Real-time monitoring Patients Using RFID Network Planning Scheme

The increasing use of the Internet of Things (IoT) in the field of health care has become one of the phenomena to improve public health and reduce the risks of health changes, as well as monitoring people with infectious diseases to know their daily activities accurately. From here emerged the impor...

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Bibliographic Details
Published in:	2021 4th International Iraqi Conference on Engineering Technology and Their Applications (IICETA) pp. 184 - 189
Main Authors:	Musadaq, Rusul, Cansever, Galip, Radie, A. Hussien
Format:	Conference Proceeding
Language:	English
Published:	IEEE 21-09-2021
Subjects:	Boundary conditions Image processing Internet of Things Linear programming monitoring Patients Monte Carlo methods Patient monitoring PSO algorithm RFID networks planning Topology
Online Access:	Get full text
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Summary:	The increasing use of the Internet of Things (IoT) in the field of health care has become one of the phenomena to improve public health and reduce the risks of health changes, as well as monitoring people with infectious diseases to know their daily activities accurately. From here emerged the importance of RFID radio monitoring systems as an effective tool in this field. The problem with using this system is the poor performance in placement of RFID readers positioning which is often done on a trial-and-error basis. This lead to time consuming and generally results in less-than-optimal coverage. This paper provides an immediate definition of radiofrequency associated with particle swarm optimization (PSO) as a patient monitoring system. The current new method for improving healthcare quality has been developed by combining the Patient Move Topology Network with the RFID Detection System. This task includes three objective functions, the first one is to find the reader's optimal number to be employed in the system, secondly, covering all patients, and the third one is to reduce the overlapping among the spread area of readers which helps with avoiding data to be confused. The result of this step will define the boundary conditions of Altinbas University as a case study for this work. Then the Monte Carlo simulation will generate random tag data. Finally, the PSO algorithm will position the readers by solving the multi-objective functionality. Experimental results show that the proposed system is capable of achieving high tag coverage for tracking and locating people with fewer readers in actual conditions.
DOI:	10.1109/IICETA51758.2021.9717740