A multiprotocol controller deployment in SDN-based ioMT architecture

Abstract

Internet of Medical Things (IoMT) as a next-generation network requires heterogeneous services, technologies, and equipment infrastructure management resulting in more complex systems. The software-defined networking (SDN) approach has emerged as a promising solution to reduce this complexity by proposing a vendor-independent structure that disaggregates the control and data planes. In this study, an architecture based on the SDN is proposed for such heterogeneous and complex IoMT networks. A new controller that supports different wireless communication protocols has been developed for the control plane. We propose machine learning (ML)-based load balancing and time-sensitive prioritization (MLA) algorithms for dense and dynamic networks. An SDN-based IoMT network that consists of IEEE 802.15.6, TDMA, and IEEE 802.11 protocols is analyzed in a simulation program simultaneously using various scenarios in terms of throughput, delay, packet loss ratio, bit error rate, and user density parameters. In addition, in this study, a new data set is created for load balancing. The performances of support vector machine (SVM), ensemble of decision trees, k-NN, and Naive Bayes ML algorithms are compared, and SVM gives the best result with 95.1% accuracy.