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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

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

Le Van Hoa and Vo Viet Minh Nhat

Hue University, Hue City, Vietnam

Correspondence to :
Vo Viet Minh Nhat (vvmnhat@hueuni.edu.vn)

Received: January 8, 2024; Revised: June 5, 2024; Accepted: August 30, 2024

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 authors thank the Ministry of Education and Training, Vietnam, for supporting and funding this project (Code B2023-DHH-18).

No potential conflict of interest relevant to this article was reported.

Le Van Hoa received his Ph.D. in Computer Science from Hue University, Vietnam, in 2020. He is currently a lecturer at Hue University, Vietnam. His research interests include optical packet/burst-based switching networks, mobile RFID/sensor systems, fairness, quality of service, smart Tourism, neural networks, and soft computing.

Vo Viet Minh Nhat received his Ph.D. in Cognitive Informatics from the University of Quebec in Montreal, Canada, in 2007. He is currently an associate professor at Hue University, Vietnam. His research interests include optical packet/burst-based switching networks, mobile RFID/sensor systems, soft computing, neural networks, and evolutionary computation.

Article

Original Article

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.

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

Le Van Hoa and Vo Viet Minh Nhat

Hue University, Hue City, Vietnam

Correspondence to:Vo Viet Minh Nhat (vvmnhat@hueuni.edu.vn)

Received: January 8, 2024; Revised: June 5, 2024; Accepted: August 30, 2024

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.

Abstract

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

Fig 1.

Figure 1.

The Family Medicine Center campus with monitored and unmonitored areas.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 181-193https://doi.org/10.5391/IJFIS.2024.24.3.181

Fig 2.

Figure 2.

The Family Medicine Center campus is gridded with an normalized distance of 3.69×cos(45°) ≈ 5.2 m.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 181-193https://doi.org/10.5391/IJFIS.2024.24.3.181

Fig 3.

Figure 3.

The Hopfield network corresponds to the cells of the gridded workspace.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 181-193https://doi.org/10.5391/IJFIS.2024.24.3.181

Fig 4.

Figure 4.

Example of placement restriction areas (at edges and around placed readers).

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 181-193https://doi.org/10.5391/IJFIS.2024.24.3.181

Fig 5.

Figure 5.

Coverage rates with varied cell sizes and dr2r of 3.9 m and 5.2 m.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 181-193https://doi.org/10.5391/IJFIS.2024.24.3.181

Fig 6.

Figure 6.

Coverage values with varied cell sizes and dr2r of 3.9 m and 5.2 m

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 181-193https://doi.org/10.5391/IJFIS.2024.24.3.181

Fig 7.

Figure 7.

Runtime increases rapidly as cell size decreases.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 181-193https://doi.org/10.5391/IJFIS.2024.24.3.181

Fig 8.

Figure 8.

Coverage efficiency with the threshold dr2r = 5.2.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 181-193https://doi.org/10.5391/IJFIS.2024.24.3.181

Fig 9.

Figure 9.

Coverage efficiency with the threshold dr2r = 3.9.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 181-193https://doi.org/10.5391/IJFIS.2024.24.3.181

Fig 10.

Figure 10.

The placement results of (a) HN-RNPO, (b) GA-RNPO and (c) PSO-RNPO without RRE.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 181-193https://doi.org/10.5391/IJFIS.2024.24.3.181

Fig 11.

Figure 11.

The placement results of (a) HN-RNPO, (b) GA-RNPO and (c) PSO-RNPO with RRE.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 181-193https://doi.org/10.5391/IJFIS.2024.24.3.181

Fig 12.

Figure 12.

Energy efficiency with the threshold dr2r = 5.2.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 181-193https://doi.org/10.5391/IJFIS.2024.24.3.181

Fig 13.

Figure 13.

Energy efficiency with the threshold dr2r = 3.9.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 181-193https://doi.org/10.5391/IJFIS.2024.24.3.181

Table 1 . Medical assets and their original (org.) and normalized (norm.) feature values.

No.AssetPrice ($) (w1 = 0.25)Frequency (w2 = 0.2)Quantity (w3 = 0.1)Urgent role (w4 = 0.45)Norm. value
Org.Norm.Org.Norm.Org.Norm.Org.Norm.
1ECG monitors10000.323medium0.5200.2high0.91.0242
2First aid kit1660.041low0.1100high0.90.8403
3Heart-lung stethoscope600.005high0.9401high0.91.0914
4Health scale with ruler1000.019high0.9300.6low0.10.3414
5Infusion pole500.002low0.1200.2low0.10.1438
6Oxygen bag440low0.1401high0.90.9300
7Pregnancy heart monitor4300.131medium0.5100high0.90.9426
8Respiratory meter30001high0.9100high0.91.2400
9Stretcher2800.08low0.1200.2low0.10.1633
10Surgical light4800.147high0.9401low0.10.4069
12Wheelchair1000.019low0.1300.6low0.10.1814

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