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Unsupervised Learning Based Capacity Augmentation in SDN Assisted Wireless Networks

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Abstract

Future tactile internet is likely to have a combination of underlying wired and wireless networks with heterogeneous (legacy and new) access technologies to support diverse applications, e.g., Internet of Things (IoT). In this context, Opportunistic Network (ON) can be an important paradigm in wireless networks to help augment capacity of network for varying internet traffic requirements. Software Defined Networking (SDN), with its logically centralized control plane, is expected to ease implementation of functions, such as radio resource management, across wireless networks with multiple Radio Access Technologies (multi-RAT). Hence, tactile internet is likely to work over an intelligent SDN controlled cloud-based implementation of wired and wireless technologies, and necessitating opportunistic network capacity augmentation with appropriate RAT. This paper presents a novel SDN assisted architecture for futuristic wireless networks which augments network capacity on need basis using unsupervised Machine Learning (ML) to create ON cells with appropriate RAT. Subsequently, we define utilities for the Wireless Network Infrastructure (WNI) and the User Equipment (UE) to evaluate the benefit of creation of ON cells. A game theoretic model is developed to understand the strategies of the two players, i.e., WNI and UE, while using the ON cell resources. The Nash Equilibria (NE) of the game reveal that both UE and WNI gain by co-operating with each other and lose otherwise in utilizing the augmented network capacity. Simulation results also confirm this observation.

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References

  1. Awan I, et al. Modelling QoS in IoT Applications. In: 17th International Conference on Network-Based Information Systems, 2014

  2. OpenFlow, OpenFlow Switch Specification, Version 1.0.0, www.openflow.org, December 31, 2009.

  3. MacQueen JB. Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, pp. 281–297, 1967

  4. Duda RO, Hart PE, Stork DG. Unsupervised Learning and Clustering, Pattern classification. 2nd ed. Hoboken: Wiley; 2001.

    MATH  Google Scholar 

  5. 3GPP TS 36.331 V8.3.0, Radio Resource Control, www.3gpp.org.

  6. Kodinariya TM, Makwana PR. Review on determining number of Cluster in K-Means Clustering. International Journal of Advance Research in Computer Science and Management Studies. November 2013;1(6):

  7. Yap K-K, et al. Openroads: Empowering research in mobile networks. ACM SIGCOMM Comput Commun Rev. 2010;40(1):125–6.

    Article  Google Scholar 

  8. Gudipati A, et al. SoftRAN: Software Defined Radio Access Network, HotSDN 2013.

  9. Bernardos CJ, et al. An architecture for software defined wireless networking. IEEE Wirel Commun. 2014;21(3):

  10. Pentikousis K, Wang Y, Hu W. Mobileflow: Toward software defined mobile networks. IEEE Commun Mag 2013; 51(7).

  11. Li L, Mao Z, Rexford J. Toward software-defifned cellular networks, European Workshop in Software Defined Networking (EWSDN), 2012.

  12. Yap K-K, et al. Blueprint for Introducing Innovation into Wireless Mobile Networks, ACM SIGCOMM workshop on Virtualized infrastructure systems and architectures, 2010.

  13. Bansal M, et al. Openradio: A programmable wireless dataplane, First Workshop on Hot Topics in Software Defined Networks, HotSDN’12.

  14. Suresh L, et al. Towards programmable enterprise WLANs with Odin, First Workshop on Hot Topics in Software Defined Networks, HotSDN’12.

  15. Sun S, et al. Integrating network function virtualization with SDR and SDN for 4G/5G networks. IEEE Netw. 2015;29(3):54–9.

    Article  Google Scholar 

  16. Ishii H, Kishiyama Y, Takahashi H. A novel architecture for LTE-B: C-plane/U-plane split and Phantom Cell concept, IEEE GLOBECOM Int. Workshop Emerging Technologies for LTE-Advanced and Beyond-4G, 2012.

  17. Zaidi Z, et al. Future RAN Architecture: SD-RAN through a General-Purpose Processing Platform. IEEE Vehicular Technology Magazine. March 2015;10(1):

  18. Duan X, Akhtar AM, Wang X. Software-defined networking based resource management: data offloading with load balancing in 5G Hetnet. EURASIP J Wirel Commun. Neworks. 2015;181(1):1–13.

    Google Scholar 

  19. Zhou S, Zhao T, Niu Z, Zhou S. Software-Defined Hyper-Cellular Architecture for Green and Elastic Wireless Access, http://arxiv.org/pdf/1512.04935v1.pdf

  20. Nunes B, Astuto A, et al. A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks. In: IEEE Communications Surveys & Tutorials, Vol. 16, No. 3, Third Quarter 2014.

  21. Kreutz D, et al. Software-Defined Networking: A Comprehensive Survey http://arxiv.org/pdf/1406.0440.pdf

  22. Feamster N, Rexford J, Zegura E. The road to SDN: an intellectual history of programmable networks. SIGCOMM Comput Commun Rev. 2014;44(2):87–98.

    Article  Google Scholar 

  23. Jagadeesan NA, Krishnamachari B. Software-defined networking paradigms in wireless networks: a survey. ACM Comput. Surv. 2014; 47(2), Article 27.

  24. Jin X, Li LE, Vanbever L, Rexford J. Softcell: Scalable and flexible cellular core network architecture. In: ACM CoNEXT, 2013.

  25. Das D, Bapat J, Das D. A dynamic QoS negotiation mechanism between wired and wireless SDN domains. IEEE Trans Netw Serv Manag. 2017;14(4):1076–85.

    Article  Google Scholar 

  26. Soelistijanto B, Howarth MP. Transfer reliability and congestion control strategies in opportunistic networks: a survey. IEEE Commun Surv Tutor. 2013;16(1):538–55.

    Article  Google Scholar 

  27. Prabhaa C, Khannab R, Kumar S. Optimizing Social Information by Game theory and Ant Colony Method to Enhance Routing protocol in Opportunistic Networks, Perspectives in Science, 2016.

  28. Payal J, Rachna S, A Survey on Opportunistic Routing Protocols for Wireless Sensor Networks. In: Proceedings of International Conference on Communication, Computing and Virtualization (ICCCV), 2016.

  29. Phuttharak J, Loke SW. Mobile crowdsourcing in peer-to-peer opportunistic networks: Energy usage and response analysis. J Netw Comput Appl. 2016;66:137–50.

    Article  Google Scholar 

  30. Wang S, et al. The potential of mobile opportunistic networks for data disseminations. IEEE Trans Veh Technol. 2015;65(2):912–22.

    Article  Google Scholar 

  31. Wang S. Analyzing the potential of mobile opportunistic networks for big data applications. IEEE Netw. 2015;29(5):57–63.

    Article  Google Scholar 

  32. Pirozmand P, Guowei W, Jedari B, Xia F. Human mobility in opportunistic networks: characteristics, models and prediction methods. J Netw Comput Appl. 2014;42:45–58.

    Article  Google Scholar 

  33. Gebert J, et al. Management of opportunistic networks through cognitive functionalities, 9th Annual Conference on Wireless On-demand Network Systems and Services (WONS) 2012; 113–118

  34. Zhang P, et al. On denial of service attacks in software defined networks. IEEE Netw. 2016;30(6):28–33.

    Article  Google Scholar 

  35. Otto W, Offloading Delay Tolerant Data through Opportunistic Networks. In: Proceedings of MobiSys PhD Forum, Pages 23-24, 2015.

  36. Andreas G, et. al, Cognitive cloud-oriented wireless networks for the Future Internet, Workshop on Wireless Cloud and White Space Oriented Networks WCNC, 2012.

  37. Sood K, Shui Y, Xiang Y. Software-defined wireless networking opportunities and challenges for internet-of-things: a review. IEEE Internet Things J. 2016;3(4):453–63.

    Article  Google Scholar 

  38. Li Y, et al. A SDN-based architecture for horizontal Internet of Things services. In: IEEE International Conference on Communications (ICC) 2016.

  39. Savarese G, Vaser M, Ruggieri M, A Software Defined Networking-based context-aware framework combining 4G cellular networks with M2M. In: 16th International Symposium on Wireless Personal Multimedia Communications (WPMC), 2013.

  40. Olivier F, et. al, SDN Based Architecture for IoT and Improvement of the Security. In: IEEE 29th International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2015.

  41. Giupponi L, et. al,Joint radio resource management algorithm for multi-RAT networks. In: IEEE Global Telecommunications Conference, GLOBECOM ’05.

  42. Chenn-Jung H, et. al, Adaptive Resource Reservation Schemes for Multimedia Handoffs in Fourth-Generation Mobile Communications System, ICICS 2005.

  43. Emmanuel P, et. al, Robust Interference Identification for Multi-RAT Optimization in Wireless Cellular Networks. In: IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN),2012.

  44. Xu C, et. al., Predicting a User’s Next Cell With Supervised Learning Based on Channel States. In: IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2013.

  45. Lawal MB, et. al, Application of Q-Learning for RACH Access to Support M2M Traffic over a Cellular Network, European Wireless 2014.

  46. Alberto T, et. al, A Machine Learning Approach to QoE-based Video Admission Control and Resource Allocation in Wireless Systems. In: 13th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET), 2014.

  47. NGMN, NGMN Recommendation on SON and O&M Requirements, www.ngmn.org.

  48. Wu J, Zhang Z, Hong Y, Wen Y. Cloud Radio Access Network (C-RAN): A Primer. IEEE Network 2015; 29(1).

  49. Das D, Das D. A novel UE centric multi-RAT deployment model. In: International Conference On Smart Technologies For Smart Nation (SmartTechCon) 2017.

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Acknowledgements

The research work is funded by Ministry of Electronics and Information Technology (Meity) and Cognizant Technologies Ltd. under COPAS project.

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  1. International Institute of Information Technology, Bangalore (IIIT-Bangalore), 26/C, Electronics City, Bangalore, 560100, India

    Dibakar Das, Jyotsna Bapat & Debabrata Das

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  1. Dibakar Das
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  2. Jyotsna Bapat
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Correspondence to Dibakar Das.

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This article is part of the topical collection “Emerging Technologies for 5G and Beyond” guest edited by Aloknath De.

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Das, D., Bapat, J. & Das, D. Unsupervised Learning Based Capacity Augmentation in SDN Assisted Wireless Networks. SN COMPUT. SCI. 1, 230 (2020). https://doi.org/10.1007/s42979-020-00233-9

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