temperature, another environmental factor that affects the performance of Cryptocurrency machines is relative humidity. The researchers created an IoT-based Monitoring system to regulate and monitor the Cryptocurrency machines as excessive heat and humidity can decrease the lifespan of these machines. The Infrared camera used color mapping and Otsu’s Segmentation Algorithm for image processing. Otsu Segmentation Algorithm separates the background and foreground of the image while Color Mapping Algorithm converts colors of the image into temperature. The researchers also tested the power consumption and data sizes to determine the efficiency of the monitoring system. Using Convolutional Neural Network, the researchers trained 300 images to assess the state of the Cryptocurrency Mining rig. Additionally, a two-tailed T-Test will determine any significant difference between the two algorithms. Upon training the images, the results obtained show that the two-tail P-value of temperature and humidity is 0.35 and 0.2566, respectively, which affirms no significant difference. However, the power consumption and data size had a substantial difference with P-values of 0.0004 and 3E-06, respectively. Moreover, it shows that the application of Otsu reduces the data size and consumes more power due to deep learning.