Skip to main content
Log in

Increasing quality of service in wireless local area networks through fuzzy logic based feedback control method

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

In many studies aimed at improving the quality of service (QoS) in wireless local area networks (WLAN), the performance was improved by applying non-ACK model, physical interference model, analytical models etc. with using optimization algorithms with stable simulations. For efficient QoS in WLANs, performance is generally increased using 802.11n medium access control protocol through request to send threshold value (RSTV), fragmentation threshold value (FTV) and buffer size (BS) parameters. While the RSTV activates the request to send/clear to send (RTS/CTS) mechanism in carrier sense multiple access with collision avoidance (CSMA/CA) protocol, FTV fragments the larger packets during CSMA/CA transmission, reducing the packet loss in WLANs. BS, however, is the memory used in the CSMA/CA model to reduce cost. In previous studies, most of the new model applied in process layers by using Riverbed Modeler simulation tool and ideal input values had been obtained for RSTV, FTV and BS during the simulation stable. In this study, a new model proposed for CSMA/CA protocol on the process layers which used feedback controlled method with fuzzy logic to improve QoS during the simulation. Values obtained with nearly six million different test results, has been revealed that throughput was increased by 26.48%, the channel utilization by 2.30%, the data traffic received by 14.59% and the data traffic sent by 17.06% respectively. While RSTV, FTV and BS input parameters are optimized with the help of the feedback controlled algorithm used in these studies, the effects of external parameters such as number of nodes, inter-arrival time, transmitting power etc. on the performance improvements in the model are demonstrated in graphics. All these test results have shown that the new model provides a high rate of performance improvement for WLANs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14.
Fig. 15

Similar content being viewed by others

References

  1. Singh, H., Singh, T., & Kaur, M. (2014). Improving the quality of service of EDCF over DCF for real time applications using probability algorithm. IJARCCE, 3–4, 6330–6333.

    Google Scholar 

  2. Dalvi, A., Svamy, P., & Meshram, B.B. (2011). DCF improvement for satisfactory throughput of 802.11 WLAN, IJCSE, 3–7 (pp. 2862–2868).

  3. Borsuk, B., & Koçak, C. (2016). RTS/CTS mechanism’s effect on performance in multimedia applications when hidden node problem occurs on wireless networks. International Journal Of Informatics Technologies, 9–2, 187–195.

    Google Scholar 

  4. Yun, J.H., & Seo, S.W. (2007). Novel collision detection scheme and its applications for IEEE 802.11 wireless LANs. Computer Communication, 30, 1350–1366.

  5. Kocak, C., & Karakurt, H. B. (2019). Fuzzy logic-based performance improvement on MAC layer in wireless local area networks. Neural Computing and Applications, 31, 6113–6128.

    Article  Google Scholar 

  6. Kocak, C., & Karakurt, H. B. (2019). Data traffic optimization in wireless local area networks with artificial neural networks. Journal Of Polytechnic, 22–3, 737–747.

    Google Scholar 

  7. Malik, S., Chaudhary, R., Pathak, A., Chakraborty, S.P. (2015). Modeling and analysis of IEEE 802.11 DCF MAC. In 3rd international conference on recent trends in computing. Procedia computer science (Vol. 57, pp. 473–482).

  8. Choi, S., Prado, J. D., Shankar, N. S., & Mangold, S. (2003) IEEE 802.11e contention-based channel access (EDCF) performance evaluation. In IEEE International Conference on Communications, 2003. ICC '03. IEEE (2003). https://doi.org/10.1109/ICC.2003.1204546.

  9. Kaur, I., Bala, M., & Bajaj, H. (2012). Performance evaluation of WLAN by varying PCF, DCF and enhanced DCF slots to improve quality of service. IOSRJCE, 2–5, 29–33.

    Article  Google Scholar 

  10. Karakurt, H. B., & Kocak, C. (2015). On wireless network PCF, DCF and EDCF with fragmentation threshold. In XVII. Academic Informatics Conference Eskisehir/Turkey

  11. Sidelnikov, A., Yu, J., & Choi, S. (2006). Fragmentation/Aggregation scheme for throughput enhancement of IEEE 802.11n WLAN. In Proceedings of IEEE APWCS.

  12. Preveze, B. (2011). Cognitive methods in multimedia communications. Dissertation, University of Baskent

  13. Prithi, S., & Sumathi, S. (2020). LD2FA-PSO: a novel learning dynamic deterministic finite automata with PSO algorithm for secured energy efficient routing in Wireless Sensor Network. Ad Hoc Networks, 97, 1–14.

    Article  Google Scholar 

  14. Poornima, G. A., & Paramasivan, B. (2020). Anomaly detection in wireless sensor network using machine learning algorithm. Computer Communications, 151, 331–337.

  15. Fanian, F., & Rafsanjani, M. K. (2020). A new fuzzy multi-hop clustering protocol with automatic rule tuning for wireless sensor networks. Applied Soft Computing, 89, 1–24.

    Article  Google Scholar 

  16. Zhao, X. W., Hai, L. X., & Wei, D. (2008). A fuzzy logic cooperative MAC for MANET. The Journal Of China Universities Of Posts And Telecommunications, 15–1, 55–60.

    Google Scholar 

  17. Frantti, T., & Koivula, M. (2011). Fuzzy packet size control for delay sensitive traffic in ad hoc networks. Expert Systems with Applications, 38, 10188–10198.

    Article  Google Scholar 

  18. Collotta, M. (2015). FLBA: A fuzzy algorithm for load balancing in IEEE 802.11 network. Journal of Network and Computer Applications 53, 183–192.

  19. Yu, Y., Wang, T., & Liew, C. (2018). Deep-reinforcement learning multiple access for heterogeneous wireless networks. IEEE Journal On Selected Areas In Communications, 37–6, 1277–1290.

    Google Scholar 

  20. Bhattacharyya, R., Bura, A., Rengarajan, D., Rumuly, M., Shakkottai, S., Kalathil, D., Mok, R. K. P., Dhamdhere, A. (2019). QFlow: A reinforcement learning approach to high QoE video streaming over wireless networks. In ’19 Proceedings of the twentieth ACM international symposium on mobile ad hoc networking and computing (pp. 251–260).

  21. Yang, J., You, X., Wu, G., Hassan, M. M., Almogren, A., & Guna, J. (2019). Application of reinforcement learning in UAV cluster task scheduling. Future Generation Computer Systems, 95, 140–148.

    Article  Google Scholar 

  22. Li, F., Chen, Y., Wang, J., Zhou, X., & Tang, B. (2019). A reinforcement learning unit matching recurrent neural network for the state trend prediction of rolling bearings. Measurement, 145, 191–203.

    Article  Google Scholar 

  23. You, C., Lu, J., Filev, D., & Tsiotras, P. (2019) Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning. Robotics and Autonomous Systems, 114, 1–18.

  24. Yao, H., Chen, X., Li, M., Zhang, P., & Wang, L. (2018). A novel reinforcement learning algorithm for virtual network embedding. Neurocomputing, 284, 1–19.

    Article  Google Scholar 

  25. Bandırmalı, N., Ertürk, İ., Çeken, C., & ve Bayılmış, C. (2008). Providing security service with skipjack encryption method for delay sensitive and energy aware wireless sensor networks. In Electrical and electronics and computer engineering symposium, ELECO’08 (pp. 152–157).

  26. Gamal, M., Sadek, N., Rizk, M. R. M., & Ahmed, M. A. E. (2020). Optimization and modeling of modified unslotted CSMA/CA for wireless sensor networks. Alexandria Engineering Journal, 59, 681–691.

    Article  Google Scholar 

  27. Mawlawi, B., Dore, J. B., Lebedev, N., & Gorce, J. M. (2014). Performance evaluation of multiband CSMA/CA with RTS/CTS for M2M communication with finite retransmission strategy. Procedia Computer Science, 40, 108–115.

    Article  Google Scholar 

  28. Mahmood, D., Khan, Z. A., Qasim, U., Naru, M. U., Mukhtar, S., Akram, M. I., & Javaid, N. (2014). Analyzing and evaluating contention access period of slotted CSMA/CA for IEEE802.15.4. Procedia Computer Science, 34, 204–115.

  29. Uddin, M. F. (2021). Downlink performance analysis of a CSMA based WLAN under physical interference model. Computer Networks, 196(108255), 1–18.

    Google Scholar 

  30. Touijer, B., Maissa, Y. B., & Mouline, S. (2021). IEEE 802.15.6 CSMA/CA access method for WBANs: Performance evaluation and new backoff counter selection procedure. Computer Networks, 188, 107759 pp. 1–17.

  31. Karakurt, H. B., & Kocak, C. (2015). Performance improvement with fragmentation threshold for the co-ordination functions by using wireless local area. Dissertation, University of Gazi.

  32. Isizoh, A. N., Anazia, A. E., Okide, S. O., Okwaraoka, C. A. P., & Onyeyili, T. I. (2013). Effects of different fragmentation thresholds on data dropped and retransmission attempts in a wireless local area network. IJERA, 3–2, 76–79.

    Google Scholar 

  33. Wang, Q., & Yuan, D. (2010). An adaptive backoff algorithm for IEEE 802.11 DCF with cross-layer optimization. In 2010 6th ınternational conference on. WiCOM. https://doi.org/10.1109/WICOM.2010.5601153

  34. Zhai, H., Kwon, Y., & Fang, Y. (2004). Performance analysis of IEEE 802.11 MAC protocols in wireless LANs. Wireless Communications and Mobile Computing 4, 917–931.

  35. Li, T. (2007). Improving performance for CSMA/CA based wireless networks. Dissertation, National University of Ireland.

  36. Malone, D., Clifford, P., & Douglas, J. L. (2006). On buffer sizing for voice in 802.11 WLANs. IEEE Communications Letters, 10–10, 701–703.

  37. Li, T., Leith, D., Malone, D. (2011). Buffer sizing for 802.11 based networks. IEEE/ACM Transactions on Networking, 156–169.

  38. Vadiati, M., Moghaddam, A. A., Nakhaei, M., Adamowski, J., & Akbarzadeh, A. H. (2016). A fuzzy-logic based decision-making approach for identification of groundwater quality based on groundwater quality indices. Journal of Environmental Management, 184, 255–270.

    Article  Google Scholar 

  39. Kustiawan, I., & Chi, K. H. (2015). Handoff decision using a kalman filter and fuzzy logic in heterogeneous wireless networks. IEEE Communications Letters, 19–12, 2258–2261.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hacı Bayram Karakurt.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karakurt, H.B., Koçak, C. Increasing quality of service in wireless local area networks through fuzzy logic based feedback control method. Wireless Netw 29, 2217–2233 (2023). https://doi.org/10.1007/s11276-023-03293-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-023-03293-w

Keywords

Navigation