Continuous, online monitoring and analysis in large water distribution networks

  • Authors:

  • Xiuli Ma;Hongmei Xiao;Shuiyuan Xie;Qiong Li;Qiong Luo;Chunhua Tian

  • Affiliations:

  • School of Electronics Engineering and Computer Science, Peking University, Beijing, China;School of Electronics Engineering and Computer Science, Peking University, Beijing, China;School of Electronics Engineering and Computer Science, Peking University, Beijing, China;School of Electronics Engineering and Computer Science, Peking University, Beijing, China;Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong;IBM Research - China, Beijing, China

  • Venue:
  • ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
  • Year:
  • 2011

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Abstract

Clean drinking water and safe water supply is vital to our life. Recent advances in technologies have made it possible to deploy smart sensor networks in large water distribution networks to monitor and identify the water quality online. In such a large-scale real-time monitoring application, large amounts of data stream out of multiple concurrent sensors continuously. In this paper, we present a system to monitor and analyze the sensor data streams online, find and summarize the spatio-temporal distribution patterns and correlations in co-evolving data, detect contamination events rapidly and facilitate corrective actions or notification. The system consists of an online data mining engine and a GUI providing the user with the current patterns discovered in the network, and an alerter notifying the user if there is anomalous water quality in the network.