Fuzzy control to support CoAP for congestion avoidance in the Internet of Things networks
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https://doi.org/10.54939/1859-1043.j.mst.88.2023.22-33Keywords:
CoAP protocol; Congestion Control; Fuzzy Control; IoT networks.Abstract
The CoAP (Constrained Application Protocol) protocol and its improvements are still limited in its ability to detect congestion early and adjust transmission rates to match the dynamic state of the Internet of Things networks. This paper proposes a solution to implement a fuzzy control mechanism for network congestion avoidance with the selection of appropriate input and output parameters. The parameters are evaluated by the simulation tool. The simulation results show that the parameter selection is suitable for the theory, allowing the fuzzy control system to achieve higher performance indices compared to the standard CoAP.
References
[1]. RFC 7252, “The Constrained Application Protocol (CoAP),” available: https://rfc-editor.org/ info/ rfc7252.
[2]. C. Bormann, Z. Shelby, “Block–Wise Transfers in the Constrained Application Protocol (CoAP),” Available: https://rfc-editor.org/info/rfc7959.
[3]. H. Haile, K. Grinnemo, S. Ferlin, et.al., “End-to-end congestion control approaches for high throughput and low delay in 4G/5G cellular networks,” in Computer Networks, Vol. 186-107692, pp. 1-22, (2021). DOI: https://doi.org/10.1016/j.comnet.2020.107692
[4]. H. Jiang, Q. Li, G. Shen, et.al., “When Machine Learning Meets Congestion Control: A Survey and Comparison,” in Computer Networks, vol. 192-108033, pp. 1-23, (2021). DOI: https://doi.org/10.1016/j.comnet.2021.108033
[5]. F. Righetti, et al. "Investigating the CoAP Congestion Control Strategies for 6TiSCH-Based IoT Networks," in IEEE Access 11, pp. 11054-11065, (2023). DOI: https://doi.org/10.1109/ACCESS.2023.3241327
[6]. M.A. Tariq, M. Khan, M.T.R. Khan, D. Kim, “Enhancements and Challenges in CoAP – A Survey,” in Sensors, vol. 20, pp. 1-29, (2020). DOI: https://doi.org/10.3390/s20216391
[7]. P.K. Donta, S.N. Srirama, et al., “iCoCoA: intelligent congestion control algorithm for CoAP using deep reinforcement learning,” in Journal Ambient Intell Human Computer, Vol. 14, pp. 2951–2966, (2023). DOI: https://doi.org/10.1007/s12652-023-04534-8
[8]. P. Aimtongkham, P. Horkaew, C. So-In, “An Enhanced CoAP Scheme Using Fuzzy Logic with Adaptive Timeout for IoT Congestion Control,” in IEEE Access, Vol. 9, pp.58967-58981, (2021). DOI: https://doi.org/10.1109/ACCESS.2021.3072625
[9]. T. N. Pham, D. H. Hoang, T. T. Duong Le, "Fuzzy Congestion Control and Avoidance for CoAP in IoT Networks," in IEEE Access, Vol. 10, pp. 105589-105611, (2022). DOI: https://doi.org/10.1109/ACCESS.2022.3211296
[10]. L.A. Zadeh, “Outline of a new approach to the analysis of complex systems and decision processes,” in IEEE Transactions on Systems, Man, and Cybernetics 3(1), pp. 28–44, (1973). DOI: https://doi.org/10.1109/TSMC.1973.5408575
[11]. T.J. Ross, “Fuzzy Logic with Engineering Applications,” Wiley Publisher, 3rd Edition, ISBN-10:047074376X, (2010). DOI: https://doi.org/10.1002/9781119994374
[12]. RFC 6298, “Computing TCP's Retransmission Timer,” available: https://rfc-editor.org/info/rfc6298
[13]. N. Cardwell, Y. Cheng, C. S. Gunn, S. H. Yeganeh, and V. Jacobson, “BBR: Congestion-Based Congestion Control,” ACM Queue, Vol. 14, No. 5, pp. 50:20–53, (2016). DOI: https://doi.org/10.1145/3012426.3022184
[14]. H.J. Zimmerman, “Fuzzy set theory - and its applications,” Kluwer Academic Publishers, Springer Science, Fourth Edition (2001).
[15]. NS-3 Network Simulator, NS3.36, available: https://www.nsnam.org/.