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A Raw Water Quality Monitoring System using Wireless Sensor Networks

by Nahshon Mokua, Ciira Wa Maina, Henry Kiragu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 21
Year of Publication: 2021
Authors: Nahshon Mokua, Ciira Wa Maina, Henry Kiragu

Nahshon Mokua, Ciira Wa Maina, Henry Kiragu . A Raw Water Quality Monitoring System using Wireless Sensor Networks. International Journal of Computer Applications. 174, 21 ( Feb 2021), 35-42. DOI=10.5120/ijca2021921113

@article{ 10.5120/ijca2021921113,
author = { Nahshon Mokua, Ciira Wa Maina, Henry Kiragu },
title = { A Raw Water Quality Monitoring System using Wireless Sensor Networks },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2021 },
volume = { 174 },
number = { 21 },
month = { Feb },
year = { 2021 },
issn = { 0975-8887 },
pages = { 35-42 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2021921113 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:22:44.880845+05:30
%A Nahshon Mokua
%A Ciira Wa Maina
%A Henry Kiragu
%T A Raw Water Quality Monitoring System using Wireless Sensor Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 21
%P 35-42
%D 2021
%I Foundation of Computer Science (FCS), NY, USA

Water treatment can be promoted through keen consideration of raw water quality parameters (Turbidity and pH). This paper discusses the development of a real-time water quality monitoring system using wireless sensor networks. At first, we present performance experiments on LoRa technology connectivity for wireless sensor networks in a rural set up of Dedan Kimathi University of Technology in Kenya. The specific sensors used for the developed system included: The DFRobot gravity Arduino turbidity sensor and the DFRobot's Gravity Analog pH Sensor. The sensed data values of these parameters were relayed to a gateway by a LoRaWAN transceiver. The gateway then uploaded the received parameter data values to The Things Network platform which was interfaced with a Google Cloud Platform, where an InfluxdB Virtual Machine database stored the received data. A web-based application (Dash Plotly app) was developed and interlinked with the database for analysis and visualization of the received data in real time. The system was deployed at the Nyeri Water and Sanitation Company treatment plant based at Nyeri town, Kenya, from 4th November, 2020 to 4th January, 2021. The dataset obtained contained a total of 2,658 records, each collected after every 30 minutes. Using a subset of 291 records, extensive experiments were performed for the evaluation and assessment of machine learning anomaly detection algorithms of the Local Outlier Factor, the Isolation Forest, Extended Isolation Forest, and the Robust Random Cut Forest for each of the two parameters; Turbidity and pH. From analysis results, the Local Outlier Factor algorithm outperformed all the other algorithms evaluated.

  1. T. P. Lambrou and C. G. Panayiotou, "Collaborative Area Monitoring Using Wireless Sensor Networks with Stationary and Mobile Nodes," EURASIP Journal on Advances in Signal Processing, vol. 2009, no. 1, 2009.
  2. K. K. Khedo, R. Perseedoss and A. Mungur, "A Wireless Sensor Network Air Pollution Monitoring System," International Journal of Wireless & Mobile Networks, vol. 2, no. 2, pp. 31- 45, 2020.
  3. "Water: A Shared Responsibility – The United Nations World Water Development Report 2," Development in Practice, vol. 17, no. 2, pp. 309-311, 2007.
  4. "Millennium development goals: time to reassess strategies," BMJ, vol. 331, no. 7525, 2005.
  5. J. Bhardwaj, K. K. Gupta and R. Gupta, "Towards a cyber-physical era: soft computing framework based multi-sensor array for water quality monitoring," Drinking Water Engineering and Science, vol. 11, no. 1, pp. 9-17, 2018.
  6. K. E. Sawaya, L. G. Olmanson, N. J. Heinert, P. L. Brezonik and M. E. Bauer, "Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery," Remote Sensing of Environment, vol. 88, no. 1-2, pp. 144-156, 2003.
  7. K. S. Adu-Manu, C. Tapparello, W. Heinzelman, F. A. Katsriku and J.-D. Abdulai, "Water Quality Monitoring Using Wireless Sensor Networks," ACM Transactions on Sensor Networks, vol. 13, no. 1, pp. 1-41, 2017.
  8. G. Tuna, B. Nefzi, O. Arkoc and S. M. Potirakis, "Wireless Sensor Network-Based Water Quality Monitoring System," Key Engineering Materials, vol. 605, pp. 47-50, 2014.
  9. J. Liu, X. Wei, S. Bai, X. Bai and X. Wang, "Autonomous underwater vehicles localisation in mobile underwater networks," International Journal of Sensor Networks, vol. 23, no. 1, p. 61, 2017.
  10. F. Ge and Y. Wang, "Energy Efficient Networks for Monitoring Water Quality in Subterranean Rivers," Sustainability, vol. 8, no. 6, p. 526, 2016.
  11. A. Alkandari, M. alnasheet, Y. Alabduljader and S. M. Moein, "Water monitoring system using Wireless Sensor Network (WSN): Case study of Kuwait beaches," 2012 Second International Conference on Digital Information Processing and Communications (ICDIPC), 2012.
  12. J. Hall, A. D. Zaffiro, R. B. Marx, P. C. Kefauver, E. R. Krishnan, R. C. Haught and J. G. Herrmann, "On-Line water quality parameters as indicators of distribution system contamination," Journal - American Water Works Association, vol. 99, no. 1, pp. 66-77, 2007.
  13. M. Allegretti, "Concept for Floating and Submersible Wireless Sensor Network for Water Basin Monitoring," Wireless Sensor Network, vol. 06, no. 06, pp. 104-108, 2014.
  14. F. Gui and X. Q. Liu, "Design for Multi-Parameter Wireless Sensor Network Monitoring System Based on Zigbee," Key Engineering Materials, vol. 464, pp. 90-94, 2011.
  15. Z. Rasin and M. R. Abdullah, "Water Quality Monitoring System Using Zigbee Based Wireless Sensor Network," Int. J. Eng. Technol., vol. 9, no. 10, pp. 14-18, 2009.
  16. M. Jared, K. Ngetich and M. Ciira, "Long Range Low Power Sensor Networks for Agricultural Monitoring - A Case Study in Kenya," in 2019 IST-Africa Week Conference (IST-Africa), Nairobi, 2019.
  17. M. M. Breunig, H.-P. Kriegel, R. T. Ng and J. Sander, "LOF: Identifying Density-Based Local Outliers," Association for Computing Machinery, p. 93–104, 2000.
  18. S. Guha, N. Mishra, G. Roy and O. Schrijvers, "Robust random cut forest based anomaly detection on streams," International conference on machine learning, pp. 2712-2721, 201.
Index Terms

Computer Science
Information Sciences


Water quality monitoring wireless sensor networks anomaly detection local outlier factor isolation forest extended isolation forest robust random cut forest.