Precise Identifying Assets Inside a Metal Cabinet Using RFID and Machine Learning Method

The demand for CLOUD services has increased in recent years. Data centers have been equipped with more information technology (IT) assets to meet this trend; therefore, an efficient inventory management system is essential to track growing assets. The purpose of the study was to propose a novel RFID-based audit inventory system to precisely identify assets inside the metal cabinet of the data center. A multi-element RFID antenna and a machine learning method were adopted to implement such a system. The experimental results revealed that the extracted features of the Received Signal Strength Indicator (RSSI), Tag Read Count (TRC), combining Model Support Vector Machines (SVM), outperformed the other models with exceeding 99% accuracy in prediction performance. The findings of the study may serve as a guide for other RFID-based systems for static asset tracking in a metal cabinet.