A Comparative Analysis of Clustered Hierarchical Protocols in Underwater Wireless Sensor Network
Keywords:
C-LEACH, Clustering Technique, ; E-LEACH, Energy Consumption, LEACH protocol, Under-water Wireless Sensor NetworksAbstract
Under-water wireless sensor networks (UWSNs) are a new evolving innovation in which sensor nodes with restricted batteries are positioned in deep seawater. Different monitoring activities like strategic investigation, ocean climate observation, and resource exploration are achieved through these sensors’ nodes. One of the vital issues in UWSN is to increase the lifetime of networks without increasing the hardware complexity, price, and size of the network. There are various challenges in underwater networks such as more propagation delay, inadequate battery power, less storage capacity, less robustness, and less energy conservation. Energy conservation is a real challenge that must be considered. Clustered routing protocols are utilized to cut down energy utilization in underwater sensor networks. LEACH protocol which is hierarchical in nature uses a clustering method for energy efficiency. The two methods; the use of a controller node in each cluster and data aggregation at that node is used in this protocol to save energy. Performance analysis of three clustered routing protocols; LEACH, E-LEACH, and, C-LEACH is performed in this paper using the NS2.35 simulator. These protocols are examined based on energy and communication-related parameters like remaining energy, nodes loss rate, number of alive and dead nodes, bitrate and bytes of data transmitted, packets transmitted and lost, etc. and results are presented systematically.
Downloads
References
Heidemann, J., Stojanovic, M., & Zorzi, M. (2012). Underwater sensor networks: applications, advances and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 370(1958), 158-175, doi: 10.1098/rsta.2011.0214.
Akyildiz, I. F., Pompili, D., & Melodia, T. (2005). Underwater acoustic sensor networks: research challenges. Ad hoc networks, 3(3), 257-279, doi: https://doi.org/10.1016/j.adhoc.2005.01.004.
Khan, A., Ali, I., Ghani, A., Khan, N., Alsaqer, M., Rahman, A. U., & Mahmood, H. (2018). Routing protocols for underwater wireless sensor networks: Taxonomy, research challenges, routing strategies and future directions. Sensors, 18(5), 1619, doi: https://doi.org/10.3390/s18051619.
Tuna, G., & Gungor, V. C. (2017). A survey on deployment techniques, localization algorithms, and research challenges for underwater acoustic sensor networks. International Journal of Communication Systems, 30(17), e3350, doi: https://doi.org/10.1002/dac.3350.
Heidemann, J., Li, Y., Syed, A., Wills, J., & Ye, W. (2005). Underwater sensor networking: Research challenges and potential applications. USC/ISI Technical Report ISI-TR-2005-603.
Nayyar, A., Puri, V., & Le, D. N. (2019). Comprehensive analysis of routing protocols surrounding underwater sensor networks (UWSNs). In Data Management, Analytics and Innovation (pp. 435-450). Springer, Singapore,doi: https://doi.org/10.1007/978-981-13-1402-5_33.
Ahmed, M., Salleh, M., Channa, M. I., & Rohani, M. F. (2017). Energy efficient routing protocols for UWSN: A Review. Telkomnika, 15(1), 212.
Bhambri, H., & Swaroop, A. (2014, March). Underwater sensor network: Architectures, challenges and applications. In 2014 International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 915-920). IEEE,doi: https://doi.org/10.1109/IndiaCom.2014.6828097.
Domingo, M. C., & Prior, R. (2008). Energy analysis of routing protocols for underwater wireless sensor networks. Computer communications, 31(6),1227-1238, doi: https://doi.org/10.1016/j.comcom.2007.11.005.
Goyal, N., Dave, M., & Verma, A. K. (2019). Protocol stack of underwater wireless sensor network: classical approaches and new trends. Wireless Personal Communications, 104(3), 995-1022, doi: https://doi.org/10.1007/s11277-018-6064-z.
Kredo, K. B., & Mohapatra, P. (2007, September). A hybrid medium access control protocol for underwater wireless networks. In Proceedings of the second workshop on Underwater networks (pp. 33-40) doi : https://doi.org/10.1145/1287812.1287821.
Mansouri, M., & Ioualalen, M. (2016). Adapting LEACH algorithm for underwater wireless sensor networks. In The Eleventh International Multi-Conference on Computing in the GlobalInformationTechnology.ICCGI (pp.36-41).
Li, Y., Wang, Y., Ju, Y., & He, R. (2014, October). Energy efficient cluster formulation protocols in clustered underwater acoustic sensor networks. In 2014 7th International Conference on Biomedical Engineering and Informatics (pp. 923-928). IEEE, doi: https://doi.org/10.1109/BMEI.2014.7002904.
Sujatha, N., & Baskar, G. An Efficient Multihop Improved Energy Leach for Underwater Wireless Sensor Network. Turkish Journal of Physiotherapy and Rehabilitation, 32, 3.
Sahana, S., Singh, K., Das, S., & Kumar, R. (2016, April). Energy efficient shortest path routing protocol in underwater sensor networks. In 2016 International Conference on Computing, Communication and Automation (ICCCA) (pp. 546-550). IEEE, doi: https://doi.org/10.1109/CCAA.2016.7813780.
Luo, J., Chen, Y., Wu, M., & Yang, Y. (2021). A survey of routing protocols for underwater wireless sensor networks. IEEE Communications Surveys & Tutorials, 23(1), 137-160, doi: https://doi.org/10.1109/COMST.2020.3048190.
Khan, M. F., Bibi, M., Aadil, F., & Lee, J. W. (2021). Adaptive node clustering for underwater sensor networks. Sensors, 21(13), 4514, doi: https://doi.org/10.3390/s21134514.
Wan, Z., Liu, S., Ni, W., & Xu, Z. (2019). An energy-efficient multi-level adaptive clustering routing algorithm for underwater wireless sensor networks. Cluster Computing, 22(6), 14651-14660, doi:
https://doi.org/10.1007/s10586-018-2376-8.
Yadav, S., & Kumar, V. (2017). Optimal clustering in underwater wireless sensor networks: acoustic, EM and FSO communication compliant technique. IEEE access, 5, 12761-12776, doi: https://doi.org/10.1109/ACCESS.2017.2723506.
Li, X., Wang, Y., & Zhou, J. (2012, December). An energy-efficient clustering algorithm for underwater acoustic sensor networks. In 2012 International Conference on Control Engineering and Communication Technology (pp. 711-714). IEEE, doi: https://doi.org/10.1109/ICCECT.2012.144.
Maurya, P., Kaur, A., & Choudhary, R. (2014, December). Behavior analysis of LEACH protocol. In 2014 International Conference on Parallel, Distributed and Grid Computing (pp. 68-71). IEE, doi: https://doi.org/10.1109/PDGC.2014.7030717.
Singh, S. K., Kumar, P., & Singh, J. P. (2017). A survey on successors of LEACH protocol. Ieee Access, 5, 4298-4328, doi: https://doi.org/10.1109/ACCESS.2017.2666082.
Xu, J., Jin, N., Lou, X., Peng, T., Zhou, Q., & Chen, Y. (2012, May). Improvement of LEACH protocol for WSN. In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (pp. 2174-2177). IEEE, doi: https://doi.org/10.1109/FSKD.2012.6233907.
Kharazian, A. A., Jamshidi, K., & Khayyambashi, M. R. (2012). Adaptive clustering in wireless sensor network: considering nodes with lowest-energy. International Journal of Ad hoc, Sensor & Ubiquitous Computing, 3(2), 1, doi: http://dx.doi.org/10.5121/ijasuc.2012.3201.
Vyas, A., & Puntambekar, S. (2022). Cluster-Based Leach Routing Protocol and Its Successor: A Review. Journal of Scientific Research, 66(1).
Issariyakul, T., & Hossain, E. (2009). Introduction to network simulator 2 (NS2). In Introduction to network simulator NS2 (pp. 1-18). Springer, Boston, MA, doi: https://doi.org/10.1007/978-0-387-71760-9_2.
Isabella Rossi, Reinforcement Learning for Resource Allocation in Cloud Computing , Machine Learning Applications Conference Proceedings, Vol 1 2021.
Kamatchi, S. B. ., Agme, V. N. ., Premkumar, S., Prasad, K. ., V, D. G. ., & Gugan, I. . (2023). Enhancing Microcomputer Edge Computing for Autonomous IoT Motion Control. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 58–67. https://doi.org/10.17762/ijritcc.v11i3.6202
Thangamayan, S., Kumar, B., Umamaheswari, K., Arun Kumar, M., Dhabliya, D., Prabu, S., & Rajesh, N. (2022). Stock price prediction using hybrid deep learning technique for accurate
performance. Paper presented at the IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022, doi:10.1109/ICKECS56523.2022.10060833 Retrieved from www.scopus.com
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Poonam, Vikas Siwach, Harkesh Sehrawat

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.