Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring

• Published in: Medical & biological engineering & computing 2023-05, Vol.61 (5), p.1133-1147
• Main Authors: Yıldırım, Emre, Cicioğlu, Murtaza, Çalhan, Ali
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Big Data; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Body area networks; Cloud Computing; Communication; Computer Applications; Computer architecture; Coronaviruses; COVID-19; Data analysis; Decision making; Decision trees; Diabetes; Diabetes mellitus; Edge computing; Fog; Fuzzy logic; Human Physiology; Humans; Imaging; Internet; Internet of medical things; Learning algorithms; Machine learning; Neural networks; New technology; Original; Original Article; Predictions; Radiology; River beds; Support vector machines; Viral diseases; Wireless networks
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-023-02776-4

The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed. Graphical abstract Fog-Cloud architecture-driven for IoMT networks • An IoMT framework is designed with important components and functions such as fog and cloud node capabilities. •Real-time data has been obtained from WBANs in Riverbed Modeler for a more realistic performance analysis of IoMT. •Fuzzy logic and machine learning algorithms (RF, SVM, and ANN) are used for diabetes predictions. •Intra and Inter-WBAN communications (IEEE 802.15.6 standard) are modeled as essential components of the IoMT framework with all functions.