Optimized Load Balancing Scheme to Enhance the Efficiency of the WLAN

doi:10.23940/ijpe.24.06.p6.391399

Abstract

Abstract: Network traffic and load are only expected to increase. Data is lost while being sent between nodes and devices as a result of traffic congestion at bottlenecks. WLANs are becoming the go-to technology for access in hotspot locations, workplaces, and residences. A station in a wireless local area network may be able to connect to several access points (APs). So, deciding which AP is "optimum" from a selection of potential candidates is a pertinent question. The user merely associates the AP with the strongest received signal strength while using IEEE 802.11. But this could lead to a substantial load imbalance among a number of APs. With only a minor modification to the load distribution mechanism, adopting load balancing can improve efficiency and utilization in addition to reducing processing delays. To increase the effectiveness of the WLAN, we provide a dynamic load balancing strategy in this paper. The network and user overhead will be reduced by the suggested dynamic strategy. The suggested method utilizes network load and throughput as performance criteria for performing load balancing. The method's viability is checked using Riverbed Modeller Simulator. The experimental findings validate the effectiveness of the proposed technique in enhancing network throughput and reducing access point congestion.

Key words: access points, load balancing, bandwidth, WLAN, throughput

Simarjit Singh Malhi, Raj Kumar, and Amardev Singh. Optimized Load Balancing Scheme to Enhance the Efficiency of the WLAN [J]. Int J Performability Eng, 2024, 20(6): 391-399.

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References

[1] Velayos, H. and Karlsson, G., 2004, June. Techniques to reduce the IEEE 802.11 b handoff time. In 2004 IEEE international conference on communications (IEEE Cat. No. 04CH37577) (Vol. 7, pp. 3844-3848). IEEE.
[2] Brickley O., Rea S. and Pesch D., 2005, May. Load balancing for qos optimisation in wireless lans utilising advanced cell breathing techniques. In 2005 IEEE 61st Vehicular Technology Conference (Vol. 3, pp. 2105-2109). IEEE.
[3] Gong, H. and Kim, J., 2008. Dynamic load balancing through association control of mobile users in WiFi networks. IEEE Transactions on Consumer Electronics, 54(2), pp.342-348.
[4] Abusubaih M., Wiethoelter S., Gross J. and Wolisz A., 2008. A new access point selection policy for multi-rate IEEE 802.11 WLANs. International Journal of Parallel, Emergent and Distributed Systems, 23(4), pp.291-307.
[5] Scully, T. and Brown, K.N., 2008, December. Wireless LAN load-balancing with genetic algorithms. In International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 3-16). London: Springer London.
[6] Bejerano, Y. and Han, S.J., 2009. Cell breathing techniques for load balancing in wireless LANs. IEEE transactions on Mobile Computing, 8(6), pp.735-749.
[7] Ye X., Lv K., Wang R. and Sun L., 2011, August. Adaptive Load-Balanced Routing Algorithm. In 2011 Second International Conference on Digital Manufacturing & Automation (pp. 155-158). IEEE.
[8] Xue G., He Q., Zhu H., He T. and Liu Y., 2012. Sociality-aware access point selection in enterprise wireless lans. IEEE Transactions on Parallel and Distributed Systems, 24(10), pp.2069-2078.
[9] Suresh L., Schulz-Zander J., Merz R., Feldmann A. and Vazao T., 2012, August. Towards programmable enterprise WLANS with Odin. In Proceedings of the first workshop on Hot topics in software defined networks (pp. 115-120).
[10] Xu S., Ren F., Xu Y., Lin C. and Yao M., 2013, June. Selecting a preferable access point with more available bandwidth. In 2013 IEEE International Conference on Communications (ICC)(pp. 6311-6316). IEEE.
[11] Hou R., Li J., Sheng M. and Yang C., 2014. Access point selection in heterogeneous wireless networks using belief propagation. Science China Information Sciences, 57, pp.1-10.
[12] Krishan, R. and Laxmi, V., 2015. IEEE 802.11 WLAN load balancing for network performance enhancement. Procedia Computer Science, 57, pp.493-499.
[13] Bhuwania A., Subba P. and Roy U.K., 2016, September. Positioning wifi access points using particle swarm optimization. In 2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)(pp. 112-115). IEEE.
[14] Alkadeki H., Wang X. and Odetayo M., 2016. Improving performance of IEEE 802.11 by a dynamic control backoff algorithm under unsaturated traffic loads. arXiv preprint arXiv:1601.00122.
[15] Luong P., Nguyen T.M. and Le L.B., 2016. Throughput analysis for coexisting IEEE 802.15. 4 and 802.11 networks under unsaturated traffic. EURASIP Journal on Wireless Communications and Networking, 2016, pp.1-14.
[16] Manzoor S., Karmon P., Hei X. and Cheng W., 2020, May. Traffic aware load balancing in software defined WiFi networks for healthcare. In 2020 Information Communication Technologies Conference (ICTC)(pp. 81-85). IEEE.
[17] Duong T.V.T.,2021. Load balancing routing under constraints of quality of transmission in mesh wireless network based on software defined networking. Journal of Communications and Networks, 23(1), pp.12-22.