Adaptive Clustering Strategies for 5G-IoT Wireless Sensor Networks: Insights into Neuro-Fuzzy Approaches

Volume: 11 | Issue: 1 | Year 2025 | Subscription
International Journal of Microwave Engineering and Technology
Received Date: 01/10/2025
Acceptance Date: 01/20/2025
Published On: 2025-02-27
First Page: 33
Last Page: 55

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By: Rajeev Kumar Singh, Dr Shruti Dixit, and Prof Aaradhna Soni

1.Rajeev Kumar Singh,Student, Department of Electronics & Telecommunication Engineering, Sanjeev Agrawal Global Educational (SAGE) University Bhopal, Madhya Pradesh, India.
2.Dr. Shruti Dixit,Associate Professor, Department of Electronics & Telecommunication Engineering, Sanjeev Agrawal Global Educational (SAGE) University Bhopal, Madhya Pradesh, India.
3.Prof. Aaradhna Soni,Associate Professor, Sanjeev Agrawal Global Educational (SAGE) University Bhopal, Madhya Pradesh, India

Abstract

The new cellular network standard is 5G. The Internet of Things, or IoT, is growing and changing quickly. Massive cellular bandwidth increases brought about by 5G make it much simpler for the Internet of Things to connect many devices. Current LTE networks will be ten times slower than 5G. This increase in speed will allow IoT devices to communicate and share data faster than ever. In this research, we have explored numerous investigations identified with routing protocols. After the examination finished, various multicast routing protocols are applied in the node-to-node simulation scenario. Besides, video web based is another moving errand to accomplish in IOT. The main objective of this theory was to dedicate to the development of IOT routing protocols for urban environments. Routing protocols in IOTs use multi-bounce node correspondences in the situation under examination to send the data produced by the nodes to monitor foci. The goal of this postulation is to improve the overall performance and usefulness of IOT routing protocols for disclosing administration. We began our examination by breaking down a few significant recreation parts of an IOT to ensure dependable outcomes utilizing our reasonable reenactment system. An effective routing strategy to boost IOT node performance for 5G communication applications is presented in this research. Achieved end to end delay is 0.003 sec while previously it is 0.01Sec. The total number of nodes is 100. The total simulation time is 2 minutes 20 sec while previously it is 4 minutes 39 sec. The energy consumption by the node is 0.1J in proposed while it is 0.3J in previous. As a result, it can be said that the suggested work outperforms earlier efforts. The suggested algorithm is based on neuro-fuzzy, whereas the previous one was based on adaptive approach, according to simulation results. According to simulation data, the suggested method performs noticeably better than the current one.

Keywords-IoT, 5G, efficiency, MATLAB, wireless, neuro-fuzzy, routing, network, delay, packet
delivery ratio

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Citation:

How to cite this article: Rajeev Kumar Singh, Dr Shruti Dixit, and Prof Aaradhna Soni, Adaptive Clustering Strategies for 5G-IoT Wireless Sensor Networks: Insights into Neuro-Fuzzy Approaches. International Journal of Microwave Engineering and Technology. 2025; 11(1): 33-55p.

How to cite this URL: Rajeev Kumar Singh, Dr Shruti Dixit, and Prof Aaradhna Soni, Adaptive Clustering Strategies for 5G-IoT Wireless Sensor Networks: Insights into Neuro-Fuzzy Approaches. International Journal of Microwave Engineering and Technology. 2025; 11(1): 33-55p. Available from:https://journalspub.com/publication/ijmet/article=15481

Refrences:

  1. Lipare A, Edla DR, Dharavath R. Fuzzy rule generation using modified PSO for clustering in wireless sensor networks. IEEE Transactions on Green Communications and Networking. 2021 Feb 18;5(2):846-57. doi: 1109/TGCN.2021.3060324.
  2. Dagdeviren ZA. A Framework for Localization and Clustering in IoT Enabled Wireless Sensor and Actor Networks. In2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) 2021 Oct 21 (pp. 732-737). IEEE., doi: 10.1109/ISMSIT52890.2021.9604598.
  3. Verma S, Zeadally S, Kaur S, Sharma AK. Intelligent and secure clustering in wireless sensor network (WSN)-based intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems. 2021 Nov 16;23(8):13473-81. doi: 1109/TITS.2021.3124730.
  4. Tripathi K, Agarwal S. Energy Efficient Clustering Scheme to Improve the Lifespan of WSN. In2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO) 2021 Sep 3 (pp. 1-5). IEEE. doi: 1109/ICRITO51393.2021.9596195.
  5. Raslan AF, Ali AF, Darwish A, El-Sherbiny HM. An improved sunflower optimization algorithm for cluster head selection in the internet of things. IEEE Access. 2021 Nov 8;9:156171-86. doi: 10.1109/ACCESS.2021.3126537.
  6. Han Y, Li G, Xu R, Su J, Li J, Wen G. Clustering the wireless sensor networks: a meta-heuristic approach. IEEe Access. 2020 Nov 27;8:214551-64. doi: 10.1109/ACCESS.2020.3041118.
  7. Umbreen S, Shehzad D, Shafi N, Khan B, Habib U. An energy-efficient mobility-based cluster head selection for lifetime enhancement of wireless sensor networks. Ieee Access. 2020 Nov 13;8:207779-93. doi: 1109/ACCESS.2020.3038031.
  8. Tao Y, Zhang J, Yang L. An unequal clustering algorithm for wireless sensor networks based on interval type-2 TSK fuzzy logic theory. IEEE Access. 2020 Oct 29;8:197173-83. doi: 10.1109/ACCESS.2020.3034607.
  9. Wang J, Li S, Ge Y. Ions motion optimization-based clustering routing protocol for cognitive radio sensor network. IEEE Access. 2020 Oct 13;8:187766-82. doi: 10.1109/ACCESS.2020.3030808.
  10. Mohamed A, Saber W, Elnahry I, Hassanien AE. Coyote optimization based on a fuzzy logic algorithm for energy-efficiency in wireless sensor networks. IEEE Access. 2020 Oct 8;8:185816-29. doi: 1109/ACCESS.2020.3029683.
  11. Hossen MA, Yoo SJ. Q-learning based multi-objective clustering algorithm for cognitive radio ad hoc networks. IEEE Access. 2019 Dec 13;7:181959-71. doi: 10.1109/ACCESS.2019.2959313.
  12. He X, Fu X, Yang Y. Energy-efficient trajectory planning algorithm based on multi-objective PSO for the mobile sink in wireless sensor networks. Ieee Access. 2019 Dec 5;7:176204-17. doi: 1109/ACCESS.2019.2957834.
  13. Sharma V, You I, Palmieri F, Jayakody DN, Li J. Secure and energy-efficient handover in fog networks using blockchain-based DMM. IEEE Communications Magazine. 2018 May 17;56(5):22-31. doi: 1109/MCOM.2018.1700863.
  14. Al-Turjman FM, Imran M, Bakhsh ST. Energy efficiency perspectives of femtocells in Internet of Things: Recent advances and challenges. IEEE Access. 2017 Nov 15;5:26808-18. doi: 10.1109/ACCESS.2017.2773834.
  15. Saghezchi FB, Mantas G, Ribeiro J, Al-Rawi M, Mumtaz S, Rodriguez J. Towards a secure network architecture for smart grids in 5G era. In2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC) 2017 Jun 26 (pp. 121-126). IEEE. doi: 10.1109/IWCMC.2017.7986273.
  16. Szymanski TH. Securing the industrial-tactile Internet of Things with deterministic silicon photonics switches. IEEE Access. 2016 Sep 26;4:8236-49. doi: 10.1109/ACCESS.2016.2613512.
  17. Zhang D, Zhou Z, Mumtaz S, Rodriguez J, Sato T. One integrated energy efficiency proposal for 5G IoT communications. IEEE Internet of Things Journal. 2016 Aug 12;3(6):1346-54. doi: 1109/JIOT.2016.2599852.
  18. Jong Hyuk Park; Vincenzo Piuri; Hsiao-Hwa Chen; Yi Pan “Special Issue on Computational Intelligence for Communications and Sensing,” in IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 4, no. 1, pp. 1-4, Feb. 2020, doi: 10.1109/TETCI.2019.2963469.
  19. Park JH, Piuri V, Chen HH, Pan Y. Guest editorial special issue on advanced computational technologies in mobile edge computing for the internet of things. IEEE Internet of Things Journal. 2019 Jun 18;6(3):4742-3. doi: 10.1109/JIOT.2019.2921237.
  20. Hossain E. Editorial: Second Quarter 2016 IEEE Communications Surveys and Tutorials. IEEE Communications Surveys & Tutorials. 2016 Jun;18(2):899-904. doi: 10.1109/COMST.2016.2559959.
  21. Wu C, Ji Y, Liu F, Ohzahata S, Kato T. Toward practical and intelligent routing in vehicular ad hoc networks. IEEE Transactions on Vehicular Technology. 2015 Sep 23;64(12):5503-19., doi: 10.1109/TVT.2015.2481464.
  22. Tian D, Wang Y, Xia H, Cai F. Clustering multi‐hop information dissemination method in vehicular ad hoc networks. IET Intelligent Transport Systems. 2013 Dec;7(4):464-72., doi: 10.1049/iet-its.2011.0227.
  23. Chuang MC, Chen MC. DEEP: Density-aware emergency message extension protocol for VANETs. IEEE Transactions on Wireless Communications. 2013 Sep 9;12(10):4983-93. doi: 1109/TWC.2013.090413.121697.
  24. Yuste AJ, Trivino A, Casilari E, Trujillo FD. Adaptive gateway discovery for mobile ad hoc networks based on the characterisation of the link lifetime. IET communications. 2011 Oct 14;5(15):2241-9. doi: 1049/iet-com.2010.0692.
  25. Villalba LG, Cañas DR, Orozco AL. Bio-inspired routing protocol for mobile ad hoc networks. IET communications. 2010 Dec 17;4(18):2187-95. doi: 10.1049/iet-com.2009.0826.
  26. Huang F, Leung KC, Li VO. Transmission radius control in wireless ad hoc networks with smart antennas. IEEE transactions on communications. 2010 Jul 15;58(8):2356-70., doi: 1109/TCOMM.2010.062510.080600.
  27. Kim D, Yoo H. TCP performance improvement considering ACK loss in ad hoc networks. Journal of Communications and Networks. 2008 Mar;10(1):98-107. doi: 10.1109/JCN.2008.6388333.
  28. Abusalah L, Khokhar A, Guizani M. A survey of secure mobile ad hoc routing protocols. IEEE communications surveys & tutorials. 2009 Jan 6;10(4):78-93. doi: 1109/SURV.2008.080407.
  29. Xie J, Nandi S, Gupta AK, Das A. Gateway-based multicast protocol–a novel multicast protocol for mobile ad hoc networks. IEE Proceedings-Communications. 2005 Dec 9;152(6):811-20. doi: 10.1049/ip- com:20045207.
  30. Hwang RH, Li CY, Wang CY, Chen YS. Mobile IPv6-based ad hoc networks: Its development and application. IEEE Journal on Selected Areas in Communications. 2005 Oct 31;23(11):2161-71., doi: 10.1109/JSAC.2005.856826.

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