An Intelligent Dynamic Bandwidth Allocation Method to Support Quality of Service in Internet of Things
Keywords:Dynamic bandwidth allocation, Reinforcement Learning, Machine learning models, Quality of Service
Worldwide, Internet of Things (IoT) devices will surpass a range of five billion by 2025 and developed countries will extend to advance by supplying almost two-thirds of such connections. With existing infrastructure, allocating bandwidth to billions of IoT devices is going to be cumbersome. This paper addresses the problem of Dynamic bandwidth allocation in IoT devices. We enhanced the dynamic bandwidth allocation algorithms to support QoS in different bandwidth ranges. Our Proposed innovative Machine learning-based Intelligent Dynamic Bandwidth Allocation (IDBA) algorithm allocates the bandwidth effectively between IoT devices based on utilization patterns observed through machine learning methods. Moreover, we showed that an IDBA algorithm results in supporting quality of service in terms of ensuring uninterrupted bandwidth to critical IoT application where bandwidth tolerance is zero percent, along with that IDBA increasing the network throughput correlated to other dynamic bandwidth allocation algorithms. We demonstrate simulations in different applications. The results show that IDBA achieves better throughput even in low bandwidth range.
An enterprise scale IoT Platform: Watson, 2017. [Online]. available at: https://www.ibm.com/blogs/internet-of-things/enterprise-scale-iot-platform-watson/
V. Kharchenko, A. L. Kor and A. Rucinski, Dependable IoT for Human and Industry, River Publishers Series in Information Science and Technology, 2018.
B. Bojovi, E. Meshkova, N. Baldo, J. Riihijärvi and M. Petrova, “Machine learning-based dynamic frequency and bandwidth allocation in self-organized LTE dense small cell deployments,” EURASIP Journal on Wireless Communications and Networking, article no. 183, 2016. https://doi.org/10.1186/s13638-016-0679-0.
B. Fan, S. Leng, and K. Yang, “A dynamic bandwidth allocation algorithm in mobile networks with big data of users and networks,” IEEE Network, vol. 30, issue 1, pp. 6-10, 2016. https://doi.org/10.1109/MNET.2016.7389824.
Narrowband-IoT: pushing the boundaries of IoT, White Paper, February 2017. [Online]. Available at: https://www.vodafone.com/business/news-and-insights/white-paper/narrowband-iot-pushing-the-boundaries-of-iot#form-content
S. TROIA, Machine Learning-based Traffic Prediction and Pattern Extraction for Dynamic Optical Routing in SDN Mobile Metro Networks, Master’s Degree Thesis, Politecnico di Milano, 2016. [Online]. Available at: https://www.politesi.polimi.it/bitstream/10589/126134/3/thesis.pdf.
S. Guha, et al., “CURE: An efficient clustering algorithm for large databases,” Information systems, vol. 26, no. 1, pp. 35-58, 2001. https://doi.org/10.1016/S0306-4379(01)00008-4.
R. Mohandas, D. John Aravindhar, “Enhanced dynamic bandwidth allocation for improve QoS in internet of things,” Journal of Advanced Research in Dynamical & Control Systems, vol. 10, 03 Special Issue, pp. 594-600, 2018.
D. Hetzer, “Adaptable bandwidth planning using reinforcement learning,” Systemics, Cybernetics and Informatics, vol. 4, no. 4, pp. 5-10, 2004.
C.-K. Tham, T. C.-K. Hui, “Reinforcement learning-based dynamic bandwidth provisioning for quality of service in differentiated services networks,” Journal Computer Communications, vol. 28, issue 15, pp. 1741-1751, 2005. https://doi.org/10.1016/j.comcom.2004.12.018.
M. Van den Akker, C. P. M. Van Hoesel, M. W. P. Savelsbergh, “A polyhedral approach to single machine scheduling”, Mathematical Programming, vol. 85, pp. 541–572, 1999. https://doi.org/10.1007/s10107990047a.
L. Ruan and E. Wong, “Machine intelligence in allocating bandwidth to achieve low-latency performance,” Proceedings of the 2018 International Conference on Optical Network Design and Modeling (ONDM), Dublin, 2018, pp. 226-229.
A. H. Lashkari, D. N. Abbaspour, S. Jazayeri, “Router-based bandwidth allocation on optical networks,” Proceedings of the 2009 International Conference on Machine Learning and Computing IPCSIT, vol. 3, 2011, pp. 504-509.
P. K. Venkatesh, et al., “A framework to extract personalized behavioral patterns of user’s IoT devices data,” Proceedings of the 27th Annual International Conference on Computer Science and Software Engineering CASCON’17, Markham, Ontario, Canada November 6-8 2017, pp. 19-27. https://doi.org/10.475/123_4.
J. Elias, et al., “An efficient dynamic bandwidth allocation algorithm for quality of service networks,” D. Gaiti et al. (Eds.): AN 2006, Lecture Notes in Computer Science, vol. 4195, 2006, pp. 132–145. https://doi.org/10.1007/11880905_12.
R. Fei, K. Yang, S. Ou, S. Zhong, L. Gao, “A utility-based dynamic bandwidth allocation algorithm with QoS guarantee for IEEE 802.16j enabled vehicular networks,” Proceedings of the 8th IEEE International Conference on Scalable Computing and Communications, December 2009, pp. 200-205. https://doi.org/10.1109/EmbeddedCom-ScalCom.2009.44.
T. Xu, I. Darwazeh, “Non-orthogonal narrowband Internet of Things: A design for saving bandwidth and doubling the number of connected devices,” IEEE Internet of Things Journal, vol. 5, no. 3, pages 2120-2129, 2018. https://doi.org/10.1109/JIOT.2018.2825098.
F. Wamser, T. Zinner, P. Tran-Gia, J. Zhu, “Dynamic bandwidth allocation for multiple network connections: Improving user QoE and network usage of YouTube in mobile broadband,” Proceedings of the ACM International Conference on CSWS’14, August 18, 2014, pp. 57-62. https://doi.org/10.1145/2630088.2630095.
Managing Bandwidth – The User Based Approach, White paper, 2008. [Online]. Available at: http://www.ruthvictor.com/pdf/Firewall-Cyberoam/Whitepaper/Managing-bandwidth-the-User-based-approach.pdf
D. Wu and R. Negi, “Effective capacity: A wireless link model for support of quality of service,” IEEE Trans. Wireless Commun., vol. 12, no. 4, pp. 630–643, 2003. https://doi.org/10.1109/TWC.2003.814353.
Y. Cheng, V. K. N. Lau, and Y. Long, “A scalable limited feedback design for network MIMO using per-cell product codebook,” IEEE Trans. Wireless Commun., vol. 99, no. 10, pp. 3093–3099, 2010. https://doi.org/10.1109/TWC.2010.082110.091189.
A. Joakar, A Methodology for Solving Problems with Data Science for Internet of Things, Open Gardens (blog), July 21, 2016. [Online]. Available at: http://www.opengardensblog.futuretext.com/archives/2016/07/a-methodology-for-solving-problems-with-datascience-for-internet-of-things.html.
H. Zhao, C. Huang, “A data-processing algorithm in EPC internet of things,” Proceedings of the 2014 IEEE International Conference on Cyber-enabled Distributed Computing and Knowledge Discovery (CyberC), 2014, pp. 128–131. https://doi.org/10.1109/CyberC.2014.30.
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