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Improving Data Communication of Wireless Sensor Network Using Energy Efficient Adaptive Cluster-Head Selection Algorithm for Secure Routing

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Abstract

Energy, delay and distance are the most important parameters in the WSN (Wireless Sensor Network) suitable for cluster head selection. Only the neighboring terminals communicate with the cluster head, and then, the package material information is transmitted to the base station, thereby the energy and lifetime associated with the sensor node and all neighboring nodes in the Cluster Head Selection (CHS) are extended. Analysis based on efficient routing reveals that the previous method has often changed the link or failed, because, unstable topographic network has been a big challenge.In this model,Energy-Efficient Adaptive Cluster-Head Selection Algorithm (EEACHS) is introduced which is selected based on the remaining energy dynamically designed to change the role of the integrated cluster, thereby, energy consumption of the entire network under the ground is balanced.The Energy-Efficient Adaptive Cluster-Head Selection Algorithm (EEACHS) adapts to the energy load and distributes the rotating cluster head positions between all the nodes in the cluster. This means that the probability of each node is relocated and the remaining energy level of the sensor is extended based on the cluster node network. An energy efficient network model is introduced which can translate mobile BS (Base Station) into a cluster-based network infrastructure using the proposed algorithm. The simulation results reveal that high energy efficiency is very helpful to extend the life of the network.

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References

  1. https://www.sciencedirect.com/science/article/pii/S0168169915002379

  2. https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.3407

  3. Lipare, A., & Edla, D. R. (2019), Cluster head selection and cluster construction using fuzzy logic in WSNs. In 2019 IEEE 16th India Council International Conference (INDICON). https://doi.org/10.1109/indicon47234.2019.9030302.

  4. Ranjith, R. S., & Vishwas, H. N. (2017). Evaluation study of secondary cluster head selection using fuzzy Logic in WSN for conservation of battery energy. In 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT). doi:https://doi.org/10.1109/icicct.2017.7975157.

  5. John, A., Rajput, A., & Babu, K. V. (2017). Energy saving cluster head selection in wireless sensor networks for Internet of things applications. In 2017 International Conference on Communication and Signal Processing (ICCSP). https://doi.org/10.1109/iccsp.2017.8286486.

  6. Haider, S. K., Jamshed, M. A., Jiang, A., Pervaiz, H., & Ni, Q. (2019), UAV-assisted cluster-head selection mechanism for wireless sensor network applications. In 2019 UK/ China Emerging Technologies (UCET). https://doi.org/10.1109/ucet.2019.8881889.

  7. Altakhayneh, W. A., Ismail, M., Altahrawi, M. A., & AbuFoul, M. K. (2019). Cluster head selection using genetic algorithm in wireless network. 2019 IEEE 14th Malaysia International Conference on Communication (MICC). https://doi.org/10.1109/micc48337.2019.9037609.

  8. Panchal, A., & Singh, R. K. (2018). Energy efficient cluster head selection with adaptive threshold in WSN. In 2018 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). https://doi.org/10.1109/ispacs.2018.8923283.

  9. Nithya B., Abhinaya S.B., Lavanya V (2018). A party-based cluster head selection algorithm for wireless sensor networks. In 2018 International Conference on Computing, Power and Communication Technologies (GUCON) (pp 327–332).

  10. Wang, H., Chang, H., Zhao, H., & Yue, Y. (2017). Research on LEACH algorithm based on double cluster head cluster clustering and data fusion. In 2017 IEEE International Conference on Mechatronics and Automation (ICMA). https://doi.org/10.1109/icma.2017.8015840.

  11. Sharawi, M., & Emary, E. (2017). Impact of grey wolf optimization on WSN cluster formation and lifetime expansion, In 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI). https://doi.org/10.1109/icaci.2017.7974501.

  12. Ahamad, F., & Kumar, R. (2016). Energy efficient region based clustering algorithm for WSN using fuzzy Logic. In 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). https://doi.org/10.1109/rteict.2016.7807984.

  13. Jin, W., & Bin, W. (2017). Adaptive clustering approach over unreliable links for WSN. In 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI). https://doi.org/10.1109/icemi.2017.8265759.

  14. Kushal, B. Y., & Chitra, M. (2016). Cluster based routing protocol to prolong network lifetime through mobile sink in WSN. In 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). https://doi.org/10.1109/rteict.2016.7808039.

  15. Jain, K. L., & Mohapatra, S. (2019). Energy efficient cluster head selection for wireless sensor network: a simulated comparison. In 2019 IEEE 10th Control and System Graduate Research Colloquium (ICSGRC). https://doi.org/10.1109/icsgrc.2019.8837086.

  16. Vhatkar, S., Shaikh, S., & Atique, M. (2017). Performance analysis of equalized and double cluster head selection method in wireless sensor network. In 2017 Fourteenth International Conference on Wireless and Optical Communications Networks (WOCN). doi:https://doi.org/10.1109/wocn.2017.8065854.

  17. Ali, H., Tariq, U. U., Hussain, M., Lu, L., Panneerselvam, J., & Zhai, X. (2020). ARSH-FATI a novel metaheuristic for cluster head selection in wireless sensor networks. IEEE Systems Journal. https://doi.org/10.1109/jsyst.2020.2986811

    Article  Google Scholar 

  18. Prasath, K. A., & Shankar, T. (2015). RMCHS: Ridge method based cluster head selection for energy efficient clustering hierarchy protocol in WSN. In 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM). https://doi.org/10.1109/icstm.2015.7225391.

  19. Joshitha, K. L., & Gangasri, A. (2017). On an effort to enhance lifetime of a regression based clustered network using candidate selection. In 2017 International Conference on Trends in Electronics and Informatics (ICEI). https://doi.org/10.1109/icoei.2017.8300816.

  20. Garg, N., & Saxena, S. (2018). Cluster Head Selection Using Genetic Algorithm in Hierarchical Clustered Sensor Network. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). https://doi.org/10.1109/iccons.2018.8662914.

  21. Usha, M., Sreenithi, S., Sujitha, M., & Swarnalatha, S. (2017). Node density based clustering to maximize the network lifetime of WSN using multiple mobile elements. In 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA). https://doi.org/10.1109/iceca.2017.8212783

  22. Prashanth, J. S., & Nandury, S. V. (2015). Cluster-based rendezvous points selection for reducing tour length of mobile element in WSN. In 2015 IEEE International Advance Computing Conference (IACC). https://doi.org/10.1109/iadcc.2015.7154898.

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Authors and Affiliations

  1. Department of ECE, Sona College of Technology, Salem, Tamilnadu, India

    S. Vijayalakshmi

  2. Department of Electronics and Communication Engineering, Government College of Engineering, Salem, TamilNadu, India

    G. Kavithaa

  3. Department of Computing and IT, Stanmore College, HA7 4BQ, Stanmore, Middlesex, United Kingdom

    N. V. Kousik

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  1. S. Vijayalakshmi
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  2. G. Kavithaa
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Correspondence to N. V. Kousik.

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Vijayalakshmi, S., Kavithaa, G. & Kousik, N.V. Improving Data Communication of Wireless Sensor Network Using Energy Efficient Adaptive Cluster-Head Selection Algorithm for Secure Routing. Wireless Pers Commun 128, 25–42 (2023). https://doi.org/10.1007/s11277-021-09398-9

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