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Energy efficient clustering routing protocol using novel admission allotment scheme (AAS) based intra-cluster communication for Wireless Sensor Network

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Abstract

The Wireless Sensor Network (WSN) is used in a variety of industrial, commercial and social applications. WSN clustering is a cost-effective method of increasing network lifetime, throughput, scalability, and packet delivery ratio. However, the WSN network's performance is hampered by low-power battery-operated sensor nodes and incorrect cluster head positioning during cluster formation. The Fuzzy C-mean algorithm (FCM) for WSN clustering and the Artificial Bee Colony Algorithm (ABC) for cluster head (CH) selection are presented in this study. The proposed ABC takes into account a variety of clustering factors, including cluster head energy balancing, cluster head load balancing, energy GINI coefficient, and inter and intra cluster distance. Further, energy efficient Ant Colony Optimization (ACO) is proposed to route the data from CH to base station (BS). This paper presents novel Admission Allotment Scheme (AAS) based intra-cluster communication to minimize overheads on the sensor nodes and packet drop. The proposed algorithm provides optimized cluster selection that offers better network lifetime, packet delivery ratio and throughput over traditional state of arts.

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References

  1. Kaur A, Gupta P and Garg R (2021) Soft computing techniques for clustering in WSN. In: IOP conference series: materials science and engineering, vol. 1022, No. 1. IOP Publishing, p 012041

  2. Daanoune I, Abdennaceur B, Ballouk A (2021) A comprehensive survey on LEACH-based clustering routing protocols in Wireless Sensor Networks. Ad Hoc Netw 114:102409

    Article  Google Scholar 

  3. Kirubasri G (2021) A contemporary survey on clustering techniques for Wireless Sensor Networks. Turkish J Comput Math Educ (TURCOMAT) 12(11):5917–5927

    Google Scholar 

  4. Rawat P, Chauhan S (2021) Clustering protocols in wireless sensor network: a survey, classification, issues, and future directions. Comput Sci Rev 40:100396

    Article  MathSciNet  Google Scholar 

  5. Ramani KPL, Badholia A (2021) Cluster based routing protocols in Wsn sensor. Inf Technol Indust 9(1):198–206

    Google Scholar 

  6. Rathore PS, Chatterjee JM, Kumar A, Sujatha R (2021) Energy-efficient cluster head selection through relay approach for WSN. J Supercomput 77(7):7649–7675

    Article  Google Scholar 

  7. Mishra PK and Verma SK (2020) A survey on clustering in wireless sensor network. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, pp 1–5

  8. Janaki Raam KV, Rajkumar K (2015) A novel approach using parallel ACO algorithm for detecting routing path based on cluster head in Wireless Sensor Network. Indian J Sci Technol 8(16):1–7

    Article  Google Scholar 

  9. Liu X, Fu H (2010) An effective clustering algorithm with ant colony. J Comput 5(4):598–605

    Article  Google Scholar 

  10. Gupta V, Sharma SK (2015) Cluster head selection using modified ACO. Proceedings of fourth international conference on soft computing for problem solving. Adv Intell Syst Comput 335:11–20

    Google Scholar 

  11. Aadil F, Bajwa KB, Khan S, Majeedchaudary N, Akram A (2016) CACONET: ACO (ACO) based clustering algorithm for VANET. PLoS One. https://doi.org/10.1371/journal.pone.0154080

    Article  Google Scholar 

  12. Yang J, Xu M, Zhao W, Xu B (2010) A multipath routing protocol based on clustering and ACO for Wireless Sensor Networks. Sensors 10(5):4521–4540

    Article  Google Scholar 

  13. Maheshwari P, Sharma AK, Verma K (2021) Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Netw 110:102317

    Article  Google Scholar 

  14. Xiuwu Y, Ying L, Yong L, Hao Y (2022) WSN clustering routing algorithm based on hybrid genetic tabu search. Wireless Pers Commun 124(4):3485–3506

    Article  Google Scholar 

  15. Yadav RK, Mahapatra RP (2021) Energy aware optimized clustering for hierarchical routing in wireless sensor network. Comput Sci Rev 41:100417

    Article  MathSciNet  Google Scholar 

  16. Selvi M, Santhosh Kumar SVN, Ganapathy S, Ayyanar A, Khanna Nehemiah H, Kannan A (2021) An energy efficient clustered gravitational and fuzzy based routing algorithm in WSNs. Wireless Pers Commun 116(1):61–90

    Article  Google Scholar 

  17. Rodríguez A, Pérez-Cisneros M, Rosas-Caro JC, Del-Valle-Soto C, Gálvez J, Cuevas E (2021) Robust clustering routing method for Wireless Sensor Networks considering the locust search scheme. Energies 14(11):3019

    Article  Google Scholar 

  18. Reddy DL, Puttamadappa C, Suresh HN (2021) Merged glowworm swarm with ant colony optimization for energy efficient clustering and routing in Wireless Sensor Network. Pervasive Mob Comput 71:101338

    Article  Google Scholar 

  19. Mehra PS, Doja MN, Alam B (2018) Correction to: Zonal based approach for clustering in heterogeneous WSN. Int J Inf Tecnol. https://doi.org/10.1007/s41870-018-0124-1

    Article  Google Scholar 

  20. Kumar R, Gangwar M (2019) Improved BEST-MAC protocol for WSN using optimal cluster head selection. Int J Inf Tecnol. https://doi.org/10.1007/s41870-019-00385-9

    Article  Google Scholar 

  21. Gulganwa P, Jain S (2022) EES-WCA: energy efficient and secure weighted clustering for WSN using machine learning approach. Int J Inf Tecnol 14:135–144. https://doi.org/10.1007/s41870-021-00744-5

    Article  Google Scholar 

  22. Siddique AA, Qadri MT (2020) Wireless sensor network (WSN) based early flood warning system. Int J Inf Tecnol 12:567–570. https://doi.org/10.1007/s41870-018-0125-0

    Article  Google Scholar 

  23. Tripathi Y, Prakash A, Tripathi R (2021) Load aware multipath data forwarding for enhanced lifetime of WSN. Int J Inf Tecnol 13:807–815. https://doi.org/10.1007/s41870-020-00557-y

    Article  Google Scholar 

  24. Kalaimani D, Zah Z, Vashist S (2021) Energy-efficient density-based Fuzzy C-means clustering in WSN for smart grids. Aust J Multi-Disciplin Eng 17(1):23–38

    Article  Google Scholar 

  25. Dagur A, Malik N, Tyagi P, Verma R, Sharma R and Chaturvedi R (2021) Energy enhancement of WSN using fuzzy C-means clustering algorithm. In: Data intelligence and cognitive informatics. Springer, Singapore, pp 315–323

  26. Karim SM, Ozturk C, Mahmood MK (2021) ABC-based optimization of cluster head selection in Wireless Sensor Networks. Int J Elect Eng Inform 13(2):287–296

    Google Scholar 

  27. Almajidi AM, Pawar VP, Alammari A and Ali NS (2020) ABC-based algorithm for clustering and validating WSNs. In: Cybernetics, cognition and machine learning applications. Springer, Singapore, pp 117–125

  28. Wang C, Liu X, Hu H, Han Y, Yao M (2020) Energy-efficient and load-balanced clustering routing protocol for wireless sensor networks using a chaotic genetic algorithm. IEEE Access 8:158082–158096

    Article  Google Scholar 

  29. Heinzelman W, Chandrakasan A and Balakrishnan H (2000) Energy-efficient communication protocol for wireless sensor networks. In: The Proceeding of the Hawaii International Conference System Sciences, Hawaii

  30. Tripathi M, Gaur MS, Laxmi V and Battula RB (2013) Energy efficient LEACH-C protocol for Wireless Sensor Network. In: Third International Conference on Computational Intelligence and Information Technology (CIIT 2013), pp 402–405. https://doi.org/10.1049/cp.2013.2620

  31. Ghosh S, Mondal S, Biswas U (2016) Efficient data gathering in WSN using fuzzy C means and ACO. Int Conf Inf Sci (ICIS) 2016:258–265. https://doi.org/10.1109/INFOSCI.2016.7845337

    Article  Google Scholar 

  32. Bhangale KB, Mohanaprasad K (2021) A review on speech processing using machine learning paradigm. Int J Speech Technol 24:367–388. https://doi.org/10.1007/s10772-021-09808-0

    Article  Google Scholar 

  33. Bhangale KB, Kothandaraman M (2022) Survey of deep learning paradigms for speech processing. Wireless Pers Commun 125:1913–1949. https://doi.org/10.1007/s11277-022-09640-y

    Article  Google Scholar 

  34. Bhangale K, Mohanaprasad K (2022) Speech emotion recognition using mel frequency log spectrogram and deep convolutional neural network. In: Sivasubramanian A, Shastry PN, Hong PC (eds) Futuristic communication and network technologies. VICFCNT 2020. Lecture notes in electrical engineering, vol 792. Springer, Singapore. https://doi.org/10.1007/978-981-16-4625-6_24

    Chapter  Google Scholar 

  35. Bhangale K, Ingle P, Kanase R and Desale D (2021) Multi-view multi-pose robust face recognition based on VGGNet. In: International conference on image processing and capsule networks. Springer, Cham, pp 414–421

  36. Arya G, Bagwari A, Chauhan DS (2022) Performance analysis of deep learning-based routing protocol for an efficient data transmission in 5G WSN communication. IEEE Access 10:9340–9356

    Article  Google Scholar 

  37. Balica RS (2022) Machine and deep learning technologies, Wireless Sensor Networks, and virtual simulation algorithms in digital twin cities. Geopolit Hist Int Relat 14(1):59–74

    Article  Google Scholar 

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Acknowledgements

I would like to express our sincere thanks to Management and all staff members of Marathwada Shikshan Parasarak Mandal’s Shri Shivaji Polytechnic Institute, Parbhani for continuous support of my research work.

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Authors and Affiliations

  1. Sandip University, Nashik, 422213, Maharashtra, India

    Mohammed Shahid Thekiya & Mangesh Dindayal Nikose

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  1. Mohammed Shahid Thekiya
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  2. Mangesh Dindayal Nikose
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Correspondence to Mohammed Shahid Thekiya.

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Thekiya, M.S., Nikose, M. Energy efficient clustering routing protocol using novel admission allotment scheme (AAS) based intra-cluster communication for Wireless Sensor Network. Int. j. inf. tecnol. 14, 2815–2824 (2022). https://doi.org/10.1007/s41870-022-01086-6

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