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Quality of service improvement in fiber-wireless networks using a fuzzy-based nature-inspired algorithm

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Abstract

Fiber-Wireless (Fi-Wi) networks attract interest as a dependable communication backbone in several additional applications. The cheap cost, dependability, accessibility of Wireless Sensor Networks, and optical fiber networks’ high bandwidth and reliability contribute to it. Although Fi-Wi networks supply good delay performance, they may not be able to fulfill the needs of the smart grid delay-critical applications mentioned earlier. Since the Quality of Service (QoS) is a prerequisite for deploying high-speed Fi-Wi broadband access networks, it is critical to identify and address common issues (such as bandwidth and latency) in high-speed next-generation networks. This study offers a unique strategy for improving QoS in Fi-Wi networks based on a fuzzy-based Cat Swarm Optimization (CSO) algorithm. CSO is a strong metaheuristic swarm-based optimization technique that has garnered a lot of favorable comments since its inception. CSO is a resilient and strong meta-heuristic algorithm based on cat behavior. It has two search modes: seeking and tracing, which may be combined using the mixing ratio parameter. The suggested solution reduces energy usage, packet loss, and latency, according to simulation findings using the MATLAB program. Compared to previous methods, simulations reveal that the suggested method produces relatively excellent outcomes and has a higher performance.

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References

  1. Mohammadani, K., et al.: Highest cost first-based Qos mapping scheme for fiber wireless architecture. Photonics 7, 114 (2020)

    Article  Google Scholar 

  2. Guo, H., Liu, J.: Collaborative computation offloading for multiaccess edge computing over fiber–wireless networks. IEEE Trans. Veh. Technol. 67(5), 4514–4526 (2018)

    Article  Google Scholar 

  3. Alvarado, A., et al.: Achievable information rates for fiber optics: Applications and computations. J. Lightwave Technol. 36(2), 424–439 (2017)

    Article  Google Scholar 

  4. Dang, B.L., et al.: Radio-over-Fiber based architecture for seamless wireless indoor communication in the 60 GHz band. Comput. Commun. 30(18), 3598–3613 (2007)

    Article  Google Scholar 

  5. Liu, Y., et al.: Load balanced optical network unit (ONU) placement in cost-efficient fiber-wireless (FiWi) access network. Optik 124(20), 4594–4601 (2013)

    Article  Google Scholar 

  6. Naghshvarianjahromi, M., Kumar, S., Deen, M.J.: Brain inspired dynamic system for the quality of service control over the long-haul nonlinear fiber-optic link. Sensors 19(9), 2175 (2019)

    Article  Google Scholar 

  7. Mali, N.N., Jog, V.V.: QoS control in FiWi access network. Int J Emerging Eng Res Technol 3, 53–60 (2015)

    Google Scholar 

  8. Song, X., et al.: Nodes deployment optimization algorithm based on improved evidence theory of underwater wireless sensor networks. Photon Netw. Commun. 37(2), 224–232 (2019)

    Article  Google Scholar 

  9. Shang, F., et al.: Research on the intrusion detection model based on improved cumulative summation and evidence theory for wireless sensor network. Photon Netw. Commun. 37(2), 212–223 (2019)

    Article  Google Scholar 

  10. Siqueira, H., et al.: Simplified binary cat swarm optimization. Integr. Comput. Aided Eng. 28(1), 35–50 (2021)

    Article  Google Scholar 

  11. Bahrami, M., Bozorg-Haddad, O., Chu, X.: Cat swarm optimization (CSO) algorithm. In: Advanced Optimization by Nature-Inspired Algorithms, pp. 9–18. Springer (2018)

    Google Scholar 

  12. Kumar, Y., Singh, P.K.: Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering. Appl. Intell. 48(9), 2681–2697 (2018)

    Article  Google Scholar 

  13. Emami, H., Alipour, M.M.: Chaotic local search-based levy flight distribution algorithm for optimizing ONU placement in fiber-wireless access network. Opt. Fiber Technol. 67, 102733 (2021)

    Article  Google Scholar 

  14. Zhang, H., et al.: Energy efficient frame aggregation scheme in IoT over fiber-wireless networks. IEEE Internet Things J. 8(13), 10779–10791 (2021)

    Article  Google Scholar 

  15. Xu, S., et al.: QoS-aware cross-domain collaborative energy-saving mechanism for FiWi virtual networks. Int. J. Network Manage 30(2), e2095 (2020)

    Article  Google Scholar 

  16. Mouhassine, N., Moughit, M., Laassiri F.: Improving the quality of service of voice over IP in wireless sensor networks by centralizing handover management and authentication using the SDN controller. In 2019 third international conference on intelligent computing in data sciences (ICDS). 2019. IEEE

  17. Inga, E., et al.: Optimal deployment of FiWi networks using heuristic method for integration microgrids with smart metering. Sensors 18(8), 2724 (2018)

    Article  Google Scholar 

  18. Singh, P., Prakash, S.: Optical network unit placement in fiber-wireless (FiWi) access network by Moth-Flame optimization algorithm. Opt. Fiber Technol. 36, 403–411 (2017)

    Article  Google Scholar 

  19. Alarifi, A., et al.: A big data approach to sentiment analysis using greedy feature selection with cat swarm optimization-based long short-term memory neural networks. J. Supercomput. 76(6), 4414–4429 (2020)

    Article  Google Scholar 

  20. Durmus, A., Kurban, R.: Optimal synthesis of concentric circular antenna arrays using political optimizer. IETE J. Res. 68, 768–777 (2021)

    Article  Google Scholar 

  21. Neghabi, A.A., et al.: Energy-aware dynamic-link load balancing method for a software-defined network using a multi-objective artificial bee colony algorithm and genetic operators. IET Commun. 14(18), 3284–3293 (2020)

    Article  Google Scholar 

  22. Chandirasekaran, D., Jayabarathi, T.: Cat swarm algorithm in wireless sensor networks for optimized cluster head selection: a real time approach. Clust. Comput. 22(5), 11351–11361 (2019)

    Article  Google Scholar 

  23. Zhang, Y.-D., et al.: Cat swarm optimization applied to alcohol use disorder identification. Multimed. Tools Appl. 77(17), 22875–22896 (2018)

    Article  Google Scholar 

  24. Gabi, D., et al.: Cloud customers service selection scheme based on improved conventional cat swarm optimization. Neural Comput. Appl. (Print) 32, 14817–14838 (2020)

    Article  Google Scholar 

  25. Du, Y., Wang, J., Lei, L.: Multi-objective scheduling of cloud manufacturing resources through the integration of cat swarm optimization and firefly algorithm. Adv. Prod. Eng. Manag. 14(3), 333 (2019)

    Google Scholar 

  26. Khodaei, H., et al.: Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Appl. Therm. Eng. 137, 395–405 (2018)

    Article  Google Scholar 

  27. Hosseini Firouz, M., Ghadimi, N.: Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods. Complexity 21(6), 70–88 (2016)

    Article  MathSciNet  Google Scholar 

  28. Fadlullah, Z.M., et al.: Smart FiWi networks: challenges and solutions for QoS and green communications. IEEE Intell. Syst. 28(2), 86–91 (2013)

    Article  Google Scholar 

  29. Baliga, J., et al.: Energy consumption in wired and wireless access networks. IEEE Commun. Mag. 49(6), 70–77 (2011)

    Article  Google Scholar 

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Li, Y. Quality of service improvement in fiber-wireless networks using a fuzzy-based nature-inspired algorithm. Photon Netw Commun 44, 82–89 (2022). https://doi.org/10.1007/s11107-022-00982-y

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  • DOI: https://doi.org/10.1007/s11107-022-00982-y

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