IoT Based Water Quality Monitoring System for Rural Areas

Full text

IoT Based Water Quality Monitoring System for
Rural Areas
Ali Hadi Abdulwahid
Automation and Control Engineering Techniques
Southern Technical University
Basrah, Iraq
email: dr.alhajjiali@stu.edu.iq
Abstract - To ensure that safety is guaranteed, it is
essential to implement monitoring in real-time for the
quality of potable water. This work is about the use of
Internet of Things (IoT) technology to develop an
affordable system to control water quality in real-time.
Several sensors are integrated into the system to
measure various chemical and physical water
properties, such as conductivity, pH, turbidity, and
temperature. The core controller, which can also be the
microprocessor, manages the processing of data
captured by the sensor. The visualization of data can be
accomplished on cloud computing via the Inter net.
Keywords – water quality monitoring, Internet of Things,
Arduino, cloud computing.
I. INTRODUCTION
Although potable water is an essential resource for
humankind, the real-time management of portable water
utilities is faced with several challenges as a result of the
depletion of water sources, outdated infrastructure, and
population increase. Therefore, there is an urgent need to
develop new systems for water quality control.
Water quality evaluation in conventional strategies
consists of an analysis of water samples in the laboratory
collected by hand at several sites. Although these strategies
are effort-intensive, time-consuming, expensive, and do not
provide real-time information concerning water quality that
could be essential in making decisions of safeguarding
public health, they are informative about the chemical,
biological, and physical water properties [1-5]. There is a
need to develop new approaches taking advantage of
Internet-based tools to control water quality due to the
decline of efficiency of current strategies.
There has been a substantial development of Internet-
based technologies to manage and control water utilities.
However, the number of monitoring sensors to be installed
and calibrated across a wide area is large; therefore, such
technologies are quite expensive. Also, the employment
algorithm for these technologies has to be suitable for a
specific area.
Based on the above problems, the present work has
proposed an IoT-based system that is cost-effective for
managing potable water quality in real-time. For this
reason, Arduino is the main controller used in this system
along with a specialized IoT module to ensure the sensor
information from the key controller can be accessed or
visualized on mobile phones through Wi-Fi or via cloud
computing. The structure of the remaining work is as
follows: Section II consists of relation with IoT, section III
presents the general flow diagram of the proposed
approach, section IV discusses the empirical work and
outcomes, and section V outlines the concluding remarks
concerning the undertaken work.
II. THE RELATION WITH IOT
Daily life has been completely revolutionized, whereby
IoT has enabled the establishment of connections between
various things, intelligent technologies, and sensors [6].
IoT, an extension of the Internet [7], makes innovative
services more productive as well as efficient and allows
instant access to information concerning physical objects.
The main technologies related to IoT include cloud
computing, wireless sensor networks, and worldwide
computing.
Cloud computing is a processing unit characterized by
affordability and extensive scale. Various researches have
provided an in-depth discussion concerning cloud
computing features [8-11]. IoT is applied in different fields
such as monitoring of water quality and environment and
home automation. The prerequisites of IoT used to monitor
water quality include separate monitoring algorithms¸ an
expansive distribution network, and a large number of
sensors [12-14]. The present research proposes a system for
monitoring the quality of water, whereby the information
collected by the sensors can be acquired through cloud
computing.
III. METHODOLOGY
The current part addresses the theoretical aspects
associated with IoT-based control of water quality in real-
time. In particular, this part is divided into two sections,
including those with the individual system elements and
those that are respectively concerned with the general block
diagram linked to the proposed system.
Fig. 1. General block diagram
(Arduino
Uno)
Turbidit y
Sensor
pH
Sensor
Conductivity
Sensor
Temperature
Sensor
ESP8266
WiFi
Smart
Phone
Monitor
9th International Conference on Renewable Energy Research and Applications
Glasgow, UK, Sep. 27-30, 2020
ICRERA 2020
279
978-1-7281-7369-6/20/$31.00 ©2020 IEEE
Authorized licensed use limited to: Auckland University of Technology. Downloaded on December 21,2020 at 05:27:24 UTC from IEEE Xplore. Restrictions apply.

Figure 1 presents the general blocked diagram linked to the
proposed system. It is evident from the diagram that the
system is linked to the main controller, Arduino Uno, and
consists of several sensors measuring various properties of
water, including conductivity, turbidity, pH, and
temperature. It is responsible for processing the collected
sensor information and making the data accessible online
through cloud computing.
IV. SYSTEM ARCHITECTURE LAYERS
Designing the layered structure of a global network is a
challenge that includes heterogeneous networks that
require the effectiveness and identification of a similar
group function and network elements as a class. The
layered framework of the recommended IoT water
monitoring system consists of four layers, including
Network layer (Ethernet shield and router), Perception
layer (Devices and sensor in the physical world),
Application layer (User interface), and the Service
Management layer (Manage the collected information).
Each layer is discussed as:
A.
Perception layer
This layer consists of closely integrated sensors that are
merged with the physical environment. The physical
condition or sensor object chemistry is monitored by the
sensor modules in real-time [15]. The main role of this
layer is to collect data concerning the quality of water and
the devices. Also, it is responsible for collecting and cost-
effectively distinguishing the captured data, transforming
data, and transporting the aware data to the Network layer
[16]. Distinct sensors are used in such:
Turbidity Sensor: The reduction of the water
transparency caused by the presence of an unresolved
suspended substance can be termed as turbidity. The origin
of the water molecules can be either organic or minerals.
Turbidity is used to measure the light scattering effect;
however, it cannot be used as a direct measure of suspended
water particles. The role of these sensors is to determine the
quantity of light diffused by the suspended solids. The level
of water turbidity increases as the number of suspended
solids rises in the water. Turbidity is measured in NTU
(Nephelometric Turbidity Units) [17].
Fig. 2. Turbidity Sensor (SEN0189)
pH Sensor: pH sensor (SKU: SEN0161) is responsible
for detecting the value of pH in water. It is presented in
Figure 3. pH is a Latin word and an acronym for “the power
of hydrogen” or “potential hydrogenii.” Hydrogen-ion
concentration in the water-based solutions is responsible for
showing the alkalinity and acidity in the solution.
pH uses a logarithmic scale that ranges from 0 to 14,
with 7 being the neutral point. Besides, the values above the
neutral point exhibit an alkaline or basic solution and the
values below the neutral point present an acidic solution.
Consequently, the normal pH range is from 6-8.5.
Fig. 3. pH Sensor (SKU: SEN0161)
TDS sensor: TDS (Total Dissolved Solids) is defined as
the number of soluble solution milligrams in one liter of
water. Generally, the high TDS values define the more
soluble solids in the water and signify that the water is less
clear. Therefore, these values can be used to determine the
level of water cleanliness. The TDS pen measures the TDS
values; however, it cannot transfer information to the
control system for real-time monitoring to analyze the water
quality. The kit of the TDS sensor is precise and can
transmit data to the control system. It is compatible with
easy to use, Arduino, as well as plug and play. Ppm (parts
per million)) is the unit used to measure TDS [17].
Fig. 4. TDS Sensor (SEN0244)
Temperature Sensor: This sensor (DS18B20), is used
to measure water temperature—temperature increase results
in the rise of the ionization rate. pH is dependent on the
temperature; therefore, when the temperature increases, the
ionization rate rises, and vice versa. Temperature is key in
the measurement of water quality.
Temperature is regarded as an essential component for
determining other applications for the analysis of the water
quality. We opted to use DS18B20 to determine the water
temperature whose range was from -55-125°C. This is
illustrated in Figure 5.
Fig. 5. Temperature Sensor (DS18B20)
9th International Conference on Renewable Energy Research and Applications
Glasgow, UK, Sep. 27-30, 2020
ICRERA 2020
280
Authorized licensed use limited to: Auckland University of Technology. Downloaded on December 21,2020 at 05:27:24 UTC from IEEE Xplore. Restrictions apply.

B.
Network layer
The data is transmitted to the device and many layers
for sharing the data resources, data packets routing, and
system topology. The network layer classifies and
aggregates collected data and sends it to the servers [15].
C.
Service management layer
This is the brain of IoT, which stores the information
from the bottom layers and processes it based on the
analysis. The service management layer is considered as an
information management center. Some of the critical
elements are processing and analyzing technologies and
expert systems.
D.
Application layer
This layer represents the smart applications. The
primary function of the application layer is to offer
intelligent services to the users such as virtualization and
smart notification depending on intelligence and netter
findings of information executed by the service layer.
ThingSpeak offers more applications such as analytics
(analysis and virtualization) and action (ThingTweet, React
and ThingHTTP)
SYSTEM IMPLEMENTATION
The main idea behind IoT involves integrating the
device with a virtual world of the Internet, whereby they
interact with each other by sensing and monitoring object
and their surroundings.
An open-source IoT application, ThingSpeak, is
regarded as an API used for data storage and retrieving
information from things via the local area network or
through the HTTP protocol. ThingSpeak provides a solution
for analyzing data on-request from third-party sources,
allows the creation of location tracking, sensor logging
applications, and network of things that can update changes
Our implementation methods include software and
hardware implementation. The hardware includes sensors,
microcontrollers, and an Ethernet shield. Data collected by
sensors is processed by a microcontroller, which sends the
information to ThingSpeak via an Ethernet shield. Arduino
is an open-source programmable circuit board that can be
linked into various maker space projects, both complex and
simple, used as a controller. On the other hand, software
implementation involves data collection from the hardware,
which is uploaded to the online database through the
ThingSpeak. Generally, Arduino IDE software was used to
develop the hardware part by establishing new channels in
the ThingSpeak and writing the code and uploading it to the
Arduino board.
Arduino Uno and sensors are the two components that
are used by this system to work around the data. Arduino
Uno works as a microcontroller while the sensors collect
data for turbidity, PH, temperature, and conductivity. For
Arduino Uno to be controlled by mobile phone and have
access to the sensor’s terminals, it is connected to ESP8266
Wi-Fi to allow data capture.
Collected data is uploaded to the Internet by an IOT
module via cloud computing and can be accessed from
mobile devices via Wi-Fi. Figure 2 presents the ESP8266
hardware circuit diagram exhibiting the connection between
the IoT module and Arduino Uno.
IoT Module enables the transfer of quality water
parameters from the sensor to the gateway. Sampled data
from gateway and IoT module is sent to the server in the
form of User Datagram (UDP) packets and stored in a
database. The data is accessible from different locations
through assigned different IP addresses.
V. EXPERIMENTS AND RESULTS
The proposed system consists of four sensors, including
turbidity, temperature, TDS, and pH, wh ich are connected
to Arduino, as illustrated in Figure 1. The parameters
measured by these sensors when placed in water include
Turbidity, TDS, pH, and Temperatures.
Consequently, the microprocessor accesses and
processes the information from all the sensors and
eventually sends it to the ThingSpeak API via the network.
A.
Water Measurement Temperature using a
temperature sensor
Figure 6 exhibits the sensor measuring the temperature
of water in the range from -50°C to 125°C.
Fig. 6. Water Temperature Measurement
B.
Measurement of water pH value using the pH sensor
Figure 7 indicates how the sensor measures the water
pH value ranging from 0 to 14. Water is considered normal,
acidic, or basic, depending on the results shown on the
sensor. If the pH value is less than 7, water is grouped as
acidic, if it is above 7, it is regarded as basic, and if the
value itself is 7, water is grouped as good or normal. Acidic
water is again classified as low acidic (pH 3-6), high acidic
(pH 0-2), and low basic (pH 8-10). Also, water is classified
in the same way as high basic (11-14) and low basic (8-10).
Fig. 7. Measurement of pH value
9th International Conference on Renewable Energy Research and Applications
Glasgow, UK, Sep. 27-30, 2020
ICRERA 2020
281
Authorized licensed use limited to: Auckland University of Technology. Downloaded on December 21,2020 at 05:27:24 UTC from IEEE Xplore. Restrictions apply.

C.
Measurement of water TDS value using TDS
sensor
Any salts, minerals, or metal dissolved in water is
referred to as the “dissolved solids”. Total dissolved solids
(TDS) has some small quantities of organic matter that are
dissolved in the water and inorganic salts such as sulphate,
chloride, sodium, potassium, magnesium, and calcium. The
range of TDS is from 0 to above 1200 (World Health
Organization). The unit of measuring TDS is ppm. The
range of TDS below 300ppm is considered as excellent for
drinking water, good (300-400ppm), fair (600-900ppm),
poor (900-1200ppm), and unacceptable (above 1200). The
TDS range is illustrated in Figure 8.
Fig. 8. TDS level Measurement
D.
Measurement of Water Turbidity using Turbidity
sensor
Its turbidity defines the clarity of the water. Water
quality varies if it is mixed with any sand particles, mud,
sand, or silt. Based on the norms of water quality, the range
of normal water is from 0-5 NTU (Nephelometric Turbidity
Units), although it can still surpass 5 NTU. Water is
classified as mud mixed or turbid water if it goes beyond 6
NTU up to 3000 NTU. Figure 9 illustrates the water
turbidity measurement.
Fig. 9. Measurement of Turbidity of water
VI. CONCLUSION
The current work has recommended a new IoT-based
system to control water quality in real-time. The proposed
system consists of components such as Arduino Uno base
unit, sensors to capture the data concerning water quality,
and IoT (ESP8266 Wi-Fi). The benefits of using this system
include the ability to process data, high efficiency,
affordability, visualization in cloud computing and mobile
devices via Wi-Fi and transmission. Consequently, the
system can be applied in fields such as monitoring the
environments with easy access to data regardless of the
location.
Any work that will be done in the future will emphasize
more on the development of a monitoring system
strengthened by a wireless sensor network (WSN) and
controlled through an online interface to allow internet-
based monitoring from rivers. Also, it will focus on the data
capture concerning the biological properties of water. This
will allow data associated with water quality to be collected
and sent to the appropriate authorities.
REFERENCES
[1] R. Deekshath, P. Dharanya, K. Kabadia, G. Dinakaran, and S.
Shanthini, “IoT Based Environmental Monitoring System using Arduino
UNO and Thingspeak,” IJSTE-International Journal of Science
Technology & Engineering, vol. 4, no. 9, 2018.
[2] B. Esakki, S. Ganesan, S. Mathiyazhagan, K. Ramasubramanian, B.
Gnanasekaran, B. Son, S. W. Park, and J. S. Choi, “Design of
Amphibious Vehicle for Unmanned Mission in Water Quality Monitoring
Using Internet of Things,” Sensors, vol. 18, no. 10, pp. 3318, 2018.
[3] N. R. Moparthi, C. Muk esh, and P. V. Sagar, "Water quality
monitoring system using IoT." pp. 1-5, 2018.
[4] U. Shafi, R. Mumtaz, H. Anwar, A. M. Qamar, and H. Khurshid,
"Surface water pollution detection using internet of things." pp. 92-96,
2018.
[5] M. S. U. Chowdury, T. B. Emran, S. Ghosh, A. Pathak, M. M. Alam,
N. Absar, K. Andersson, and M. S. Hossain, “IoT Based Real-time River
Water Quality Monitoring System,” Procedia Computer Science, vol. 155,
pp. 161-168, 2019.
[6] S. Gokulanathan, P. Maniva sagam, N. Prabu, and T. Venkatesh, “A
GSM Based Water Quality Monitoring System using Arduino,” 2019.
[7] M. Parameswari, and M. B. Moses, “Efficient analysis of water quality
measurement reporting system using IOT based system in WSN,” Cluster
Computing, vol. 22, no. 5, pp. 12193-12201, 2019.
[8] K. M. S, and M. S. S. R, "IoT and WSN Based Water Quality
Monitoring System." pp. 205-210 , 2019..
[9] T. M. Truong, C. H. Phan, H. Van Tran, L. N. Duong, L. Van Nguyen,
and T. T. Ha, "To Develop a Water Quality Monitoring System for
Aquaculture Areas Based on Agent Model." pp. 47-58, 2019.
[10] Z. Shareef, and S. Reddy, "Design and Development of IoT-Based
Framework for India n Aquaculture," Intelligent Communication, Control
and Devices, pp. 195-201: Springer, 2020.
[11] P. Liu, J. Wang, A. K. Sangaiah, Y. Xie, and X. Yin, “Analysis and
Prediction of Water Quality Using LSTM Deep Neural Networks in IoT
Environment,” Sustainability, vol. 11, no. 7, pp. 2058, 2019.
[12] K. Komathy, "Ballast Water Quality Compliance Monitoring Using
IoT," Information and Communication Technology for Sustainable
Development, pp. 443-451: Springer, 2020.
[13] B. Etikasari, S. Kautsar, H. Riskiawan, and D. Setyohadi, "Wireless
sensor network development in unmanned aerial vehicle (ua v) for water
quality monitoring system." p. 012061, 2019.
[14] N. Dalwadi, and M. Padole, "The Internet of Things Based Water
Quality Monitoring a nd Control," Smart Systems and IoT: Innovations in
Computing, pp. 409-417: Springer, 2020.
[15]S. Benedict, N. Gowtham, D. Giri, N. Sreelakshmi and K. Elissa,
"Real-Time Water Quality Analysis Framework using Monitoring and
Prediction Mechanisms", Conference on Information and Communication
Technology (CICT), pp. 1-6, 2018.
[16]R. C. Jisha, G. Vignesh and D. D eekshit, "IOT based Water Level
Monitoring and Implementation on both Agriculture and Domestic
Areas", 2019 2nd International Conference on Intelligent Computing
Instrumentation and Control Technologies (ICICICT), vol. 1, 2019.
[17]B. NGOM, M. Robert SEYE, M. DIALLO et al., "A Hybrid
Measurement Kit for Real-time Air Quality Monitoring Across Senegal
Cities", 2018 1st International Conference on Smart Cities and
Communities (SCCIC), pp. 1-6, 2018.
9th International Conference on Renewable Energy Research and Applications
Glasgow, UK, Sep. 27-30, 2020
ICRERA 2020
282
Authorized licensed use limited to: Auckland University of Technology. Downloaded on December 21,2020 at 05:27:24 UTC from IEEE Xplore. Restrictions apply.