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.
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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
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DOI: https://doi.org/10.1007/978-981-15-0637-6_41
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