An efficient algorithm for large-scale RFID Network Planning

Hasnan, Khalid; Talib, Nihad Hasan; Nawawi, Azli; Elewe, Adel Muhsin; Abdullah, Haslaile; Tahir, Suhaidah

doi:10.1109/jeeit.2019.8717490

Cited by 7 publications

(1 citation statement)

References 5 publications

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“…The method improved cost efficiency in large and complex environments with different indoor topologies [10]. The gradient-based cuckoo search (GBCS) method and over dimensioning (OD) model achieved optimal tag coverage with a minimum number of deployed RFID readers and obtained superior results over a large area [11,12].…”

Section: Introductionmentioning

confidence: 99%

Real-time monitoring of clinic risks using an integrated RFID-FA scheme

Hussein

Fayyadh

2021

Bulletin EEI

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Patient safety is a global public health concern because of increases in the number of mistreatments due to the improper identification of patients or the improper administration of drugs. Risk in clinic management refers to the systematic process used to specify, control, and analyze organizational risks. The present article developed a new method to detect different objects automatically in real-time by monitoring and controlling the hospital workflow using radio frequency identification (RFID). The system methodology starts with identifying the functional area by detecting the room optical characters. Then clustering and matching the symmetrical functional area using histogram matching technique. For the monitoring process, the radio frequency network planning RNP has been used. Density-based scan algorithm (DBSCAN) was used for clustering and extracting the area, then all gathered data transferred to the firefly algorithm to track drug distribution and specify doctor and nurse locations. The simulation results observe real-time tracking and identification of people and drugs based on hospital zone designs. The results present 87% tag real-time coverage for managing and monitoring human inside the hospital. The effectiveness of this system shows that it was useful in monitoring clinic operations and effective for a hospital network solution.

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Section: Introductionmentioning

confidence: 99%