Abstract
Till date, the research work in Wireless Sensor Network is mainly inclined towards rectifying the problem associated with the nodes and protocol associated with it, e.g., energy problems, clustering issue, security loopholes, uncertain traffic, etc. However, there is less emphasis towards the user’s demand, i.e., data quality. As wireless nodes undergo various forms of adverse wireless condition in order to carry out data aggregation, it is quite inevitable that an aggregated data forwarded may not have a good data quality. Therefore, we present a novel clustering technique that concentrates on achieving the lowest possible error. With an aid of analytical modeling, a novel clustering technique is formulated using probability theory that targets the node with higher retention of redundant information so that it can be mitigated effectively. The study outcome shows better data quality of the proposed system.
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References
Ibnkahla, M.: Wireless Sensor Networks: A Cognitive Perspective. CRC Press, Boca Raton (2016)
Ray, N.K., Turuk, A.K.: Handbook of Research on Advanced Wireless Sensor Network Applications, Protocols. And Architectures. IGI Global, Hershey (2016)
Forster, A.: Introduction to Wireless Sensor Networks. Wiley, Hoboken (2016)
Khan, S., Pathan, A.S.K., Alraje, N.A.: Wireless Sensor Networks: Current Status and Future Trends. CRC Press, Boca Raton (2016)
Ilyas, M., Mahgoub, I.: Smart Dust: Sensor Network Applications. Architecture and Design. CRC Press, Boca Raton (2016)
Dobre, C., Xhafa, F.: Pervasive Computing: Next Generation Platforms for Intelligent Data Collection. Morgan Kaufmann, Burlington (2016)
Cao, J., Liu, X.: Wireless Sensor Networks For Structural Health Monitoring. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-319-29034-8
Akyildiz, I.F., Vuran, M.C.: Wireless Sensor Networks. Wiley, Hoboken (2010)
EL Emary, I.M.M., Ramakrishnan, S.: Wireless Sensor Networks: From Theory to Applications. CRC Press, Boca Raton (2013)
Prathiba, B., Sankar, K.J., Sumalatha, V.: Enhancing the data quality in wireless sensor networks — a review. In: 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), Pune, pp. 448–454 (2016)
Chidean, M.I., Morgado, E., Sanromán-Junquera, M., Ramiro-Bargueño, J., Ramos, J., Caamaño, A.J.: Energy efficiency and quality of data reconstruction through data-coupled clustering for self-organized large-scale WSNs. IEEE Sens. J. 16(12), 5010–5020 (2016)
Hong, Z., Wang, R., Li, X.: A clustering-tree topology control based on the energy forecast for heterogeneous wireless sensor networks. IEEE/CAA J. Automatica Sinica. 3(1), 68–77 (2016)
Belabed, F., Bouallegue, R.: Performance evaluation of the optimized weighted clustering algorithm in wireless sensor networks. In: IEEE - 31st International Conference on Advanced Information Networking and Applications Workshops (2017)
Belabed, F., Bouallegue, R.: An optimized weight-based clustering algorithm in wireless sensor networks. In: 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), Paphos, pp. 757–762 (2016)
Kumrawat, M., Dhawan, M.: Optimizing energy consumption in wireless sensor network through distributed weighted clustering algorithm. In: IEEE International Conference on Computer, Communication and Control (2015)
Jingxia, Z., Junjie, C., Xu, Z., Liu, Y.: LEACH-WM: weighted and intra-cluster multi-hop energy-efficient algorithm for wireless sensor networks. In: IEEE - Proceedings of the 35th Chinese Control Conference (2016)
Tripathy, A.K., Chinara, S.: Comparison of residual energy-based clustering algorithms for wireless sensor network. Hindawi – Int. Sch. Res. Netw. (2012)
Wang, Y., Guardiola, I.G., Wu, X.: RSSI and LQI data clustering techniques to determine the number of nodes in wireless sensor networks. Int. J. Distrib. Sens. Netw. 10, 380526 (2014)
Liu, Z., Xing, W., Wang, Y., Lu, D.: Hierarchical spatial clustering in multihop wireless sensor networks. Int. J. Distrib. Sens. Netw. 9(11), 528980 (2013)
Ebadi, S.: A multihop clustering algorithm for energy saving in wireless sensor networks. Int. Sch. Res. Netw. ISRN Sens. Netw. 2012 (2012)
Zhang, Y., Xiong, W., Han, D., Chen, W., Wang, J.: Routing algorithm with uneven clustering for energy heterogeneous wireless sensor networks. J. Sens. 2016 (2016)
Zeb, A., Islam, A.K.M.M., Zareei, M.: Clustering analysis in wireless sensor networks: the ambit of performance metrics and schemes taxonomy. Int. J. Distrib. Sens. Netw. 12(7), 4979142 (2016)
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Prathiba, B., Sankar, K.J., Sumalatha, V. (2018). A Novel Clustering Algorithm for Leveraging Data Quality in Wireless Sensor Network. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-10-8657-1_53
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DOI: https://doi.org/10.1007/978-981-10-8657-1_53
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