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A Software-Defined Network-based Intelligent Decision Support System for the Internet of Things Networks

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Abstract

The concept of connected devices is effectively integrated to create heterogeneous wireless networks like the Internet of Things (IoT). These networks are using multiples technologies and standards to manage open, complex, distributed, and dynamic networks. The smart home is one of the amazing area of services in IoT where smart devices are connected with consumer devices for better monitoring and controlling the systems. With tremendous benefits, the smart devices have some barriers to accomplish the tasks like heterogeneity which leads to interoperability, big data handling, scalability, resource allocation, and connectivity issues. In this context, this paper proposes an intelligent Software Defined Network-based Intelligent Decision Support System for IoT (SDN-IDSSIoT). The proposed system contains two main modules including IDSS conceptual model and SDN-based architecture to support interoperability among heterogeneous smart home devices. Experiments are conducted to evaluate the proposed system in terms of average throughput and round trip time as compared to the stat of the art existing models. The proposed model archives around 20% more throughput, around 30% less round trip time in the presence of different number of nodes.

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References

  1. Nauman, A., Qadri, Y. A., Amjad, M., Zikria, Y. B., Afzal, M. K., & Kim, S. W. (2020). Multimedia Internet of Things: A comprehensive survey. IEEE Access : Practical Innovations, Open Solutions, 8, 8202–8250

    Article  Google Scholar 

  2. Zaib, M. H., Bashir, F., Qureshi, K. N., Kausar, S., Rizwan, M., & Jeon, G. (2021). Deep learning based cyber bullying early detection using distributed denial of service flow. Multimedia Systems, pp.1–20

  3. Villa-Henriksen, A., Edwards, G. T., Pesonen, L. A., Green, O., & Sørensen, C. A. G. (2020). Internet of Things in arable farming: Implementation, applications, challenges and potential. Biosystems engineering, 191, 60–84

    Article  Google Scholar 

  4. Iqbal, S., Abdullah, A. H., Qureshi, K. N., & Lloret, J. (2017). Soft-GORA: Soft constrained globally optimal resource allocation for critical links in IoT backhaul communication. IEEE Access : Practical Innovations, Open Solutions, 6, 614–624

    Article  Google Scholar 

  5. Qureshi, K. N., Hussain, R., & Jeon, G. (2020). A distributed software defined networking model to improve the scalability and quality of services for flexible green energy internet for smart grid systems. Computers & Electrical Engineering, 84, 106634

    Article  Google Scholar 

  6. Hasan, A., & Qureshi, K. (2018). Internet of Things device authentication scheme using hardware serialization, in 2018 International Conference on Applied and Engineering Mathematics (ICAEM), pp. 109–114. IEEE

  7. Rahman, A., Nasir, M. K., Rahman, Z., Mosavi, A., Shahab, S., & Minaei-Bidgoli, B. (2020). Distblockbuilding: A distributed blockchain-based sdn-iot network for smart building management. IEEE Access : Practical Innovations, Open Solutions, 8, 140008–140018

    Article  Google Scholar 

  8. Ghaffar, Z., Alshahrani, A., Fayaz, M., Alghamdi, A. M., & Gwak, J. (2021). A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges. Electronics, 10(8), 880

  9. Yazdinejad, A., Parizi, R. M., Dehghantanha, A., Zhang, Q., & Choo, K. K. R. (2020). An energy-efficient SDN controller architecture for IoT networks with blockchain-based security. IEEE Transactions on Services Computing, 13(4), 625–638

    Article  Google Scholar 

  10. Shirmarz, A., & Ghaffari, A. (2021). Taxonomy of controller placement problem (CPP) optimization in Software Defined Network (SDN): a survey, Journal of Ambient Intelligence and Humanized Computing, 12, 10473-10498.

  11. Balasubramanian, V., Aloqaily, M., & Reisslein, M. (2021). An SDN architecture for time sensitive industrial IoT. Computer Networks, 186, 107739

    Article  Google Scholar 

  12. Jung, D., Tran Tuan, V., Dai Tran, Q., Park, M., & Park, S. (2020). Conceptual framework of an intelligent decision support system for smart city disaster management. Applied Sciences, 10(2), 666

  13. Guo, Y., Wang, N., Xu, Z. Y., & Wu, K. (2020). The internet of things-based decision support system for information processing in intelligent manufacturing using data mining technology. Mechanical Systems and Signal Processing, 142, 106630

    Article  Google Scholar 

  14. Khakifirooz, M., Fathi, M., Ampatzidis, Y., & Pardalos, P. M. (2021). Ambient-Intelligent Decision Support System (Am-IDSS) for Smart Manufacturing. Encyclopedia of Organizational Knowledge, Administration, and Technology (pp. 2338–2351). IGI Global

  15. Belciug, S., & Gorunescu, F. (2020). A brief history of intelligent decision support systems. Intelligent Decision Support Systems—A Journey to Smarter Healthcare (pp. 57–70). Springer

  16. Teniwut, W., & Hasyim, C. (2020). Decision support system in supply chain: A systematic literature review. Uncertain Supply Chain Management, 8(1), 131–148

    Article  Google Scholar 

  17. Qureshi, K. N., Jeon, G., & Piccialli, F. (2020). Anomaly detection and trust authority in artificial intelligence and cloud computing. Computer Networks, 184, 107647.

  18. Lee, S. K., Bae, M., & Kim, H. (2017). Future of IoT networks: A survey. Applied Sciences, 7(10), 1072

    Article  Google Scholar 

  19. Kim, Y., & Lee, Y. (2015). Automatic generation of social relationships between Internet of Things in smart home using SDN-based home cloud, in 2015IEEE 29th International conference on advanced information networking and applications workshops (pp. 662–667). IEEE

  20. Jang, H. C., Huang, C. W., & Yeh, F. K. (2016). Design a bandwidth allocation framework for SDN based smart home, in 2016IEEE 7th Annual information technology, electronics and mobile communication conference (IEMCON) (pp. 1–6). IEEE

  21. Sharma, P. K., Park, J. H., Jeong, Y. S., & Park, J. H. (2019). Shsec: sdn based secure smart home network architecture for internet of things. Mobile Networks and Applications, 24(3), 913–924

    Article  Google Scholar 

  22. Nobakht, M., Sivaraman, V., & Boreli, R. (2016). A host-based intrusion detection and mitigation framework for smart home IoT using OpenFlow, in 2016 11th International conference on availability, reliability and security (ARES) (pp. 147–156). IEEE

  23. Chaudhary, R., Aujla, G. S., Garg, S., Kumar, N., & Rodrigues, J. J. (2018). SDN-enabled multi-attribute-based secure communication for smart grid in IIoT environment. IEEE Transactions on Industrial Informatics, 14(6), 2629–2640

    Article  Google Scholar 

  24. Chen, N., Wang, M., Zhang, N., Shen, X. S., & Zhao, D. (2017). SDN-based framework for the PEV integrated smart grid,. IEEE Network, 31(2), 14–21

    Article  Google Scholar 

  25. Kyriakopoulos, G., Ntanos, S., Anagnostopoulos, T., Tsotsolas, N., Salmon, I., & Ntalianis, K. (2020). Internet of things (IoT)-enabled elderly fall verification, exploiting temporal inference models in smart homes. International Journal of Environmental Research and Public Health, 17(2), 408

    Article  Google Scholar 

  26. Qin, Z., Denker, G., Giannelli, C., Bellavista, P., & Venkatasubramanian, N. (2014). A software defined networking architecture for the internet-of-things, in 2014 IEEE network operations and management symposium (NOMS) (pp. 1–9). IEEE

  27. Gazis, V., Goertz, M., Huber, M., Leonardi, A., Mathioudakis, K., Wiesmaier, A., & Zeiger, F. (2015). Short paper: IoT: Challenges, projects, architectures, in 201518th International Conference on Intelligence in Next Generation Networks (pp. 145–147). IEEE

  28. Gambi, E., Montanini, L., Raffaeli, L., Spinsante, S., & Lambrinos, L. (2016). Interoperability in IoT infrastructures for enhanced living environments, in 2016IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom) (pp. 1–5). IEEE

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Authors and Affiliations

  1. Department of Electronic & Computer Engineering, University of Limerick, V94 T9PX, Limerick, Ireland

    Kashif Naseer Qureshi

  2. Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia

    Adi Alhudhaif

  3. Department of Computer Science, Bahria University, Islamabad, Pakistan

    Moeen Azahar & Ibrahim Tariq Javed

  4. Department of Embedded Systems Engineering, Incheon National University, Incheon, Korea

    Gwanggil Jeon

Authors
  1. Kashif Naseer Qureshi
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  2. Adi Alhudhaif
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  3. Moeen Azahar
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  4. Ibrahim Tariq Javed
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  5. Gwanggil Jeon
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Correspondence to Gwanggil Jeon.

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Qureshi, K.N., Alhudhaif, A., Azahar, M. et al. A Software-Defined Network-based Intelligent Decision Support System for the Internet of Things Networks. Wireless Pers Commun 126, 2825–2839 (2022). https://doi.org/10.1007/s11277-022-09626-w

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  • DOI: https://doi.org/10.1007/s11277-022-09626-w

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