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Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

Anomaly detection on compressed data in resource-constrained smart water meters

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DOI: http://dx.doi.org/10.15439/2022F123

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 635639 ()

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Abstract. Increasing amount of devices in our daily life are equipped with sensors that transfer information to a cloud solution where the data is finally analysed. By improving the data intelligence on the edge, the data transfer can be reduced, which not only saves bandwidth and thus reduces energy consumption, but also leads to increased privacy protection. In this paper, we propose a privacy-friendly water leakage detection approach for various kind of water meters (optical and digital) performed on a very constrained, wireless devices.


  1. H. Fuentes and D. Mauricio, “Smart water consumption measurement system for houses using iot and cloud computing,” Environmental Monitoring and Assessment, vol. 192, no. 9, pp. 1–16, 2020. http://dx.doi.org/10.1007/s10661-020-08535-4
  2. M. Fagiani, S. Squartini, L. Gabrielli, M. Severini, and F. Piazza, “A statistical framework for automatic leakage detection in smart water and gas grids,” Energies, vol. 9, no. 9, p. 665, 2016. http://dx.doi.org/10.3390/en9090665
  3. S. Alvisi, F. Casellato, M. Franchini, M. Govoni, C. Luciani, F. Poltronieri, G. Riberto, C. Stefanelli, and M. Tortonesi, “Wireless middleware solutions for smart water metering,” Sensors, vol. 19, no. 8, p. 1853, 2019. http://dx.doi.org/10.3390/s19081853
  4. S. Kartakis, W. Yu, R. Akhavan, and J. A. McCann, “Adaptive edge analytics for distributed networked control of water systems,” in 2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI). IEEE, 2016. http://dx.doi.org/10.1109/IoTDI.2015.34 pp. 72–82.
  5. J. Kraus and V. Bubla, “Optimal methods for data storage in performance measuring and monitoring devices.” in Proceedings of electronic power engineering conference, 01 2008. ISBN 9788021436503 pp. 131–133.
  6. T. Britton, G. Cole, R. Stewart, and D. Wiskar, “Remote diagnosis of leakage in residential households,” Journal of Australian Water Association, vol. 35, no. 6, pp. 89–93, 2008.
  7. A. Apostolico and A. Fraenkel, “Robust transmission of unbounded strings using fibonacci representations,” IEEE Transactions on Information Theory, vol. 33, no. 2, pp. 238–245, 1987. http://dx.doi.org/10.1109/TIT.1987.1057284
  8. S. T. Klein and M. K. Ben-Nissan, “On the usefulness of fibonacci compression codes,” The Computer Journal, vol. 53, no. 6, pp. 701–716, 2010. http://dx.doi.org/10.1093/comjnl/bxp046
  9. A. Robinson and C. Cherry, “Results of a prototype television bandwidth compression scheme,” Proceedings of the IEEE, vol. 55, no. 3, pp. 356–364, 1967. http://dx.doi.org/10.1109/PROC.1967.5493
  10. C. Beckel, L. Sadamori, and S. Santini, “Automatic socio-economic classification of households using electricity consumption data,” in Proceedings of the fourth international conference on Future energy systems, 2013. http://dx.doi.org/10.1145/2487166.2487175 pp. 75–86.
  11. C. Kittel, Elementary statistical physics. Courier Corporation, 2004.
  12. S. Kullback and R. A. Leibler, “On information and sufficiency,” The Annals of Mathematical Statistics, vol. 22, no. 1, pp. 79–86, 1951. http://dx.doi.org/10.1214/aoms/1177729694. [Online]. Available: http://www.jstor.org/stable/2236703
  13. Z. Li, T. J. Oechtering, and D. Gündüz, “Privacy against a hypothesis testing adversary,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 6, pp. 1567–1581, 2018. http://dx.doi.org/10.1109/TIFS.2018.2882343