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

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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.

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Abbreviations

\(d_{a}\) :

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

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

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Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar University, Patiala, Punjab, 147004, India

    Himanshu Jindal, Sharad Saxena & Singara Singh Kasana

Authors
  1. Himanshu Jindal
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  2. Sharad Saxena
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  3. Singara Singh Kasana
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Correspondence to Sharad Saxena.

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Jindal, H., Saxena, S. & Kasana, S.S. A sustainable multi-parametric sensors network topology for river water quality monitoring. Wireless Netw 24, 3241–3265 (2018). https://doi.org/10.1007/s11276-017-1532-z

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