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Clustering Based Energy Load Analysis Model (CBELAM) in Wireless Sensor Networks

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A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 210))

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Abstract

Sensor nodes of wireless sensor networks (WSNs) find it hard to prolong the network lifetime, which is also very expensive. In WSNs, reliable data transmission and prolonged network lifetime have to be achieved. Energy-efficient routing protocol has to be developed, which is a challenging issue in a network. The objective focused in this paper is to balance and minimize the energy consumed by a node during data transmission in WSN, thereby attempts in prolonging the network lifetime. Clustering-based energy load analysis model is presented in this paper to analyze the energy model. The experimental results of the proposed method show better results over the existing methods.

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References

  1. Hayes, T., & Ali, F. H. (2016). Location-aware sensor routing protocol for mobile wireless sensor networks. IET Wireless Sensor Systems, 6(2), 49–57.

    Article  Google Scholar 

  2. Yan, J., Zhou, M., & Ding, Z. (2016). Recent advances in energy-efficient routing protocols for wireless sensor networks: A review. IEEE Access, 4, 5673–5686.

    Article  Google Scholar 

  3. Rahim, R., Murugan, S., Mostafa, R. R., Dubey, A. K., Regin, R., Kulkarni, V., & Dhanalakshmi, K. S. (2020). Detecting the Phishing Attack Using Collaborative Approach and Secure Login through Dynamic Virtual Passwords. Webology, 17(2).

    Google Scholar 

  4. Rahim R., Murugan, S., Priya, S., Magesh, S., & Manikandan, R. (2020). Taylor Based Grey Wolf Optimization Algorithm (TGWOA) For Energy Aware Secure Routing Protocol. International Journal of Computer Networks and Applications, 7(93). https://doi.org/ 10.22247/ijcna/2020/196041. 

  5. Mittal, N., Singh, U., & Sohi, B. S. (2017). A stable energy efficient clustering protocol for wireless sensor networks. Wireless Networks, 23(6), 1809–1821.

    Article  Google Scholar 

  6. Murugan, S., Jeyalaksshmi, S., Mahalakshmi, B., Suseendran, G., Jabeen, T. N., & Manikandan, R. (2020). Comparison of ACO and PSO algorithm using energy consumption and load balancing in emerging MANET and VANET infrastructure. Journal of Critical Reviews, 7(9)

    Google Scholar 

  7. Khalaf, O. I., Abdulsahib, G. M., & Sabbar, B. M. (2020). Optimization of wireless sensor network coverage using the bee algorithm. Journal of Information Science and Engineering, 36(2), 377–386.

    Google Scholar 

  8. Bouabdallah, F., Bouabdallah, N., & Boutaba, R. (2008). On balancing energy consumption in wireless sensor networks. IEEE Transactions on Vehicular Technology, 58(6), 2909–2924.

    Article  Google Scholar 

  9. Sampathkumar, A., Murugan, S., Rastogi, R., Mishra, M. K., Malathy, S., & Manikandan, R. (2020). Energy Efficient ACPI and JEHDO Mechanism for IoT Device Energy Management in Healthcare. In Internet of Things in Smart Technologies for Sustainable Urban Development (pp. 131–140).

    Google Scholar 

  10. Montoya, G. A., & Donoso, Y. (2013). Energy load balancing strategy to extend lifetime in wireless sensor networks. Procedia Computer Science, 17, 395–402.

    Article  Google Scholar 

  11. Elsmany, E. F. A., Omar, M. A., Wan, T. C., & Altahir, A. A. (2019). EESRA: Energy efficient, scalable routing algorithm for wireless sensor networks. IEEE Access, 7, 96974–96983.

    Article  Google Scholar 

  12. Chu, K. C., Horng, D. J., Chang, K. C. (2019). Numerical optimization of the energy consumption for wireless sensor networks based on an improved ant colony algorithm. IEEE Access

    Google Scholar 

  13. Datta, D., Mishra, S., & Rajest, S. S. (2020). Quantification of tolerance limits of engineering system using uncertainty modeling for sustainable energy. International Journal of Intelligent Networks, 1, 1–8. https://doi.org/10.1016/j.ijin.2020.05.006

    Article  Google Scholar 

  14. Pereira, H. (2020). Métrica de roteamento para prolongar o tempo de operação de redes de baixa potência e com perdas

    Google Scholar 

  15. Xu, C., Xiong, Z., Zhao, G., & Yu, S. (2019). An energy-efficient region source routing protocol for lifetime maximization in WSN. IEEE Access, 7, 135277–135289.

    Article  Google Scholar 

  16. Förster, A., Förster, A., & Murphy, A. L. (2009). Optimal cluster sizes for wireless sensor networks: An experimental analysis. International Conference on Ad Hoc Networks, 49–63

    Google Scholar 

  17. Mahajan, S., Malhotra, J., & Sharma, S. (2013). Improved enhanced chain based energy efficient wireless sensor network

    Google Scholar 

  18. Obaid, A. J., Alghurabi, K. A., Albermany, S. A. K. & Sharma, S. (2021). “Improving extreme learning machine accuracy utilizing genetic algorithm for intrusion detection purposes.” In Advances in intelligent systems and computing (pp. 171–177), Springer.

    Google Scholar 

  19. Sampathkumar, A., Maheswar, R., Harshavardhanan, P., Murugan, S., Jayarajan, P., & Sivasankaran, V. (2020, July). Majority Voting based Hybrid Ensemble Classification Approach for Predicting Parking Availability in Smart City based on IoT. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1–8). IEEE.

    Google Scholar 

  20. Sharma, S., & Obaid, A. J. (2020). Mathematical modelling, analysis and design of fuzzy logic controller for the control of ventilation systems using MATLAB fuzzy logic toolbox. Journal of Interdisciplinary Mathematics, 23(4), 843–849.

    Article  Google Scholar 

  21. Sharma, S., & Obaid, A. J. (2020). Contact-mechanics and dynamics analysis of three-different ellipsoidal raceway geometries for deep Groove ball bearing using Abaqus 6.13 version FEA simulation for high load-bearing as well as speed-rotating applications. International Research Journal of Multidisciplinary Science and Technology, 3(5), 36–43.

    Google Scholar 

  22. Obaid, A. J., & Sharma, S. (2020). Recent trends and development of heuristic artificial intelligence approach in mechanical system and engineering product design. Saudi Journal of Engineering and Technology, 5(2), 86–93.

    Article  Google Scholar 

  23. Subramaniyan, M., Sampathkumar, A., Jain, D., Manikandan, R., Patan, R., & Kumar, A. (2021). Deep Learning Approach Using 3D-ImpCNN Classification for Coronavirus Disease. https://doi.org/10.1007/978-3-030-60188-1_7. 

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Author information

Authors and Affiliations

  1. Department of ECE, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India

    S. David & V. P. Ajay

  2. Department of ECE, PSG College of Technology, Coimbatore, Tamil Nadu, India

    P. T. Vanathi

  3. Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

    Nesasudha

  4. Department of Computer Science and Engineering, Sri Aravindar Engineering College, Villupuram, Tamil Nadu, India

    S. Murugan

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  1. S. David
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  2. V. P. Ajay
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  3. P. T. Vanathi
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  4. Nesasudha
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  5. S. Murugan
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Editor information

Editors and Affiliations

  1. Department of Software Engineering, Quaid-e-Awam University of Engineering, Science and Technology (QUEST), Nawabshah, Pakistan

    Pardeep Kumar

  2. Department of Computer Science, University of Kufa, Najaf, Iraq

    Ahmed Jabbar Obaid

  3. Department of Electrical and Electronics Engineering, Trakya University, Edirne, Turkey

    Korhan Cengiz

  4. Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India

    Ashish Khanna

  5. Department of Automatics and Applied Software, University of Arad, Arad, Romania

    Valentina Emilia Balas

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David, S., Ajay, V.P., Vanathi, P.T., Nesasudha, Murugan, S. (2022). Clustering Based Energy Load Analysis Model (CBELAM) in Wireless Sensor Networks. In: Kumar, P., Obaid, A.J., Cengiz, K., Khanna, A., Balas, V.E. (eds) A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems. Intelligent Systems Reference Library, vol 210. Springer, Cham. https://doi.org/10.1007/978-3-030-76653-5_7

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