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

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 abstractFog-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 learn...
Source: Medical and Biological Engineering and Computing - Category: Biomedical Engineering Source Type: research