Skip to main content

Advertisement

Springer Nature Link
Log in

Applied fuzzy heuristics for automation of hygienic drinking water supply system using wireless sensor networks

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

About 20% of communicable infectious disease is spread by drinking contaminated water. Hence, a real-time in-pipe drinking water quality system using sensor networks is proposed. The proposed prototype Drinking Water Quality Monitoring System (DWQMS) checks for parameters such as pH, temperature, turbidity, oxidation–reduction potential, conductivity, and dissolved oxygen in the drinking water supplied through pipes by the municipality in a fast and efficient manner. In the proposed work, a sensor network that is powered by solar energy is deployed inside the water pipelines to improve the network connectivity and enhance the network lifetime. The prototype designed uses an Energy Aware Multipath Routing Protocol (EAMRP) to prevent the water flow when contamination is detected in a particular pipeline region without interrupting the supply in non-contaminated regions. The key ingredients of the proposed protocol are an energy-efficient algorithm; maximizing the data correlation among sensors; shortest path routing and fast data transmission algorithm to report the water quality to the users quickly; event detection algorithms to assess the water contamination risks in pipes; and fuzzy rule descriptors to predict the water quality as desirable/acceptable/rejected for drinking with better accuracy. The simulation results show that the designed DWQMS acts as an early warning system and outperforms in terms of energy efficiency, detects the contaminants with better accuracy, increases network lifetime, and better estimates the water quality parameters. The proposed algorithms are tested in a small test bed of wireless sensor networks with 20 nodes that monitor the drinking water quality distributed in water distribution mains, which alert the consumers/houses in the water-contaminated regions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Hall J, Zaffiro AD, Marx RB, Kefauver PC, Krishnan ER, Haught RC, Herrmann JG (2007) On-line water quality parameters as indicators of distribution system contamination. J Am Water Works Assoc 99(1):66–77

    Google Scholar 

  2. Panguluri S, Meiners G, Hall J, Szabo JG (2009) Distribution system water quality monitoring: sensor technology evaluation methodology and results. U.S. Environmental Protection Agency, EPA/600/R-09/076

  3. Zhuiykov S (2012) Solid-state sensors monitoring parameters of water quality for the next generation of wireless sensor networks. Sens Actuators B Chem 161(1):1–20

    Google Scholar 

  4. Lambrou TP, Panayiotou CG, Anastasiou CC (2014) A low-cost sensor network for real time monitoring and contamination detection in drinking water distribution systems. IEEE Sens 14(8):2765–2772

    Google Scholar 

  5. Cloete NA, Malekian R, Nair L (2016) Design of smart sensors for real-time water quality monitoring. IEEE Access 4:3975–3990

    Google Scholar 

  6. Skadsen J, Janke R, Grayman W, Samuels W, Tenbroek M, Steglitz B, Bahl S (2008) Distribution system on-line monitoring for detecting contamination and water quality changes. J Am Water Works Assoc 100(7):81–94

    Google Scholar 

  7. Whittle J, Girod L, Preis A, Allen M, Lim HB, Iqbal M, Srirangarajan S, Fu C, Wong KJ, Goldsmith D (2010) Waterwise@sg: a Testbed for Continuous Monitoring of the Water Distribution System in Singapore. In: Water Distribution System Analysis

  8. Whittle AJ, Allen M, Preis A, Iqbal M (2013) Sensor networks for monitoring and control water distribution systems. In: 6th International Conference on Structural Health Monitoring of Intelligent Infrastructure

  9. Ch I, Wu R (2012) Robust maximum lifetime routing and energy allocation in wireless sensor networks. Center for Information and Systems Engineering, Boston University, Boston

    Google Scholar 

  10. Akyildiz F, Su W, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun Mag 40(8):102–114

    Google Scholar 

  11. Chang JH, Tassiulas L (2004) Maximum lifetime routing in wireless sensor networks. IEEE/ACM Trans Netw 12(4):609–619

    Google Scholar 

  12. Jarry A, Leone P, Powell O, Rolim J (2006) An optimal data propagation algorithm for maximizing the lifespan of sensor networks. In: Distributed Computing in Sensor Systems, pp 405–421

    Google Scholar 

  13. Wattenhofer R, Zollinger A (2004) XTC: a practical topology control algorithm for ad-hoc networks. In: Parallel and Distributed Processing Symposium, pp 216–222

  14. Shiri ME, Kadivar M, Dehghan M (2009) Distributed topology control algorithm based on one- and two-hop neighbors’ information for ad hoc networks. Comput Commun 32(2):368–375

    Google Scholar 

  15. Huang CJ, Wang YW, Liao HH, Lin CF, Hu KW, Chang TY (2011) A power-efficient routing protocol for underwater wireless sensor networks. J Appl Soft Comput 11(2):2348–2355

    Google Scholar 

  16. Priya SK, Revathi T, Muneeswaran K, Vijayalakshmi K (2016) Heuristic routing with bandwidth and energy constraints in sensor networks. J Appl Soft Comput 29(1):12–25

    Google Scholar 

  17. Chettibi S, Chikhi S (2016) Dynamic fuzzy logic and reinforcement learning for adaptive energy efficient routing in mobile ad-hoc networks. J Appl Soft Comput 38:321–328

    Google Scholar 

  18. Priya SK, Revathi T, Muneeswaran K (2017) Fuzzy-based multi-constraint multi-objective QoS aware routing heuristics for query driven sensor networks. Int J Appl Soft Comput 52:532–548

    Google Scholar 

  19. Liu RS, Sinha P, Koksal CE (2010) Joint energy management and resource allocation in rechargeable sensor networks. In: IEEE INFOCOM. IEEE Communications Society, pp 902–910

  20. Wang X, Kar K (2006) Cross-layer rate optimization for proportional fairness in multihop wireless networks with random access. IEEE J Sel Areas Commun 24(8):1548–1599

    Google Scholar 

  21. Chen S, Fang Y, Xia Y (2007) Lexicographic max–min fairness for data collection in wireless sensor networks. IEEE Trans Mob Comput 6(7):762–776

    Google Scholar 

  22. Hou YT, Shi Y, Sherali HD (2008) Rate allocation and network lifetime problems for wireless sensor networks. IEEE/ACM Trans Netw 16(2):321–334

    Google Scholar 

  23. Fan KW, Zheng Z, Sinha P (2008) Steady and fair rate allocation for rechargeable sensors in perpetual sensor networks. In: Computer-Communication Networks. ACM, pp 978–990

  24. Liu RS, Fan KW, Zheng Z, Sinha P (2011) Perpetual and fair data collection for environmental energy harvesting sensor networks. IEEE/ACM Trans Netw 19(4):947–960

    Google Scholar 

  25. Prauzek M, Krömer P, Rodway J, Musilek P (2016) Differential evolution of fuzzy controller for environmentally-powered wireless sensors. J Appl Soft Comput 48:193–206

    Google Scholar 

  26. Mckenna A, Wilson M, Klise KA (2008) Detecting changes in water quality data. Am Water Works Assoc 100(1):74–85

    Google Scholar 

  27. Hach HST (2008) Guardian blue early warning system brochure. Hach Company, Loveland

    Google Scholar 

  28. Whitewater Technologies (2012) Blue box intelligent water analytics system brochure

  29. Hart D, McKenna S, Klise K, Cruz V, Wilson M (2007) CANARY: a water quality event detection algorithm development tool. In: World Environmental and Water Resources Congress

  30. Llinas J, Hall DL (1998) An introduction to multi-sensor data fusion. In: IEEE International Symposium on Circuits and Systems, vol 6, pp 537–540

  31. Su IJ, Tsai CC, Sung WT (2012) Area temperature system monitoring and computing based on adaptive fuzzy logic in wireless sensor networks. J Appl Soft Comput 12(5):1532–1541

    Google Scholar 

  32. Doctor F, Syue CH, Liu YX, Shieh JS, Iqbal R (2016) Type-2 fuzzy sets applied to multivariable self-organizing fuzzy logic controllers for regulating anesthesia. J Appl Soft Comput 38:872–889

    Google Scholar 

  33. Lermontov A, Yokoyama L, Lermontov M, Machado MAS (2011) a fuzzy water quality index for watershed quality analysis and management. In: Environmental Management in Practice, pp 387–410

    Google Scholar 

  34. Aakame RB, Fekhaoui M, Bellaouchou A, El Abidi A, El Abbassi M, Saoiabi A (2015) Assessment of physicochemical quality of water from groundwater in the areas of northwest of Morocco and health hazard. J Mater Environ Sci 6(5):1228–1233

    Google Scholar 

  35. Cude G (2001) Oregon Water Quality Index: a tool for evaluating water quality management effectiveness. J Am Water Resour Assoc 37(1):125–137

    Google Scholar 

  36. Nasr AS, Rezaei M, Barmaki MD (2012) Analysis of groundwater quality using Mamdani fuzzy inference system (MFIS) in Yazd Province. Iran. Int J Comput Appl 59(7):45–53

    Google Scholar 

  37. Chu X, Sethu H (2015) Cooperative topology control with adaptation for improved lifetime in wireless ad hoc networks. Ad Hoc Netw 30:99–114

    Google Scholar 

  38. Atkinson AB (1970) On the measurement of inequality. J Econ Theory 2(3):244–263

    MathSciNet  Google Scholar 

Download references

Acknowledgements

This work is jointly supported by Department of Science and Technology, Government of India, under Water Technology Initiative (WTI) scheme with [Grant Number: DST/TM/WTI/2K14/216].

Author information

Authors and Affiliations

  1. Department of Information Technology, Mepco Schlenk Engineering College (Autonomous), Sivakasi, India

    S. Kavi Priya, G. Shenbagalakshmi & T. Revathi

Authors
  1. S. Kavi Priya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Shenbagalakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. Revathi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Kavi Priya.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kavi Priya, S., Shenbagalakshmi, G. & Revathi, T. Applied fuzzy heuristics for automation of hygienic drinking water supply system using wireless sensor networks. J Supercomput 76, 4349–4375 (2020). https://doi.org/10.1007/s11227-018-2341-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2341-6

Keywords