International Journal of Fuzzy Logic and Intelligent Systems 2024; 24(3): 181-193
Published online September 25, 2024
https://doi.org/10.5391/IJFIS.2024.24.3.181
© The Korean Institute of Intelligent Systems
Le Van Hoa and Vo Viet Minh Nhat
Hue University, Hue City, Vietnam
Correspondence to :
Vo Viet Minh Nhat (vvmnhat@hueuni.edu.vn)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords: RFID, Network planning, Hopfield network, Optimization, Healthcare
No potential conflict of interest relevant to this article was reported.
International Journal of Fuzzy Logic and Intelligent Systems 2024; 24(3): 181-193
Published online September 25, 2024 https://doi.org/10.5391/IJFIS.2024.24.3.181
Copyright © The Korean Institute of Intelligent Systems.
Le Van Hoa and Vo Viet Minh Nhat
Hue University, Hue City, Vietnam
Correspondence to:Vo Viet Minh Nhat (vvmnhat@hueuni.edu.vn)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords: RFID, Network planning, Hopfield network, Optimization, Healthcare
The Family Medicine Center campus with monitored and unmonitored areas.
The Family Medicine Center campus is gridded with an normalized distance of 3.69×cos(45°) ≈ 5.2 m.
The Hopfield network corresponds to the cells of the gridded workspace.
Example of placement restriction areas (at edges and around placed readers).
Coverage rates with varied cell sizes and
Coverage values with varied cell sizes and
Runtime increases rapidly as cell size decreases.
Coverage efficiency with the threshold
Coverage efficiency with the threshold
The placement results of (a) HN-RNPO, (b) GA-RNPO and (c) PSO-RNPO without RRE.
The placement results of (a) HN-RNPO, (b) GA-RNPO and (c) PSO-RNPO with RRE.
Energy efficiency with the threshold
Energy efficiency with the threshold
Table 1 . Medical assets and their original (org.) and normalized (norm.) feature values.
No. | Asset | Price ($) ( | Frequency ( | Quantity ( | Urgent role ( | Norm. value | ||||
---|---|---|---|---|---|---|---|---|---|---|
Org. | Norm. | Org. | Norm. | Org. | Norm. | Org. | Norm. | |||
1 | ECG monitors | 1000 | 0.323 | medium | 0.5 | 20 | 0.2 | high | 0.9 | 1.0242 |
2 | First aid kit | 166 | 0.041 | low | 0.1 | 10 | 0 | high | 0.9 | 0.8403 |
3 | Heart-lung stethoscope | 60 | 0.005 | high | 0.9 | 40 | 1 | high | 0.9 | 1.0914 |
4 | Health scale with ruler | 100 | 0.019 | high | 0.9 | 30 | 0.6 | low | 0.1 | 0.3414 |
5 | Infusion pole | 50 | 0.002 | low | 0.1 | 20 | 0.2 | low | 0.1 | 0.1438 |
6 | Oxygen bag | 44 | 0 | low | 0.1 | 40 | 1 | high | 0.9 | 0.9300 |
7 | Pregnancy heart monitor | 430 | 0.131 | medium | 0.5 | 10 | 0 | high | 0.9 | 0.9426 |
8 | Respiratory meter | 3000 | 1 | high | 0.9 | 10 | 0 | high | 0.9 | 1.2400 |
9 | Stretcher | 280 | 0.08 | low | 0.1 | 20 | 0.2 | low | 0.1 | 0.1633 |
10 | Surgical light | 480 | 0.147 | high | 0.9 | 40 | 1 | low | 0.1 | 0.4069 |
12 | Wheelchair | 100 | 0.019 | low | 0.1 | 30 | 0.6 | low | 0.1 | 0.1814 |
Gayoung Kim
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International Journal of Fuzzy Logic and Intelligent Systems 2019; 19(4): 342-348 https://doi.org/10.5391/IJFIS.2019.19.4.342Minyoung Kim
Int. J. Fuzzy Log. Intell. Syst. 2017; 17(1): 10-16 https://doi.org/10.5391/IJFIS.2017.17.1.10The Family Medicine Center campus with monitored and unmonitored areas.
|@|~(^,^)~|@|The Family Medicine Center campus is gridded with an normalized distance of 3.69×cos(45°) ≈ 5.2 m.
|@|~(^,^)~|@|The Hopfield network corresponds to the cells of the gridded workspace.
|@|~(^,^)~|@|Example of placement restriction areas (at edges and around placed readers).
|@|~(^,^)~|@|Coverage rates with varied cell sizes and
Coverage values with varied cell sizes and
Runtime increases rapidly as cell size decreases.
|@|~(^,^)~|@|Coverage efficiency with the threshold
Coverage efficiency with the threshold
The placement results of (a) HN-RNPO, (b) GA-RNPO and (c) PSO-RNPO without RRE.
|@|~(^,^)~|@|The placement results of (a) HN-RNPO, (b) GA-RNPO and (c) PSO-RNPO with RRE.
|@|~(^,^)~|@|Energy efficiency with the threshold
Energy efficiency with the threshold