Abstract
Wireless sensor network (WSN) is the network of sensor nodes set up to supervise physical observable fact. Congestion is state in the network when too many packets are present in the network than capacity of network. Congestion can be at node level or link level. Our work is to related to node level congestion. Because of funnel like topology of wireless sensor network, the congestion occurs at the nodes near sink as all the nodes start sending data to sink node whenever an event occurs. Congestion detection is vital as it leads in poor performance of network. In this paper we have implemented the machine learning techniques to detect congestion in wireless sensor network using Ensemble approach of clustering and classification. The Naïve Bayes classification based on the K- means and Expectation- Maximization clustering algorithms are applied to generate the classifier model. The classification model is also generated using only Naïve Bayes algorithm and the performance is compared with classifier of ensemble approach. The analysis of performance parameters of the generated models indicates that EM based Naïve Bayes classifier model is more accurate in detection of the congestion for the generated our network data set.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dawhkova, E., Gurtov, A.: Survey on congestion control mechanisms for wireless sensor networks. Center for Wireless Communication, Finland (2013)
Madalgi, J.B., Kumar, S.A.: Development of wireless sensor network congestion detection classifier using support vector machine. In: IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS-2018). RVCE, Bengaluru, IEEE Digital Library (2018). ISBN 978-1-5386-6078-2
Karami, H., Taheri, M.: A novel framework to generate clustering algorithms based on a particular classification structure. In: Artificial Intelligence and Signal Processing Conference (AISP), Shiraz, pp. 201–204 (2017). https://doi.org/10.1109/AISP.2017.8324081
Chakraborty, T.: EC3: combining clustering and classification for ensemble learning. In: IEEE International Conference on Data Mining (ICDM), New Orleans, LA, pp. 781–786 (2017). https://doi.org/10.1109/ICDM.2017.92
Erol, H., Tyoden, B.M., Erol, R.: Classification performances of data mining clustering algorithms for remotely sensed multispectral image data. In: 2018 Innovations in Intelligent Systems and Applications (INISTA), Thessaloniki, pp. 1–4 (2018). https://doi.org/10.1109/INISTA.2018.8466320
Zhou, L., Wang, L., Ge, X., Shi, Q.: A clustering-based KNN improved algorithm CLKNN for text classification. In: 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics, Wuhan, pp. 212–215 (2010). https://doi.org/10.1109/CAR.2010.5456668
Ru, X., Liu, Z., Huang, Z., Jiang, W.: Class discovery based on K-means clustering and perturbation analysis. In: 2015 8th International Congress on Image and Signal Processing (CISP), Shenyang, pp. 1236–1240 (2015). https://doi.org/10.1109/CISP.2015.7408070
Alapati, Y.K.: Combining clustering with classification: a technique to improve classification accuracy. Int. J. Comput. Sci. Eng. 5(6), 2319–7323 (2016)
Papas, D., Tjortjis, C.: Combining clustering and classification for software quality evaluation. In: Likas, A., Blekas, K., Kalles, D. (eds.) Artificial Intelligence: Methods and Applications, SETN 2014. Lecture Notes in Computer Science, vol. 8445. Springer, Cham (2014)
de Oliveira, E., Basoni, H.G., Saúde, M.R., Ciarelli, P.M.: Combining clustering and classification approaches for reducing the effort of automatic tweets classification. In: Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR-2014), pp. 465–472 (2014). https://doi.org/10.5220/0005159304650472
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining (2009)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Madalgi, J.B., Anupama Kumar, S. (2020). Efficiency of Naïve Bayes Technique Based on Clustering to Detect Congestion in Wireless Sensor Network. In: Hemanth, D., Shakya, S., Baig, Z. (eds) Intelligent Data Communication Technologies and Internet of Things. ICICI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-34080-3_84
Download citation
DOI: https://doi.org/10.1007/978-3-030-34080-3_84
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34079-7
Online ISBN: 978-3-030-34080-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)