Relative Entropy-based Energy Efficient Multi-hop Forwarding Routing Protocol for Wireless Sensor Network
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Abstract
Recently, cluster distribution and selection have been made in the most advanced protocols based on Low Power Adaptive Clustering Hierarchical Protocol (LEACH), followed by cluster head election. Conversely, it is not thought that data redundancy is sufficient. This study suggests a modified Leach approach incorporating relative information entropy (Leach-CIE). The relative entropy value of the data’s probability distribution in the node’s two subsequent periods is calculated in conjunction with the close entropy model. The information is then compared with a threshold value to determine the degree of redundancy. The network accomplishes reducing energy consumption by refusing to transmit redundancy data. Instead, a multi-hop routing (MFRCRE) considers its residual energy, the energy consumption of each node in the network during the data transmission phase, by optimizing node forwarding conditions and considering factors such as communication distance and energy consumption. It is proposed to balance the rate—account ratio. The results of the experiments demonstrate that this approach can successfully reduce and balance the energy consumption of network nodes to increase the life of the network.
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Raghuwanshi, A., & Chaudhary, V. (2022). Relative Entropy-based Energy Efficient Multi-hop Forwarding Routing Protocol for Wireless Sensor Network. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, 14(04), 6-16. https://doi.org/10.18090/samriddhi.v14i04.02
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Research Article
References
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[3] A. Dekdouk, “Energy efficient oriented mobile learning that emphasizes energy saving awareness education,”
2013 ACS International Conference on Computer Systems
and Applications (AICCSA), 2013, pp. 1- 4, doi: 10.1109/ AICCSA.2013.6616496.
[4] X. Zhong, Y. Tan, Q. Ding and L. Zhang, “Integrative management and energy-saving strategy in WSN,” 2014 World Congress on Computer Applications and Information Systems (WCCAIS), 2014, pp. 1-7, doi: 10.1109/WCCAIS.2014.6916580.
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[6] Z. Zhang, R. Luo and W. Fu, “Energy Saving Algorithm of Wireless Network Nodes in Cluster,” 2020 International Wireless Communications and Mobile Computing (IWCMC), 2020, pp.
394-397, doi: 10.1109/IWCMC48107.2020.9148250.
[7] S. H. Singh, K. R. Verma and P. Rajpoot, “Partition Based Strategic Node Placement and Efficient Communication
Method for WSN,” 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2018, pp. 1807-1812, doi: 10.1109/ RTEICT42901.2018.9012336.
[8] N. A. M. Alduais, J. Abdullah and A. Jamil, “RDCM: An Efficient Real-Time Data Collection Model for IoT/WSN Edge With
Multivariate Sensors,” in IEEE Access, vol. 7, pp. 89063-89082, 2019, doi: 10.1109/ACCESS.2019.2926209.
[9] Satish Kumar Nalluri, Venkata Krishna Bharadwaj Parasaram, Varun Teja Bathini. (2020). Secure Automation Frameworks for Smart Manufacturing Using Blockchain-Assisted Traceability.
International Journal of Research & Technology, 8(2), 47–53.
Retrieved from https://ijrt.org/j/article/view/879
[10] C. Jing, T. Gu and L. Chang, “ESCAL: An Energy-Saving Clustering Algorithm Based on LEACH,” 2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop, 2008, pp.
403-406, doi: 10.1109/KAMW.2008.4810509.
[11] Xiangyi Li, Guixia Kang, Xidong Zhang and Dongyan Huang, “An energy-efficient cooperative MIMO strategy for Wireless Sensor Networks with intra-body channel,” 2012 International Symposium on Communications and Information Technologies (ISCIT), 2012, pp. 679-684, doi: 10.1109/ISCIT.2012.6380987.
[12] H. Bizagwira, J. Toussaint and M. Misson, “Multi-channel routing protocol for dynamic WSN,” 2016 Wireless Days (WD), 2016, pp.
1-3, doi: 10.1109/WD.2016.7461512.
[13] W. Feng et al., “Joint Energy-Saving Scheduling and Secure Routing for Critical Event Reporting in Wireless Sensor Networks,” in IEEE Access, vol. 8, pp. 53281-53292, 2020, doi: 10.1109/ACCESS.2020.2981115.
[14] A. M. I. Alkuhlani and S. B. Thorat, “Enhanced location privacy and energy saving technique for sensors in Internet of Things domain,” 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), 2016, pp. 122-125, doi: 10.1109/ ICGTSPICC.2016.7955282.
[15] H. Ben Fradj, R. Anane and R. Bouallegue, “Energy Consumption for Opportunistic Routing Algorithms in WSN,” 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), 2018, pp. 259-265, doi: 10.1109/ AINA.2018.00048.
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[17] M. L. Laouira, A. Abdelli, J. B. Othman and H. Kim, “An Efficient WSN Based Solution for Border Surveillance,” in IEEE Transactions on Sustainable Computing, vol. 6, no. 1, pp. 54-65, 1 Jan.-March 2021, doi: 10.1109/TSUSC.2019.2904855.
[18] X. -S. Yi, P. -J. Jiang, X. -W. Wang and S. -C. Zhang, “Survey of Energy-Saving Protocols in Wireless Sensor Networks,” 2011 First International Conference on Robot, Vision and Signal Processing, 2011, pp. 208-211, doi: 10.1109/RVSP.2011.43.
[19] S. Misra and R. Kumar, “A literature survey on various clustering approaches in wireless sensor network,” 2016 2nd International Conference on Communication Control and Intelligent Systems (CCIS), 2016, pp. 18-22, doi: 10.1109/CCIntelS.2016.7878192.
[20] Jia Meng, Hao Wu, Hengliang Tang, Lan Dong and Hua Cui, “Research on energy efficiency in cooperative communication of WSNs,” 2012 IEEE 14th International Conference on
Communication Technology, 2012, pp. 485-490, doi: 10.1109/ ICCT.2012.6511267.
[20] Z. Hu, Y. Sun and Y. Ji, “A Dynamic Spectrum Access Strategy Based on Real-Time Usability in Cognitive Radio Sensor Networks,” 2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks, 2011, pp. 318-322, doi: 10.1109/ MSN.2011.3.