An Artificial Neural Network-Based RFID Network Planning Method for Asset Monitoring in Healthcare

Radio frequency identification (RFID) network planning is the problem of determining where to place RFID readers in a work area so that tags or objects tagged therein can be monitored. With recent developments in RFID technology, RFID-based monitoring systems have been deployed in many fields, including medical asset management. Monitoring medical assets in a hospital is essential for limiting losses and theft, thereby improving the quality of medical treatment and patient care. This study proposes an RFID network planning model for medical asset monitoring in which an artificial neural network (ANN) is used to optimize the placement of readers within a hospital campus with the constraints of a limited number of used readers and different priorities of monitored assets. The case study considered the Family Medicine Center, University of Medicine and Pharmacy, Hue University. The simulation results show that with the ANN-based optimization method, the optimal location of readers is quickly found while satisfying certain constraints related to medical asset monitoring.