A Multi-Protocol Controller Deployment in SDN-based IoMT Architecture

CİCİOĞLU M., Calhan A.

IEEE Internet of Things Journal, cilt.9, sa.21, ss.20833-20840, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 9 Sayı: 21
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1109/jiot.2022.3175669
  • Dergi Adı: IEEE Internet of Things Journal
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Compendex, INSPEC
  • Sayfa Sayıları: ss.20833-20840
  • Anahtar Kelimeler: Protocols, Wireless communication, Computer architecture, IEEE 802, 15 Standard, Time division multiple access, Standards, Internet of Things, Machine learning (ML), software-defined networking (SDN), wireless sensor networks, INTERNET, FRAMEWORK, THREATS
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

IEEEInternet 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-based load balancing and time-sensitive prioritization (MLA) algorithms for dense and dynamic networks. SDN-based IoMT network that consists of IEEE 802.15.6, TDMA, and IEEE 802.11 protocols are 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 dataset is created for load balancing. The performances of Support Vector Machine SVM), Ensemble of Decision Trees, k-NN, and Naive Bayes machine learning algorithms are compared, and SVM gives the best result with 95.1% accuracy.