Abstract
Water is considered as the most important resource in our life. Pipelines are considered as very important solution to transport water. So, due to the existence of the harsh environmental condition, different detection ways are not great to monitor pipelines. Therefore, all used systems need to be improved to become more efficient. For this reason, wireless sensor networks (WSNs) are used in water pipeline field. This latter are employed to solve different problems. In this paper, the low-cost damage detection technique for outlier is provided to discuss the task amounts. Our proposed solution uses kernel principal component analysis (KPCA). We aim at analyzing the nature of information. Determine if it is normal or abnormal to help to identify specific events in WSN field for water pipeline. Using real data collected from different stations in WSNs, this solution shows a higher performance in finding abnormal data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
D. Wachla, P. Przystalka, W. Moczulski, A method of leakage location in water distribution networks using artificial neuro-fuzzy system. IFAC-Papers OnLine 48(21), 1216–1223 (2015)
X. Deng, X. Tian, S. Chen, C.J. Harris, Fault discriminant enhanced kernel principal component analysis incorporating prior fault information for monitoring nonlinear processes. Chemom. Intell. Lab. Syst. 162, 21–34 (2017)
Y, Zhang, N, Meratnia, P, Havinga, Outlier detection Techniques for wireless sensor networks: a survey. IEEE Commun. Surv. Tutorials 12, 159–170 (2010)
D. Cai, X. He, J. Han, T.S. Huang, Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1548–1560 (2011)
O. Ghorbel, M. Abid, H. Snoussi, Improved KPCA for outlier detection in Wireless Sensor Networks, in 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (2014), pp. 507–511
Chakour et al., Adaptive Kernel Principal Component Analysis for Nonlinear Time-Varying Processes Monitoring ICEECA2012
H.K. Verma, V.S. Samparthi, Article: outlier detection of data in wireless sensor networks using kernel density estimation. Int. J. Comput. Appl., (Published by Foundation of Computer Science, 2010), pp. 28–32
M.A. Rassam, A. Zainal, M.A. Maarof, An adaptive and efficient dimension reduction model for multivariate wireless sensor networks applications. Appl. Soft Comput. (2013)
K. Kapitanova, S.H. Son, K.D. Kang, Event detection in wireless sensor networks, in Second International Conference, ADHOCNETS2010, Victoria, BC, Canada, August 2010
Y. Zhang, N. Meratnia, P. J.M. Havinga, Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine. Ad Hoc Networks, December 2012
T. Nakanishi, A generative wireless sensor network framework for agricultural use, in Makassar International Conference on Electrical Engineering and Informatics (MICEEI) (2014), pp. 205–211
N. Chitradevi, V. Palanisamy, K. Baskaran, U.B. Nisha, Outlier aware data aggregation in distributed wireless sensor network using robust principal component analysis, in International Conference on Computing Communication and Networking Technologies (2010), pp. 1–9
Y. Zhang, N.A.S. Hammb, N. Meratnia, A. Steinb, M. Voorta, P.J.M. Havinga, Statistics-based outlier detection for wireless sensor networks. Int. J. Geogr. Inf. Sci. 26(8), 1373–1392 (2012)
M. Ding, Z. Tian, H. Xu, Adaptive kernel principal component analysis. Signal Process, 1542–1553 (2010)
H, Hoffmann, Kernel PCA for novelty detection. Pattern Recogn., 863–874 (2007)
T. Naumowicz, R. Freeman, A. Heil, M. Calsyn, E. Hellmich, A. Brandle, T, Guilford, J. Schiller, Autonomous monitoring of vulnerable habitats using a wireless sensor network, in Proceedings of the Workshop on Real-World Wireless Sensor Networks, REALWSN’08. Glasgow, Scotland (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Aseeri, M., Ghorbel, O., Alshammari, H., Alabdullah, A., Abid, M. (2020). Outlier Detection Method-Based KPCA for Water Pipeline in Wireless Sensor Networks. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1041. Springer, Singapore. https://doi.org/10.1007/978-981-15-0637-6_41
Download citation
DOI: https://doi.org/10.1007/978-981-15-0637-6_41
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0636-9
Online ISBN: 978-981-15-0637-6
eBook Packages: EngineeringEngineering (R0)