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WiMCA: multi-indicator client association in software-defined Wi-Fi networks

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Abstract

In a world with increasing traffic demands, wireless technologies aim to meet them by means of new Radio Access Technologies that provide faster connectivity. Such is the case of 4G and 5G. However, in indoor scenarios, where the capabilities of these technologies are significantly affected by the distance to the base station and the materials used in the construction of buildings, Wi-Fi is still the technology of reference thanks to its low cost and easy deployment. In this context, it is usual to find multi-AP Wi-Fi networks whose deployment has been carefully planned. However, the user-AP association decision procedure is not defined by the IEEE 802.11 standard. As a result, vendors choose selfish approaches based on signal strength. This leads to uneven user distributions and nonoptimal resource utilization. To deal with this, densification has been used over the years, but this is expensive as it needs more infrastructure. Moreover, this results in more APs in the same collision domain. To avoid the need for densification, in this paper we introduce WiMCA, a joint SDN-based user association and channel assignment solution for Wi-Fi networks that considers signal strength, channel occupancy and AP load to make better association decisions. Experimental results have demonstrated that, in terms of aggregated goodput, WiMCA outperforms approaches based on signal strength by 55%, providing better user level fairness and accommodating more users and traffic before reaching the point at which densification is needed.

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References

  1. Balachandran, A., Voelker, G. M., Bahl, P., & Rangan, P. V. (2002) Characterizing user behavior and network performance in a public wireless LAN. In Proceedings of ACM SIGMETRICS performance evaluation review, Marina Del Rey, California (Vol. 30, pp. 195–205).

  2. Balazinska, M., & Castro, P. (2003). Characterizing mobility and network usage in a corporate wireless local-area network. In Proceedings of ACM international conference on mobile systems, applications, and services (MobiSys), San Francisco, California, USA.

  3. Kreutz, D., Ramos, F. M., Verissimo, P. E., Rothenberg, C. E., Azodolmolky, S., & Uhlig, S. (2014). Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103(1), 14–76.

    Article  Google Scholar 

  4. McKeown, N., Anderson, T., Balakrishnan, H., Parulkar, G., Peterson, L., Rexford, J., et al. (2008). OpenFlow: Enabling innovation in campus networks. SIGCOMM Computer Communication Review, 38(2), 69–74.

    Article  Google Scholar 

  5. SENSE, FBK . (2020). The 5G-EmPOWER wiki. https://github.com/5g-empower/5g-empower.github.io/wiki. Accessed on June 18, 2020.

  6. Suresh, L., Schulz-Zander, J., Merz, R., Feldmann, A., & Vazao, T. (2012). Towards programmable enterprise WLANS with Odin. In Proceedings of the first workshop on Hot topics in software defined networks, Association for Computing Machinery, New York, NY, USA, HotSDN ’12 (pp. 115–120).

  7. Gómez, B., Coronado, E., Villalón, J., Riggio, R., & Garrido, A. (2020). User association in software-defined wi-fi networks for enhanced resource allocation. In Proceedings of IEEE Wireless communications and networking conference (WCNC), Seoul, South Korea.

  8. Gong, D., & Yang, Y. (2012). AP association in 802.11n WLANs with heterogeneous clients. In Proceedings of IEEE international conference on computer communications (INFOCOM).

  9. Coronado, E., Riggio, R., Villalón, J., & Garrido, A. (2018). Wi-balance: channel-aware user association in software-defined Wi-Fi networks. In Proceedings of IEEE/IFIP network operations and management symposium (NOMS), Taipei, Taiwan.

  10. Sun, L., Wang, L., Qin, Z., Yuan, Z., & Chen, Y. (2018). A novel on-line association algorithm for supporting load balancing in multiple-AP wireless LAN. Mobile Networks and Applications, 23, 1–12.

    Article  Google Scholar 

  11. Sheu, S. T., & Wu, C. C. (1999). Dynamic load balance algorithm (DLBA) for IEEE 802.11 wireless LAN. Journal of Applied Science and Engineering, 2(1), 45–52.

    Google Scholar 

  12. Yen, L. H., & Yeh, T. T. (2006). SNMP-based approach to load distribution in IEEE 802.11 networks. In Proceedings of IEEE vehicular technology conference (VTC), Melbourne, Vic., Australia.

  13. Bejerano, Y., Han, S. J., & Li, L. E. (2004). Fairness and load balancing in wireless LANs using association control. In Proceedings of international conference on mobile computing and networking, Philadelphia, PA, USA.

  14. Yang, L., Cui, Y., Tang, H., & Xiao, S. (2015). Demand-aware load balancing in wireless lans using association control. In Proceedings of IEEE global communications conference, San Diego, CA, USA.

  15. De Schepper, T., Latré, S., & Famaey, J. (2017). A transparent load balancing algorithm for heterogeneous local area networks. In Proceedings of IFIP/IEEE international symposium on integrated network management (IM), Lisbon, Portugal.

  16. Lei, T., Wen, X., Lu, Z., & Li, Y. (2017). A semi-matching based load balancing scheme for dense IEEE 802.11 WLANs. IEEE Access, 5, 15332–15339.

    Article  Google Scholar 

  17. Feng, J., Zhao, L., Chen, C., Ren, Z., & Du, J. (2016). User-oriented load balance in software-defined campus WLANS. In Proceedings of IEEE vehicular technology conference (VTC), Nanjing, China.

  18. Abusubaih, M., Gross, J., Wiethoelter, S., & Wolisz, A. (2006). On access point selection in IEEE 802.11 wireless local area networks. In Proceedings of IEEE conference on local computer networks (LCN), Tampa, FL, USA.

  19. Papaoulakis, N., Patrikakis, C., Stefanoudaki, C., Sipsas, P., & Voulodimos, A. (2011). Load balancing through terminal based dynamic AP reselection for QoS in IEEE 802.11 networks. In Proceedings of IEEE conference on pervasive computing and communications (PerCom), Seattle, WA, USA.

  20. Collotta, M., Pau, G., Salerno, V.M., & Scatà, G. (2012). A distributed load balancing approach for industrial IEEE 802.11 wireless networks. In Proceedings of IEEE international conference on emerging technologies and factory automation, Krakow, Poland.

  21. Garcia, E., Vidal, R., & Paradells, J. (2008). Cooperative load balancing in IEEE 802.11 networks with cell breathing. In Proceedings of IEEE symposium on computers and communications (ISCC), Marrakech, Morocco.

  22. Lin, C. Y., Tsai, W. P., Tsai, M. H., & Cai, Y. Z. (2017). Adaptive load-balancing scheme through wireless SDN-based association control. In Proceedings of IEEE international conference on advanced information networking and applications (AINA), Taipei, Taiwan.

  23. Wang, S., Huang, J., Cheng, X., & Chen, B. (2014). Coverage adjustment for load balancing with an AP service availability guarantee in WLANs. Wireless Networks, 20(3), 475–491.

    Article  Google Scholar 

  24. Chan, Y. C., & Lin, D. J. (2014). The design of an ap-based handoff scheme for IEEE 802.11 WLANs. International Journal of e-Education, e-Business, e-Management and e-Learning, 4(1), 72.

    Google Scholar 

  25. Berezin, M. E., Rousseau, F., & Duda, A. (2011). Multichannel virtual access points for seamless handoffs in IEEE 802.11 wireless networks. In Proceedings of IEEE vehicular technology conference (VTC), Yokohama, Japan.

  26. Suresh, L., Schulz-Zander, J., Merz, R., Feldmann, A., & Vazao, T. (2012). Towards programmable enterprise WLANS with Odin. In ACM hot topics in software defined networking (HotSDN), Helsinki, Finland.

  27. Rangisetti, A. K., Baldaniya, H. B., Kumar, P., & Tamma, B. R. (2014). Load-aware hand-offs in software defined wireless LANs. In Proceedings of IEEE international conference on wireless and mobile computing, networking and communications (WiMob), Larnaca, Cyprus.

  28. Zeljković, E., Slamnik-Kriještorac, N., Latré, S., & Marquez-Barja, J. M. (2019). ABRAHAM: Machine learning backed proactive handover algorithm using SDN. IEEE Transactions on Network and Service Management, 16(4), 1522–1536.

    Article  Google Scholar 

  29. Jabri, I., Krommenacker, N., Divoux, T., & Soudani, A. (2008). IEEE 802.11 Load balancing: an approach for QoS Enhancement. International Journal of Wireless Information Networks, 15(1), 16–30.

    Article  Google Scholar 

  30. de Sa, F.R., da Cunha, A. M., & Cesar, C. d. A. C. (2017). Effective AP association in SDWN based on signal strength and occupancy rate. In Proceedings of IEEE conference on local computer networks (LCN), Singapore.

  31. Raschellà, A., Bouhafs, F., Mackay, M., Zachariou, K., Pilavakis, V., & Georgiades, M. (2018). Smart access point selection for dense WLANs: A use-case. In Proceedings of IEEE wireless communications and networking conference (WCNC), Barcelona, Spain.

  32. Chen, J., Liu, B., Zhou, H., Yu, Q., Gui, L., & Shen, X. (2017). QoS-driven efficient client association in high-density software-defined WLAN. IEEE Transactions on Vehicular Technology, 66(8), 7372–7383.

    Article  Google Scholar 

  33. Kiran, N., Changchuan, Y., & Akram, Z. (2016). AP load balance based handover in software defined WiFi systems. In: Proceedings of IEEE international conference on network infrastructure and digital content (IC-NIDC), Beijing, China.

  34. Sood, K., Liu, S., Yu, S., & Xiang, Y. (2015). Dynamic access point association using software defined networking. In Proceedings of international telecommunication networks and applications conference (ITNAC), Sydney, NSW, Australia

  35. Lin, Y. D., Wang, C. C., Lu, Y. J., Lai, Y. C., & Yang, H. C. (2018). Two-tier dynamic load balancing in SDN-enabled Wi-Fi networks. Wireless Networks, 24(8), 2811–2823.

    Article  Google Scholar 

  36. Han, Y., Yang, K., Lu, X., & Zhou, D. (2016). An adaptive load balancing application for software-defined enterprise WLANs. In Proceedings of international conference on ICT convergence, Jeju, South Korea.

  37. Cao, F., Zhong, Z., Fan, Z., Sooriyabandara, M., Armour, S., & Ganesh, A. (2016). User association for load balancing with uneven user distribution in IEEE 802.11ax networks. In: Proceedings of IEEE consumer communications & networking conference (CCNC), Las Vegas, NV, USA.

  38. Jeunen, O., Bosch, P., Van Herwegen, M., Van Doorselaer, K., Godman, N., & Latré, S. (2018). A machine learning approach for IEEE 802.11 channel allocation. In: Proceedings of IEEE international conference on network and service management (CNSM), Rome, Italy.

  39. Oh, H. S., Jeong, D. G., & Jeon, W. S. (2020). Joint radio resource management of channel-assignment and user-association for load balancing in dense WLAN environment. IEEE Access, 8, 69615–69628.

    Article  Google Scholar 

  40. Kundakcioglu, O. E., & Alizamir, S. (2009). Encyclopedia of optimization, generalized assignment problem (pp. 1153–1162). Berlin: Springer.

    Book  Google Scholar 

  41. Xia, D., Hart, J., & Fu, Q. (2013). Evaluation of the Minstrel rate adaptation algorithm in IEEE 802.11g WLANs. In IEEE international conference on communications (ICC) (pp. 2223–2228). IEEE.

  42. Riggio, R., Marina, M. K., Schulz-Zander, J., Kuklinski, S., & Rasheed, T. (2015). Programming abstractions for software-defined wireless networks. IEEE Transactions on Network and Service Management, 12(2), 146–162.

    Article  Google Scholar 

  43. (2008) Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 1: Radio Resource Measurement of Wireless LANs. IEEE Std 80211k-2008 (Amendment to IEEE Std 80211-2007) (pp. 1–244).

  44. Sanchez, M. I., & Boukerche, A. (2016). On IEEE 802.11 K/R/V amendments: Do they have a real impact? IEEE Wireless Communications, 23(1), 48–55.

    Article  Google Scholar 

  45. Endt, T., Brown, R., & Bursi, A. (2020). OpenWRT project. https://openwrt.org/es/start. Accessed on June 17, 2020.

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Authors and Affiliations

  1. High-Performance Networks and Architectures (RAAP), University of Castilla-La Mancha, Albacete, Spain

    Blas Gómez, José M. Villalón & Antonio Garrido

  2. i2CAT Foundation, Barcelona, Spain

    Estefanía Coronado

  3. RISE Research Institutes of Sweden AB, Stockholm, Sweden

    Roberto Riggio

Authors
  1. Blas Gómez
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  2. Estefanía Coronado
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  3. José M. Villalón
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  4. Roberto Riggio
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  5. Antonio Garrido
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Correspondence to Blas Gómez.

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Gómez, B., Coronado, E., Villalón, J.M. et al. WiMCA: multi-indicator client association in software-defined Wi-Fi networks. Wireless Netw 27, 3109–3125 (2021). https://doi.org/10.1007/s11276-021-02636-9

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