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An energy efficient fuzzy clustering-based congestion control algorithm for cognitive radio sensor networks

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Abstract

In wireless sensor networks, the expensive and scarce resources are energy and frequency spectrum. To overcome this scarcity of spectrum, cognitive radio has been introduced in WSNs. The licensed band is utilized by the primary users in cognitive radio whereas the secondary users can use the licensed channels. For improving the network lifetime, overall network scalability, and energy consumption, the clustering technique is used to group the sensor nodes into clusters. Clustering algorithms must be energy efficient because of the difficulties in replacing or recharging the batteries of nodes. While designing the algorithms, more constraints result in the clustering for CRSN as the dynamic spectrum access has been addressed. We propose an energy-efficient fuzzy clustering and congestion control algorithm (EFCCA) in this paper to improve energy efficiency. With the consideration of spectrum availability, queue length, and residual energy, the election of cluster head contributes to the energy efficiency of the algorithm. The active Queue Management algorithm is used to monitor and control the congestion rate. The proposed EFCCA’s performance evaluates with the help of comparison with other clustering methods and it shows enhanced performance in energy efficiency and lifetime based on the outcomes of experimental investigation.

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References

  1. Mirza Samiulla Beg, D., & Akhilesh, A. W. (2019). A Comprehensive Study in Wireless Sensor Network (WSN) using Artificial Bee Colony (ABC) algorithms. International Research Journal of Engineering and Technology, 6(9), 873–879.

    Google Scholar 

  2. Priya, N., & Pankajavalli, P. B. (2019). Review and future directions of fault tolerance schemes and applied techniques in wireless sensor networks. International Journal of Computational Science and Engineering, 7, 599–606.

    Google Scholar 

  3. Moon, B. (2017). Dynamic spectrum access for the internet of things service in cognitive radio-enabled LPWANs. Sensors, 17(12), 2818.

    Article  Google Scholar 

  4. Bhagat, N., & Malhotra, J., (2017). The issue and challenges in futirisyic development of coginitive radio networks. International Journal of Advanced Research in Computer Science, 8(7), 303–309.

    Article  Google Scholar 

  5. Nayyar, A., Nguyen, B. L., & Nguyen, N. G. (2020). The internet of drone things (IoDT): Future envisions of smart drones. In: First international conference on sustainable technologies for computational intelligence (pp. 563–580). Springer, Singapore

  6. Sahoo, B. M., Amgoth, T., & Pandey, H. M. (2020). Particle swarm optimization based energy efficient clustering and sink mobility in a heterogeneous wireless sensor network. Ad Hoc Networks, 106, 102237.

    Article  Google Scholar 

  7. Dattatraya, K., Navnath, & Raghava Rao, K. (2019). Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN. Journal of King Saud University-Computer and Information Sciences, 34(3), 716–726.

    Article  Google Scholar 

  8. K, Jaiswal. (2019). EOMR: An energy-efficient optimal multi-path routing protocol to improve QoS in wireless sensor network for IoT applications. Wireless Personal Communications, 111(4), 2493–2515.

    Google Scholar 

  9. Özger, M., Alagoz, F., & Akan, O. B. (2018). Clustering in multi-channel cognitive radio ad hoc and sensor networks. IEEE Communication Magazine, 56, 156–162.

    Article  Google Scholar 

  10. Tao, Y., Zhang, Y., & Ji, Y. (2013). Flow-balanced routing for multi-hop clustered wireless sensor networks. Ad Hoc Networks, 11(1), 541–554

    Article  Google Scholar 

  11. Nazir, B., & Hasbullah, H. (2013). Energy efficient and QoS aware routing protocol for clustered wireless sensor networks. Computers & Electrical Engineering, 39(8), 2425–2441

    Article  Google Scholar 

  12. Gherbi, C., Aliouat, Z., & Benmohammed, M. (2016). An adaptive clustering approach to dynamic load balancing and energy efficiency in wireless sensor networks. Energy, 114, 647–662

    Article  Google Scholar 

  13. Eletreby, R., Elsayed, H., & Khairy, M. (2014). CogLEACH: A spectrum aware clustering protocol for cognitive radio sensor networks. In: Proceedings of 9th international conference on cognitive radio oriented wireless networks, ICST, 2014. https://doi.org/10.4108/icst.crowncom.2014.255370

  14. Bhagyalakshmi, L., & Murugan, K. (2014). Avoiding energy holes problem using load balancing approach in a wireless sensor network. KSII Transactions on Internet and Information Systems, 8(5), 1618–1637

    Article  Google Scholar 

  15. Zheng, M., Chen, S., Liang, W., & Song, M. (2019). NSAC: A novel clustering protocol in cognitive radio sensor networks for the Internet of Things. IEEE Internet of Things Journal, 6(3), 5864–5865

    Article  Google Scholar 

  16. Idoudi, H., Mabrouk, O., Minet, P., & Saidane, L. A. (2019). Cluster-based scheduling for cognitive radio sensor networks. Journal of Ambient Intelligence and Humanized Computing, 10(2), 477–489

    Article  Google Scholar 

  17. Vishnu, J. B., & Bhagyaveni, M. A. (2020). Energy efficient cognitive radio sensor networks with team-based hybrid sensing. Wireless Personal Communications, 111(2), 929–945

    Article  Google Scholar 

  18. Bayrakdar, M. E. (2020). Exploiting cognitive wireless nodes for priority-based data communication in terrestrial sensor networks. ETRI Journal, 42(1), 36–45

    Article  Google Scholar 

  19. Pei, E., Pei, J., Liu, S., Cheng, W., Y, Li., & Zhang, Z. (2019). A heterogeneous nodes-based low energy adaptive clustering hierarchy in a cognitive radio sensor network. IEEE Access: Practical Innovations, Open Solutions, 7, 132010–132026.

    Article  Google Scholar 

  20. Gatate, V., & Agarkhed, J. (2021). Energy preservation and network critic-based channel scheduling (EPNCS) in cognitive radio sensor networks. International Journal of Information Technology, 13(1), 69–81.

    Article  Google Scholar 

  21. Zheng, M., Wang, C., Song, M., Liang, W., & Yu, H. (2021). SACR: A stability-aware cluster-based routing protocol for cognitive radio sensor networks. IEEE Sensors Journal, 21(15), 17350–17359.

    Article  Google Scholar 

  22. Shakhov, V., & Koo, I. (2021). An efficient clustering protocol for cognitive radio sensor networks. Electronics, 10(1), 84.

    Article  Google Scholar 

  23. Mukherjee, A., Goswami, P., & Yang, L. (2019). Distributed artificial intelligence-based cluster head power allocation in cognitive radio sensor networks. IEEE Sensors Letters, 3(8), 1–4

    Article  Google Scholar 

  24. Wang, T., Guan, X., Wan, X., Shen, H., & Zhu, X. (2019). A spectrum-aware clustering algorithm based on weighted clustering metricd in cognitive radio sensor networks. IEEE Access: Practical Innovations, Open Solutions, 7, 109555–109565.

    Article  Google Scholar 

  25. Bhagyalakshmi, L., Suman, S. K., & Sujeethadevi, T. (2020). Joint routing and resource allocation for cluster-based isolated nodes in cognitive radio wireless sensor networks. Wireless Personal Communications, 114(4), 3477–3488.

    Article  Google Scholar 

  26. Stephan, T., F, Al-Turjman., & B, Balusamy. (2020). Energy and spectrum aware unequal clustering with deep learning-based primary user classification in cognitive radio sensor networks. International Journal of Machine Learning and Cybernetics, 12(11), 3261–3294.

    Article  Google Scholar 

  27. Tripathi, Y., Prakash, A., & Tripathi, R. (2021). An optimum transmission distance and adaptive clustering based routing protocol for cognitive radio sensor network. Wireless Personal Communications, 116(1), 907–926.

    Article  Google Scholar 

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Funding

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Authors and Affiliations

  1. Department of ECE, JNTUA, Ananthapuram, Andhra Pradesh, India

    V. Jyothi

  2. Department of ECE, Santhiram Engineering College, Nandyal, Andhra Pradesh, India

    M. V. Subramanyam

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  1. V. Jyothi
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  2. M. V. Subramanyam
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Contributions

VJ and MVS contributed to the design and methodology of this study, the assessment of the outcomes and the writing of the manuscript.

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Correspondence to V. Jyothi.

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Jyothi, V., Subramanyam, M.V. An energy efficient fuzzy clustering-based congestion control algorithm for cognitive radio sensor networks. Wireless Netw 30, 4825–4840 (2024). https://doi.org/10.1007/s11276-022-03143-1

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