Scalable Multi-Clustering Aggregation Scheme in WSN Using Machine Learning

Year 2025, Volume: 9 Issue: 1, 103 - 115, 20.01.2025

Ramesh D , Jaya T

https://doi.org/10.31127/tuje.1488192

Abstract

Due to Limited resource constraints in WSN data packets collide while being routed to sink, redundant data can be eliminated by data aggregation, which minimizes overall amount of data transferred and increases network's lifespan. Minimizing energy consumption and boost data aggregation rate is most crucial factor in WSN. A Scalable Multi-Clustering Aggregation utilizing Machine Learning (SMCA-ML) focuses on data aggregation approach of heterogeneous wireless sensor networks, using neurons as wireless sensor network nodes in a machine learning method. Machine Learning method accumulates the captured data collected by senor nodes and integrates the accumulated data with multi-clustering route. Threshold value of hidden layer and weight of input layer are randomly generated by the proposed method prior to training. This results in an unstable output that affects the efficiency of data aggregation and causes a long delay. More crucially, distinct threshold settings were made in accordance with the features of uneven energy consumption in wireless sensor networks (WSNs) allow data packets more swiftly by setting smaller threshold in far sink with enough energy. To maximize data aggregation, close sink area with tight energy employs a greater threshold. This way, the combination can result in high data fusion, efficient energy consumption, and little delay. The results of simulation suggested that SMCA-ML based data aggregation algorithm can significantly extend lifespan of network, substantially decrease energy consumption, enhance network energy, expand network performance and improve data aggregation efficiency when compared to conventional Stable Election Protocol (SEP), Back Propagation algorithm, and Extreme Learning Machine.

Keywords

: Data Aggregation, Machine Learning, Energy Efficient, Network life span, Clustering, reliability

References

• Giji Kiruba, D., & Benita, J. (2022). A Survey of Secured Cluster Head: SCH Based Routing Scheme for IOT Based Mobile Wireless Sensor Network. ECS Trans. Vol. 107, pp. 16725.
• Ji, S., Tan C., Yang, P., Sun, Y.J., Fu D., & Wang, J. (2018). Compressive sampling and data fusion-based structural damage monitoring in wireless sensor network. J Supercomputing, vol. 74, no. 3, pp. 1108_1131.
• Rajesh, D., & Rajanna, G. S. (2023). Energy-efficient CH selection protocol for mobile smart dust network. e-Prime - Advances in Electrical Engineering, Electronics and Energy, Vol. 4, 100176.
• Rajesh, D., & Kiruba, D.G. (2021). A probability-based energy competent cluster based secured ch selection routing EC2SR protocol for smart dust. Peer-to-Peer Netw. Appl. Vol. 14, pp. 1976–1987.
• Rajesh, D., & Jaya, T. (2021). ECIGC-MWSN: Energy capable information gathering in clustered secured CH based routing in MWSN. Materials Today: Proceedings, Volume 43, Part 6, pp. 3457-3462.
• Rajesh, D., & Rajanna, G.S. (2023). Energy Aware Data Harvesting Strategy Based on Optimal Node Selection for Extended Network Lifecycle in Smart Dust. Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 939-949.
• Zhou, Z., Liao, H., Gu, B., Huq, K. M. S., Mumtaz, S., & Rodriguez, J. (2018). Robust mobile crowd sensing: When deep learning meets edge computing. IEEE Netw., vol. 32, no. 4, pp. 54_60.
• Zhou, Z., Chen, X., & Gu, B. (2019). Multi-scale dynamic allocation of licensed and unlicensed spectrum in software defined HetNets. IEEE Netw., vol. 33, no. 4, pp. 9_15.
• Giji Kiruba, D., Benita, J., & Rajesh, D. (2024). Energy Efficient Clustering Mechanism for Malicious Sensor Nodes in IOT Based MWSN. International Research Journal of Multidisciplinary Scope (IRJMS), 5(1):50-61.
• Giji Kiruba, D., Benita, J., & Rajesh, D. (2023). A Proficient Obtrusion Recognition Clustered Mechanism for Malicious Sensor Nodes in a Mobile Wireless Sensor Network. Indian Journal of Information Sources and Services, 13(2), 53–63.
• Yang, A.M., Yang, X.L., Chang, J.C., Bai, B., Kong , F.B., & Ran, Q.B. (2018). Research on a fusion scheme of cellular network and wireless sensor for cyber physical social systems. IEEE Access, vol. 6, pp. 18786_18794.
• Rajesh, & Giji Kiruba (2022). A Comparative Study On Energy Efficient Secured Clustered Approaches for IOT Based MWSN. Suranaree J. Sci. Technol. Vol. 29, no. 4, pp. 010151(1-18).
• Ikram Daanoune & Abdennaceur Baghdad. (2022). IBRE-LEACH: Improving the Performance of the BRE-LEACH for Wireless Sensor Networks. Wireless Personal Communications: An International JournalVolume 126Issue 4, pp 3495–3513.
• Din,S., Ahmad, A., Paul, A., Ullah Rathore, M. M., & Jeon, G. (2017). A cluster based data fusion technique to analyze big data in wireless multi-sensor system. IEEE Access, vol. 5, pp. 5069_5083.
• Jan, S. R. U., Jan, M. A., Khan, R., Ullah, H., Alam, M., & Usman, M. (2019). An energy-efficient and congestion control data-driven approach for clusterbased sensor network. Mobile Netw Appl, vol. 24, no. 4, pp. 1295_1305.
• Lu, Y., & Sun, N. (2018). A resilient data aggregation method based on Spatio- Temporal correlation for wireless sensor networks. EURASIP J. Wireless Commun. Netw., vol. 2018, no. 1, pp. 157_165.
• Muthukumaran, K., Chitra, K., & Selvakumar, C. (2018). An energy efficient clustering scheme using multilevel routing for wireless sensor network. Computers & Electrical Engineering, Volume 69, Pages 642-652, ISSN 0045-7906.
• Huafeng Wu, Jiangfeng Xian, Xiaojun Mei, Yuanyuan Zhang, Jun Wang, Junkuo Cao, & Prasant Mohapatra. (2019). Efficient target detection in maritime search and rescue wireless sensor network using data fusion. Computer Communications, Volume 136, Pages 53-62, ISSN 0140-3664.
• Zhang, W., Yang, J., Su, H. et al. (2018). Medical data fusion algorithm based on Internet of things. Pers Ubiquit Comput 22, 895–902.
• Giji Kiruba, & Benita. (2021). Energy capable clustering method for extend the duration of IoT based mobile wireless sensor network with remote nodes. Energy Harvesting and Systems, vol. 8, no. 1, pp. 55-61.
• Rajesh, D., & Jaya, T. (2022). Enhancement of network lifetime by fuzzy based secure CH clustered routing protocol for mobile wireless sensor network. J Ambient Intell Human Comput vol. 13, pp. 2795–2805.
• Rajesh, D., Giji Kiruba, D., & Ramesh, D. (2023). Energy Proficient Secure Clustered Protocol in Mobile Wireless Sensor Network Utilizing Blue Brain Technology. Indian Journal of Information Sources and Services, 13(2): 30–38.
• Biradar, S. P., & Vishwanath, D. T. S. (2018). Network lifetime maximization of sensor network based on energy aware source tree routing. Int. J. Adv. Netw. Appl., vol. 10, no. 02, pp. 3788_3793.
• Saeed Mehrjoo, & Farshad Khunjush. (2018). Optimal data aggregation tree in wireless sensor networks based on improved river formation dynamics. Comput. Intell., vol. 34, no. 3, pp. 802_820.
• Rajesh, D., & Jaya, T. (2019). Exploration on Cluster Related Energy Proficient Routing in Mobile Wireless Sensor Network. International Journal of Innovative Technology and Exploring Engineering (IJITEE), Vol. 8, no. 4, pp. 93-97.
• Aydın, V. A. (2024). Comparison of CNN-based methods for yoga pose classification. Turkish Journal of Engineering, 8 (1), 65-75
• Lin, H., Bai, D., & Liu, Y. (2019). Maximum data collection rate routing for data gather trees with data aggregation in rechargeable wireless sensor networks,.Cluster Comput., vol. 22, no. S1, pp. 597_607.
• Dennison, R., Dennison, R., Dasebenezer, G.K., & Chinnathurai, E.S. (2023). Enhancing lifespan and energy efficiency in mobile smart dust networks. Ingénierie des Systèmes d’Information., 28(5): 1317-1323
• Osamy, W., Khedr, A. M., & Aziz, A. (2018). El-Sawy Cluster-tree routing based entropy scheme for data gathering in wireless sensor networks. IEEE Access, vol. 6, pp. 77372_77387.
• Osaba, E., Yang, X.S., Fister, I., Del Ser, J., Lopez Garcia, P., & Vazquez Pardavila, A. J. (2019). A discrete and improved bat algorithm for solving a medical goods distribution problem with pharmacological waste collection. Swarm Evol. Comput., vol. 44, pp. 273_286.
• Wang, J., Gao, Y., Liu, W., Sangaiah, A. K., & Kim H.J. (2019). An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks. Int. J. Distrib. Sensor Netw., vol. 15, no. 3, Art. no. 155014771983958.
• Dennison, R., Dasebenezer, G. K., & Dennison, R. (2024). Energy capable protocol for heterogeneous blue brain network. Turkish Journal of Engineering, 8(1), 152-161
• Chaturvedi, I., Ragusa, E., Gastaldo, P., Zunino, R., & Cambria, E. (2018). Bayesian network based extreme learning machine for subjectivity detection. J. Franklin Inst., vol. 355, no. 4, pp. 1780_1797.
• Sattar, A. M. A., Ertugrul, Ö. F., Gharabaghi, B., Mcbean, E. A., & Cao, J. (2019). Extreme learning machine model for water network management. Neural Comput. Appl., vol. 31, no. 1, pp. 157_169.
• Ji, S., Tan, C., Yang, P., Sun, Y.J., Fu, D., & Wang, J. (2018). Compressive sampling and data fusion-based structural damage monitoring in wireless sensor network. J Supercomput., vol. 74, no. 3, pp. 1108_1131.