Applying Random Forest for IoT Systems in Industrial Environments
Monika Dangore, Deepali Bhaturkar, Kanchan M. Bhale, Hemlata Makarand Jadhav, Vishal Borate, Yogesh Kisan Mali
Abstract
There has been a total transformation in industrial environments as a result of the implementation of intelligent Internet of Things (IoT) technologies, which have enabled better resource allocation, predictive maintenance, and realtime monitoring. Despite this, it is still challenging to guarantee the accuracy and dependability of these systems, particularly in industrial settings that are both complex and constantly evolving. Despite the fact that previous research has studied the potential of machine learning in IoT applications, there is a lack of research on how to properly incorporate Random Forest in order to tackle the difficulties that are specific to industrial IoT systems. Developing a novel strategy that makes use of Random Forest’s characteristics to improve prediction accuracy in industrial IoT installations, boost adaptability, and minimize latency. Using Random Forest, a powerful machine learning technique that is known for its durability and flexibility, this work addresses the need for greater intelligence in IoT systems that are used in industrial settings. For the purpose of predictive analytics, the suggested technique includes the collection of data in real time from sensors on industrial devices, as well as the use of feature engineering and the utilization of a Random Forest model. When dealing with complex and ever-changing industrial data, the model will perform better than more conventional approaches. For example, it will perform better than other approaches. Consequently, the IoT system has become more adaptable and can swiftly adjust to new industrial conditions. Additionally, the accuracy of forecasts has substantially improved as a result of this. The findings will demonstrate that use of Random Forest for intelligent IoT systems in manufacturing facilities is successful.