A sustainable multi-parametric sensors network topology for river water quality monitoring

  • Himanshu Jindal
  • Sharad Saxena
  • Singara Singh Kasana
Article

Abstract

The deterioration of water quality due to natural and man-made hazards has affected the life on the Earth. Hence, water quality needs to be monitored regularly. The traditional approaches for monitoring are observed to be more expensive, time consuming with complex infrastructure and are less accurate. Therefore, there is a scope for improvement in monitoring approaches. For the purpose, the paper has presented multi-parametric sensors network topology (MPST). The topology has polyhedron infrastructure to observe the temporal and spatial variations like electrical conductivity, pH, temperature, chloride and dissolved oxygen; in shallow river water. Its main features are energy efficient, in-expensive infrastructure that requires less manpower, sustainable and can cope with varying currents of water. The MPST is tested at Sutlej river, Bassi, Ludhiana in India and the generated results are analyzed on various physical parameters. Further, it is compared with traditional sampling method for the accuracy. From the results, the topology is identified as an economical, scalable and convenient way for river water quality monitoring.

Keywords

MPST Monitoring pH Temperature EC River Noise Communication Efficient 

List of symbols

\(d_{a}\)

Distance among sensors

\(r_{c}\)

Sensing range

\(\vartheta\)

Ratio of covered and targeted area

\({\mathscr {A}}_{KLM}^{\varDelta }\)

Uncovered area

e

Edge of triangular pyramid

s

Slant of triangular pyramid

h

Height of triangular pyramid

a

Length of side of triangular pyramid

\(H_{t}\)

Depth of river

\(I_{A}, I_{B}\)

Intensity

\(T_{l}\)

Transmission loss

\(S_{l}\)

Source level

\(\nu\)

Noise constant

\(P_{w_{a}}\)

Power consumption

\(A_{l}\)

Path loss

\(f_{all}\)

Frequency

l

Spreading factor

\(N_{H}\)

Number of hops

\(T_{x}\)

Transmission time

\(E_{c}\)

Energy consumed

\(E_{c_{total}}\)

Total energy consumed

\(K_{p}\)

Number of packets

\(A_{p}\)

Loss in multi-path propagation

\(\beta\)

Absorption coefficient

\(D_{s}\)

Depth (in kms)

Nl

Ambient noise

Nt

Turbulence noise

Ns

Shipping noise

Nw

Wave noise

Nth

Thermal noise

dp

Doppler effect

\(amp_{P}(t)\)

Amplitudes of channel

\(\upsilon _{P}\)

Delays

\(P_{s}\)

Propagation speed

\(T_{i_{1}}, T_{i_{2}}\)

Signal time

\(D_{v}\)

Directional vector

\(ME_{err}\)

Mean estimation error

\((X_{i}, Y_{i})\)

Sensor’s position

\((X_{i}^{\prime }, Y_{i}^{\prime })\)

Localization estimated position of sensor

Abbreviations

AoA

Angle of arrival

AS

Anchored sensors

BoS

Bottom sensors

BT-FIDA

Backtracking based installation field deployment algorithm

CCOR

Congestion control

CH

Cluster head

Cl

Chloride

DisSenT

DistriNet Sensor Network Toolkit

DO

Dissolved oxygen

EC

Electrical conductivity

FDOM

Fluorescent dissolved organic matter

GPS

Global positioning system

LOS

Loss in signals

MPS

Multi-parametric sensors

MPST

Multi-parametric sensors network topology

RF

Radio frequency

RSS

Received signal strength

SEMM

Sensor energy management method

S-TDMA

Spatial time division multiple access

TDoA

Time difference of arrival

TDS

Total dissolved solids

ToA

Time of arrival

Temp

Temperature

UV–Vis

Ultraviolet–visible

WSN

Wireless sensors network

References

  1. 1.
    Ainslie, M. A., & McColm, J. G. (1998). A simplified formula for viscous and chemical absorption in sea water. The Journal of the Acoustical Society of America, 103(3), 1671–1672. doi:10.1121/1.421258.CrossRefGoogle Scholar
  2. 2.
    Angove, P., Grady, M. O., Hayes, J., Flynn, B. O., Hare, G. M. P. O., & Diamond, D. (2011). A mobile gateway for remote interaction with wireless sensor networks. IEEE Sensors Journal, 11(12), 3309–3310. doi:10.1109/JSEN.2011.2159199.CrossRefGoogle Scholar
  3. 3.
    Berry, P. A., Smith, R. G., & Benveniste, J. (2012). EnviSat altimetry for river and lakes monitoring. In 2012 IEEE international geoscience and remote sensing symposium (pp. 1844–1847). IEEE.Google Scholar
  4. 4.
    Chandrasekhar, V., Seah, W. K., Choo, Y. S., & Ee, H. V. (2006). Localization in underwater sensor networks: Survey and challenges. In Proceedings of the 1st ACM international workshop on underwater networks (pp. 33–40). ACM.Google Scholar
  5. 5.
    Chung, W. Y., & Yoo, J. H. (2015). Remote water quality monitoring in wide area. Sensors and Actuators B: Chemical, 217, 51–57.CrossRefGoogle Scholar
  6. 6.
    Diamond, D., Coyle, S., Scarmagnani, S., & Hayes, J. (1821). Wireless sensor networks and chemo-/biosensing. Chemical Reviews, 108(2), 652–679. doi:10.1021/cr0681187.CrossRefGoogle Scholar
  7. 7.
    Diamond, D., Lau, K. T., Brady, S., & Cleary, J. (2008b). Integration of analytical measurements and wireless communicationscurrent issues and future strategies. Talanta, 75(3), 606–612. doi:10.1016/j.talanta.2007.11.022. (special section: remote sensing).CrossRefGoogle Scholar
  8. 8.
    Ding, W., Tang, L., & Ji, S. (2016). Optimizing routing based on congestion control for wireless sensor networks. Wireless Networks, 22(3), 915–925.CrossRefGoogle Scholar
  9. 9.
    Dutta, S., Sarma, D., & Nath, P. (2015). Ground and river water quality monitoring using a smartphone-based ph sensor. AIP Advances, 5(5), 057,151.CrossRefGoogle Scholar
  10. 10.
    Foley, J. D., van Dam, A., Feiner, S. K., et al. (2013). Clipping lines. In Computer graphics: Principle and practice (3rd ed.). ISGoogle Scholar
  11. 11.
    Ge, F., & Wang, Y. (2016). Energy efficient networks for monitoring water quality in subterranean rivers. Sustainability, 8(6), 526.CrossRefGoogle Scholar
  12. 12.
    Jindal, R., & Sharma, C. (2011). Studies on water quality of Sutlej river around ludhiana with reference to physicochemical parameters. Environmental Monitoring and Assessment, 174(1–4), 417–425.CrossRefGoogle Scholar
  13. 13.
    Kamenar, E., Zelenika, S., Blažević, D., Maćešić, S., Gregov, G., Marković, K., et al. (2016). Harvesting of river flow energy for wireless sensor network technology. Microsystem Technologies, 22(7), 1557–1574.CrossRefGoogle Scholar
  14. 14.
    Khalfallah, Z., Fajjariy, I., Aitsaadiz, N., Langar, R., & Pujolle, G. (2013). A new WSN deployment algorithm for water pollution monitoring in amazon rainforest rivers. In 2013 IEEE global communications conference (GLOBECOM) (pp. 267–273). IEEE.Google Scholar
  15. 15.
    King, P., Venkatesan, R., & Li, C. (2010). Modeling a shallow water acoustic communication channel using environmental data for seafloor sensor networks. Wireless Communications and Mobile Computing, 10(11), 1521–1532. doi:10.1002/wcm.851.CrossRefGoogle Scholar
  16. 16.
    Lee, E. J., Yoo, G. Y., Jeong, Y., Kim, K. U., Park, J. H., & Oh, N. H. (2015). Comparison of UV–Vis and fdom sensors for in situ monitoring of stream doc concentrations. Biogeosciences, 12(10), 3109–3118.CrossRefGoogle Scholar
  17. 17.
    Lee, H. H., Hong, S. T., Shin, G. W., & Kim, H. I. (2012). Propagation analysis of wireless mesh network for real-time monitoring around the four major rivers in South Korea. In 2012 International symposium on communications and information technologies (ISCIT) (pp. 428–433). IEEE.Google Scholar
  18. 18.
    Lee, K. H., Yu, C. H., Choi, J. W., & Seo, Y. B. (2008). Toa based sensor localization in underwater wireless sensor networks. In SICE annual conference, 2008 (pp. 1357–1361). IEEE.Google Scholar
  19. 19.
    Luque-Nieto, M. A., Moreno-Roldán, J. M., Poncela, J., & Otero, P. (2016). Optimal fair scheduling in S-TDMA sensor networks for monitoring river plumes. Journal of Sensors. doi:10.1155/2016/8671516.
  20. 20.
    O’Connor, E., Smeaton, A. F., & O’Connor, N. E. (2011). A multi-modal event detection system for river and coastal marine monitoring applications. In OCEANS 2011 IEEE-Spain (pp. 1–10). IEEE.Google Scholar
  21. 21.
    O’Connor, E., Smeaton, A. F., O’Connor, N. E., & Regan, F. (2012). A neural network approach to smarter sensor networks for water quality monitoring. Sensors, 12(4), 4605–4632.CrossRefGoogle Scholar
  22. 22.
    Pellerin, B. A., Stauffer, B. A., Young, D. A., Sullivan, D. J., Bricker, S. B., Walbridge, M. R., et al. (2016). Emerging tools for continuous nutrient monitoring networks: Sensors advancing science and water resources protection. JAWRA Journal of the American Water Resources Association, 52(4), 993–1008.CrossRefGoogle Scholar
  23. 23.
    Shaban, M., Urban, B., Saadi, A. E., & Faisal, M. (2010). Detection and mapping of water pollution variation in the Nile Delta using multivariate clustering and GIS techniques. Journal of Environmental Management, 91(8), 1785–1793. doi:10.1016/j.jenvman.2010.03.020.CrossRefGoogle Scholar
  24. 24.
    Skarbøvik, E., & Roseth, R. (2015). Use of sensor data for turbidity, ph and conductivity as an alternative to conventional water quality monitoring in four norwegian case studies. Acta Agriculturae Scandinavica, Section B—Soil and Plant Science, 65(1), 63–73.CrossRefGoogle Scholar
  25. 25.
    Thorp, W. H. (1967). Analytic description of the low-frequency attenuation coefficient. Acoustical Society of America Journal, 42, 270. doi:10.1121/1.1910566.CrossRefGoogle Scholar
  26. 26.
    Ueyama, J., Hughes, D., Man, K. L., Guan, S. U., Matthys, N., Horré, W., et al. (2010). Applying a multi-paradigm approach to implementing wireless sensor network based river monitoring. In 2010 First ACIS international symposium on cryptography and network security, data mining and knowledge discovery, e-commerce and its applications and embedded systems (CDEE) (pp. 187–191). IEEE.Google Scholar
  27. 27.
    Velásquez-Villada, C., & Donoso, Y. (2016). Delay/disruption tolerant network-based message forwarding for a river pollution monitoring wireless sensor network application. Sensors, 16(4), 436.CrossRefGoogle Scholar
  28. 28.
    Vieira, R. G., Da Cunha, A. M., & de Camargo, A. P. (2015). An energy management method of sensor nodes for environmental monitoring in amazonian basin. Wireless Networks, 21(3), 793–807.CrossRefGoogle Scholar
  29. 29.
    Wenz, G. M. (1962). Acoustic ambient noise in the ocean: Spectra and sources. The Journal of the Acoustical Society of America, 34(12), 1936–1956.CrossRefGoogle Scholar
  30. 30.
    Yadav, S., & Yadav, R. S. (2016). A review on energy efficient protocols in wireless sensor networks. Wireless Networks, 22(1), 335–350.MathSciNetCrossRefGoogle Scholar
  31. 31.
    Yang, J., Zhang, C., Li, X., Huang, Y., Fu, S., & Acevedo, M. F. (2010). Integration of wireless sensor networks in environmental monitoring cyber infrastructure. Wireless Networks, 16(4), 1091–1108. doi:10.1007/s11276-009-0190-1.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentThapar UniversityPatialaIndia

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