IOT Based Data Acquisition System using Machine Learning Techniques

Abstract

Monitoring system is a very essential element in the industry nowadays. The development of Data Acquisition (DAQ) system is to develop a device that can acquire data from different signal from industrial sensor. The conventional way of storing data is by providing the database on local server. However, the research develops an interface of acquiring the signal from industrial sensor to a low-cost IoT device that can push and store the acquired data to the cloud. The acquired data can be monitored by using mobile application interface based on trending graph or tabulated data. By using the stored data also, machine learning algorithm can be used to analyse the stored data. Two different type of machine learning techniques has been introduced for the research which are Random Forest and Decision Tree based on regression features. The result from analysed data will be updated and viewed on the mobile application user interface. The result also has shown Random Forest has higher accuracy rate compared to Decision tree but with much higher of processing time. The research can be further enhanced by using the output signal from industrial and run in the industrial condition