RFID-Enabled IoT Asset Management System and Machine Learning Integration

Asset tracking is crucial in today's fast-paced business environment, but traditional methods are inefficient and prone to errors, leading to revenue and productivity losses. To address this issue, a smart asset tracking system is proposed in this study, which integrates IoT technology, Node MCU, RFID sensors, buzzer, and Tag4 for improved accuracy and efficiency in asset management. The system uses RFID tags to track assets, and the data is processed using two Machine Learning algorithms (Gradient Boosting and Random Forest) for improved accuracy and thereby computing metrics such as accuracy, precision, recall, f1 score and error rate and comparing them. The system includes a buzzer to alert personnel in case of asset theft or misplacement. This study provides a detailed description of the system's architecture, operation, benefits, and potential applications in various industries.