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A Spark-based genetic algorithm for sensor placement in large scale drinking water distribution systems

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Abstract

Water pollution incidents have occurred frequently in recent years, causing severe damages, economic loss and long-lasting society impact. A viable solution is to install water quality monitoring sensors in water supply networks (WSNs) for real-time pollution detection, thereby mitigating the risk of catastrophic contamination incidents. Given the significant cost of placing sensors at all locations in a network, a critical issue is where to deploy sensors within WSNs, while achieving rapid detection of contaminant events. Existing studies have mainly focused on sensor placement in water distribution systems (WDSs). However, the problem is still not adequately addressed, especially for large scale WSNs. In this paper, we investigate the sensor placement problem in large scale WDSs with the objective of minimizing the impact of contamination events. Specifically, we propose a two-phase Spark-based genetic algorithm (SGA). Experimental results show that SGA outperforms other traditional algorithms in both accuracy and efficiency, which validates the feasibility and effectiveness of our proposed approach.

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References

  1. Hrudey, S., Payment, P., Huck, P., Gillham, R., Hrudey, E.: A fatal waterborne disease epidemic in Walkerton, Ontario: comparison with other waterborne outbreaks in the developed world. Water Sci. Technol. 47(3), 7–14 (2003)

    Article  Google Scholar 

  2. Kou, J.: Drinking water reservoirs were poisoned in Ruyang City of Henan Province. http://news.china.com/zh_cn/social/1007/20031004/11549967.html

  3. Hart, W.E., Berry, J.W., Boman, E.G., Phillips, C.A., Riesen, L.A., Watson, J.: Limited-memory techniques for sensor placement in water distribution networks. In: Learning and Intelligent Optimization. Springer, Berlin (2008)

  4. Zeng, D., Gu, L., Lian, L., Guo, S., Yao, H., Hu, J.: On cost-efficient sensor placement for contaminant detection in water distribution systems. IEEE Trans. Ind. Inf. 11, 112–121 (2016)

    Google Scholar 

  5. Xu, J., Johnson, M.P., Fischbeck, P.S., Small, M.J., Vanbriesen, J.M.: Robust placement of sensors in dynamic water distribution systems. Eur. J. Oper. Res. 202(3), 707–716 (2010)

    Article  MATH  Google Scholar 

  6. Bergerwolf, T.Y., Hart, W., Saia, J.: Discrete sensor placement problems in distribution networks. Math. Comput. Model. 42(13), 1385–1396 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  7. Carr, R.D., Greenberg, H.J., Hart, W.E., Konjevod, G., Lauer, E., Lin, H., Morrison, T., Phillips, C.A.: Robust optimization of contaminant sensor placement for community water systems. Math. Program. 107(1–2), 337–356 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  8. Ostfeld, A., Uber, J.G., Salomons, E., Berry, J.W., Hart, W.E., Phillips, C.A., Watson, J.-P., Dorini, G., Jonkergouw, P., Kapelan, Z., et al.: The battle of the water sensor networks (BWSN): a design challenge for engineers and algorithms. J. Water Resour. Plan. Manag. 134(6), 556–568 (2008)

    Article  Google Scholar 

  9. Krause, A., Leskovec, J., Guestrin, C., VanBriesen, J., Faloutsos, C.: Efficient sensor placement optimization for securing large water distribution networks. J. Water Resour. Plan. Manag. 134(6), 516–526 (2008)

    Article  Google Scholar 

  10. Chang, N.-B., Prapinpongsanone, N., Ernest, A.: Optimal sensor deployment in a large-scale complex drinking water network: comparisons between a rule-based decision support system and optimization models. Comput. Chem. Eng. 43, 191–199 (2012)

    Article  Google Scholar 

  11. Gong, W., Cai, Z.: Differential evolution with ranking-based mutation operators. IEEE Trans. Syst. Man Cybernet. 43(6), 2066–2081 (2013)

    Google Scholar 

  12. Yang, M., Li, C., Cai, Z., Guan, J.: Differential evolution with auto-enhanced population diversity. IEEE Trans. Syst. Man Cybernet. 45(2), 302–315 (2015)

    Google Scholar 

  13. Von Laszewski, G., Wang, L., Wang, F., Fox, G.C., Mahinthakumar, G.K.: Threat detection in an urban water distribution systems with simulations conducted in grids and clouds. In: Proceedings of the Second International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering, Ajaccio, Corsica, France (2011)

  14. Wu, Z., Khaliefa, M.: Cloud computing for high performance optimization of water distribution systems. In: Proceedings of Albuquerque, New Mexico, World Environmental and Water Resources Congress (2012)

  15. Shen, H., McBean, E.A.: Application of parallel computing in data mining for contaminant source identification in water distribution systems. Can. Water Resour. J. 38(1), 34–39 (2013)

    Article  Google Scholar 

  16. Wang, L., Chen, D., Liu, W., Ma, Y., Wu, Y., Deng, Z.: DDDAS-based parallel simulation of threat management for urban water distribution systems. Comput. Sci. Eng. 16(1), 8–17 (2014)

    Article  Google Scholar 

  17. Rathi, S., Gupta, R.: A critical review of sensor location methods for contamination detection in water distribution networks. Water Qual. Res. J. Can. 50(2), 95–108 (2015)

    Article  Google Scholar 

  18. Wu, J., Zhu, X., Zhang, C., Yu, P.S.: Bag constrained structure pattern mining for multi-graph classification. IEEE Trans. Knowl. Data Eng. 26(10), 2382–2396 (2014)

    Article  Google Scholar 

  19. Wu, J., Pan, S., Zhu, X., Zhang, P., Zhang, C.: SODE: self-adaptive one-dependence estimators for classification. Pattern Recogn. 51, 358–377 (2016)

    Article  Google Scholar 

  20. Guan, J., Aral, M.M., Maslia, M.L., Grayman, W.M.: Optimization model and algorithms for design of water sensor placement in water distribution systems. In: 8th Annual Symposium on Water Distribution Systems Analysis. Environmental and Water Resources Institute of ASCE (EWRI of ASCE) New York, pp. 1–16 (2006)

  21. Liu, S., Auckenthaler, P.: Optimal sensor placement for event detection and source identification in water distribution networks. J. Water Supply 63(1), 51–57 (2014)

    Article  Google Scholar 

  22. Schwartz, R., Lahav, O., Ostfeld, A.: Optimal sensor placement in water distribution systems for injection of chlorpyrifos. In: World Environmental and Water Resources Congress 2014@ sWater Without Borders. ASCE, pp 485–494 (2014)

  23. Afshar, A., Mariño, M.A.: Multi-objective coverage-based aco model for quality monitoring in large water networks. Water Resour. Manag. 26(8), 2159–2176 (2012)

    Article  Google Scholar 

  24. Ma, Y., Wang, L., Liu, D., Liu, P., Wang, J., Tao, J.: Generic parallel programming for massive remote sensing data processing. In: International Conference on Cluster Computing, pp 420–428 (2012)

  25. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Operating Systems Design and Implementation, vol. 51(1), pp 107–113. Prentice-Hall, Upper Saddle River (2008)

  26. Wang, L., Tao, J., Ranjan, R., Marten, H., Streit, A., Chen, J., Chen, D.: G-Hadoop: Mapreduce across distributed data centers for data-intensive computing. Future Gen. Comput. Syst. 29(3), 739–750 (2013)

    Article  Google Scholar 

  27. Zhao, J., Wang, L., Tao, J., Chen, J., Sun, W., Ranjan, R., Kolodziej, J., Streit, A., Georgakopoulos, D.: A security framework in G-Hadoop for big data computing across distributed cloud data centres. J. Comput. Syst. Sci. 80(5), 994–1007 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  28. Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., Mccauley, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. Technical Report (2012)

  29. U.S. EPA: Tutorial threat ensemble vulnerability analysis—sensor placement optimization tool TEVA-SPOT graphical user interface, version 2.2.0 Beta, EPA-600-R-08-147. Office of Research and Development, National Homeland Security Research Center, vol. 1(1), pp. 1–183 (2009)

  30. Luque, G., Alba, E.: Parallel Genetic Algorithms: Theory and Real World Applications, vol. 367. Springer, Berlin (2011)

    MATH  Google Scholar 

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Acknowledgements

This research was supported in part by the NSF of China (Grant No. 61305087, 61673354, 61672474, 61501412). Ming Li’s research is partially supported by US National Science Foundation Award (Grant No. 1626586). This paper has been subjected to Hubei Key Laboratory of Intelligent Geo-Information Processing within School of Computer Science, China University of Geosciences, Wuhan, China, 430074. It was also supported by Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing (KLIGIP201603, KLIGIP201607).

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, China

    Chengyu Hu, Guo Ren & Chao Liu

  2. Department of Computer Science, California State University, Fresno, CA, USA

    Ming Li

  3. School of Computing and Engineering, University of West London, London, UK

    Wei Jie

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  1. Chengyu Hu
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  2. Guo Ren
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  3. Chao Liu
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  4. Ming Li
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Correspondence to Chao Liu.

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Hu, C., Ren, G., Liu, C. et al. A Spark-based genetic algorithm for sensor placement in large scale drinking water distribution systems. Cluster Comput 20, 1089–1099 (2017). https://doi.org/10.1007/s10586-017-0838-z

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  • DOI: https://doi.org/10.1007/s10586-017-0838-z

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