Cicioğlu, MurtazaÇalhan, Ali2024-09-202024-09-202022-11-012327-4662https://doi.org/10.1109/JIOT.2022.3175669https://ieeexplore.ieee.org/document/9776486https://hdl.handle.net/11452/44952Internet 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.eninfo:eu-repo/semantics/closedAccessInternetFrameworkThreatsProtocolsWireless communicationComputer architectureIeee 80215 standardTime division multiple accessStandardsInternet of thingsMachine learning (ml)Software-defined networking (sdn)Wireless sensor networksScience & technologyTechnologyComputer science, information systemsEngineering, electrical & electronicTelecommunicationsComputer scienceEngineeringArticle000871080800008208332084092110.1109/JIOT.2022.3175669