Remote Monitoring and Control System of a Water Distribution
Network using LoRaWAN Technology
RICARDO YAURI1, MARTIN GONZALES2, VANESSA GAMERO3
1Facultad de Ingeniería, Universidad Tecnológica del Perú, Lima, PERÚ
1,2Facultad de Ingeniería Eléctrica y Electrónica, Universidad Nacional Mayor de San Marcos Lima,
PERÚ
3Departamento de Engenharia de Sistemas Eletrônicos, Universidade de São Paulo, São Paulo,
BRAZIL
Abstract: - The problems related to the proper management and control in the distribution of potable water
affect environmental sustainability generated by leaks and breaks in the infrastructure, causing leaks and loss of
water. According to reports from the National Superintendence of Sanitation Services of Peru, more than 50%
of complaints about the water service are related to billing problems and water leaks. It is for this reason that
technologies such as the Internet of Things technology contribute to generating solutions for the automatic
acquisition of data in residences and houses. That is why this paper aims to use long-range and low-power
wireless communication systems to improve the service-oriented to the control of the water distribution
network, monitoring of vandalism, and detection of anomalous events, reducing response time and economic
losses. The paper's development methodology considers the implementation of a water controller node with
flow control sensors and solenoid valves and a gateway with Lora communication. In addition, a solenoid valve
control circuit and a remote visualization and control system are implemented. The results indicate that the
implemented nodes allow adequate monitoring and control in real-time of the water flow, contributing to the
adequate management of its consumption and supporting the detection of anomalous events using a Web
application.
Key-Words: - Low Power Wide Area Network, Internet of Things, LoRaWAN, Arduino, potable water
Received: October 17, 2022. Revised: January 19, 2023. Accepted: February 21, 2023. Published: March 28, 2023.
1 Introduction
There are many obstacles and inconveniences to
face the problems related to the efficient
management of potable water distribution (WDNP),
[1], [2]. Analyzing the history of events related to
the management of potable water in Peru, it is
possible to obtain an x-ray of the obstacles faced
today by the sector of water and sanitation services.
During the last years, the amount of potable water
that was stolen through clandestine connections in
Lima and Callao in 2016 was able to supply 3,000
families in 12 months, making it very difficult to
detect water connections that are outside the law
because they are underground, [3].
To measure the environmental sustainability of
the services provided by the water companies, the
indicator of "non-billed water (ANF)" is used,
which is not billed because of losses due to leaks
and broken pipes, in addition to the existence of
manual meters that generate errors. According to the
Benchmarking report of the National
Superintendency of Sanitation Services of Peru
(SUNASS), in 2018 the company SEDAPAL
registered a value of 27.8% in this indicator being
the reduction of the level of the ANF an objective of
the National Sanitation Plan, [4].
According to the reports reported by SUNASS in
2020, a total of 15,572 users of the potable water
and sewerage service throughout Peru were served
by the National Superintendency of Sanitation
Services (Sunass), through its channels of remote
care, during the 100 days of the state of emergency
due to COVID-19, [4]. In this period, 47% of the
attention are due to consultations due to billing
problems, while 31% was due to operational
problems due to lack of water, sewerage, and
flooding due to pipe breaks, [5], [6].
Regarding the management processes of potable
water resources, solutions have been developed
based on the prediction of the minimum night flow
for leak detection using Internet of Things (IoT)
technologies and artificial intelligence algorithms
(IA) for anomaly detection, [7], [8], [9], [10]. This
type of study contributes to preventive leak
detection processes in smart cities, integrating
systems based on Geographic Information Systems
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(GIS) and predictive analysis, [1]. Other studies
investigate the optimization of energy consumption
that potable water meters need, through the
deployment of drones for data collection, [10], and
the use of communication technology based on low
energy consumption wide area networks for the
transmission of data to Web services, [11], [12]. On
the other hand, systematic review papers describe
how the Internet of Things and machine learning
(ML) technologies have the ability to improve the
processes of acquisition, processing, and
transmission of data in real-time from the most
critical areas of a company water distribution
network, [13], [14].
From what has been described above, it can be
inferred that there is a problem in the efficiency of
water management, where engineering, legal,
economic, environmental, and social aspects are
involved. Having identified this problem, the
research seeks to contribute to a solution from the
engineering aspect and answer the following
question: How does the development of a low-cost
electronic system based on LoRaWAN technology
allow the monitoring and control of the flow of
potable water? For this reason, this article describes
the criteria for the implementation, design, and
construction of the system and tests the electronic
system with Low Power Wide Area Network
(LPWAN) technology to evaluate its performance.
The objective of this paper is to develop a system
that integrates an electronic circuit with LoRaWAN
wireless network technology, which is used to
manage water resources in a potable water
distribution network. In addition, it performs flow
reading processes, water flow control, and graphic
analysis for decision-making based on historical
data. This paper is organized into five sections as
described below. In section 2 we present a brief
review of related works. Section 3 shows the most
important concepts related to the technologies. In
section 4 the proposed system is described and in
section 5 the results are mentioned. Finally, in
section 6 the conclusions are presented.
2 Related Works
Efficient management in the distribution of potable
water has been studied in many research papers.
Thus, this section shows some of the studies found
related to aspects such as the Internet of Things,
artificial intelligence, and communication protocols
for low energy consumption hardware devices.
In [15], the authors propose an architecture based
on machine learning for the monitoring and control
of a water distribution system based on dynamic
operating conditions. This solution uses smart
meters to generate data in real-time, through
efficient software architectures.
In [2], the authors proposed a model based on
comprehensive monitoring (SC) of water
distribution networks with detection devices. This
paper describes how monitoring relies on smart
metering technologies and wireless sensors in
battery-powered nodes, limiting high sampling
rates. As a result, CS techniques can reduce process
execution times by 50%, achieving significant
energy savings.
According to [16], most drinking water losses
occur during transportation, so IoT-based systems
contribute to monitoring the status of drinking water
distribution pipes. In addition, the water demand
prediction process can be performed with deep
learning techniques and traditional methodologies
for time series such as autoregressive integrated
moving average (ARIMA).
On the other hand, in [7], the authors describe a
project to improve water supply and respond
preemptively to drought and water loss by reducing
pipe leaks and caring for aging pipes. To achieve
this, data is collected by sensors connected to the
Internet of Things devices using Multi-Layer
Perceptron (MLP) and Long Short-Term Memory
algorithms. In another direction, power optimization
is critical when using low-power IoT devices, which
is described in [17], where the authors describe the
use of a wireless communication network between
air vehicles and sensor nodes. Its communication is
optimized by minimizing the energy consumption of
the drone to obtain optimal data collection
trajectories.
According to [18], the management of the
drinking water resource is a great challenge that
generates control initiatives at a global level as well
as for sustainable development. In this context,
Smart Cities solutions contribute to the rational
consumption of water. This research proposes a
system to monitor and identify leaks in WDNP
through data inference techniques and Deep
Learning. Similarly, in [19], the authors describe
how the Internet of Things generates solutions in
these areas, there being a factor related to citizen
participation to support policies for sustainable and
efficient use of aquatic resources. In addition, it is
described that it is necessary to carry out a study of
water consumption before, during, and after the
period of confinement due to the COVID pandemic,
being important to promote the design of
educational activities and promote sustainable
behaviors based on the analysis of the data
collected.
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3 Wireless Networks for the Internet
of Things
The Internet of Things is an interconnection of
various IoT devices with the Internet infrastructure
using networks and communication protocols, [20]
[21], [22]. Today there is a wide range of networks
to connect devices and some of the most important
are described below.
3.1 Bluetooth Low Energy
Bluetooth technology is also very well-known
because it is used in many devices such as phones,
hearing aids, or cameras. When used for IoT, the
BLE (Bluetooth Low Energy) version is considered,
which is a specification aimed mainly at small-scale
IoT applications, such as portable devices, that
require the sending of small data with minimal
power consumption, [23], [24]. BLE provides data
transfer rates of just under 1 Mbps, and operates in
the unlicensed 2.4GHz band, which is ideal for use
indoors and over short distances, and with an
unlimited number of nodes, unlike traditional
Bluetooth.
3.2 Narrowband IoT
It is a technology promoted by the 3GPP (3rd
Generation Partnership Project), through mobile
operators and large manufacturers such as Huawei,
Ericsson, or Nokia to respond to the need for IoT
communication, [25]. NB-IoT uses the cellular
communication bands and has been designed to
operate in the LTE band using the spacing between
LTE channels, the guard bands, to make the most of
the communications spectrum, [26].
3.3 LoraWAN Networks
LoRaWAN is a wireless technology for low-power
wide-area networks. The name, LoRa, is a reference
to the long-range data links that allow this
technology long-range communications reaching up
to five kilometers in urban areas and up to 15
kilometers or more in rural areas, with line-of-sight,
[27], [28]. A key feature of LoRa-based solutions is
the low power requirements, allowing the creation
of battery-powered devices that can last up to 10
years, [29]. The specifications of this technology are
summarized in Table 1.
In a LoRaWAN network, the nodes are not
associated with a single specific gateway but can be
received by multiple gateways, [30]. Each gateway
will forward the received packet from the end node
to a network server via a backhaul (either cellular,
Ethernet, satellite, or Wi-Fi) (Fig. 1). The network
server is in charge of the intelligence and
complexity of the system, managing the network
and filtering redundant received packets,
implementing security controls, [31].
3.4 Applications and Web Services
Web applications allow IoT devices to store the data
they generate without having to use space on
physical servers. Being a distributed structure and
not dependent on a single organization, it provides
great redundancy and effective security systems for
businesses, facilitating the adoption of the IoT, [32].
Table 1. LoraWAN Features, [33].
Characteristics
Parameters
Standard
LoRaWAN
Frequency band
not licensed: 433/868/915 MHz
Bandwidth
125KHz/500KHz
Transmission speed max.
250bps - 50kbps
coverage range
≤ 15 km
Penetration
high penetration
power consumption
very low consumption
Fig. 1: LoRaWAN architecture, [31]
IoT cloud servers make it easy to communicate
with sensor nodes, manage them, and integrate them
with applications. If the different types of hardware,
connectivity, and sensors are taken into account, a
tool that allows to make changes, escalate processes
and respond to incidents in a centralized way
becomes essential, [34].
4 Proposed System
In this research, it is necessary to use flow
measurement sensors and solenoid valves to control
the passage and blockage of a certain water circuit.
In addition, the data obtained by the sensors are
managed by the nodes which integrate a
microcontroller to carry out the transmission of data
to the Internet using LoRaWAN.
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4.1 Selection of Technologies
In the case of data transmission technology,
LPWAN solutions are used, which provide an
alternative that covers a wide range of coverage,
low power consumption, and low cost, being chosen
based on a comparative study with other solutions (
Table 2).
Table 2. Features of wireless technologies
Feature
Coverage
Consumption
Quality
wide
Very low
WiFi
Low
Low
BLE
Low
Very low
ZigBee
low
Low
NB-IoT
wide
Very low
SigFox
wide
Very low
LoRaWAN
wide
Very low
There is a wide range of microcontroller models
on the market according to each use case. In this
case, the study was carried out among those
available in the local market and that are tolerant to
5 volts because it is a voltage compatible with a
greater number of sensors and actuators that were
used in this investigation, selecting the Arduino
UNO card. In addition, to provide transmission
capacity to the Arduino card, one of the most
outstanding boards in the field of LoRa technology
called Dragino (Fig. 2) is selected, which allows us
to achieve extremely long transmission ranges at
low speeds. of transmission.
The system uses flow sensors which are used to
measure different fluids (water, fuel, oil) and
different volumes with greater or lesser precision.
According to a study carried out on the available
sensors, it was considered to choose the one that has
a greater pressure capacity and greater size of
connection threads, selecting the YF-S201 sensor (
Table 3). All sensors use a magnet located in the
turbine, which generates a positive pulse each time
it passes the Hall effect sensor. In this way, you can
obtain the revolutions per minute generated by the
propeller and then calculate the water flow.
Fig. 2: Dragino Lora Module, [35]
Table 3. Flow sensor comparison
YF-S401
YF-S201
FS300A
Connection
1/4"
1/2"
3/4"
Flow
0.3 a 6
L/Min
1 a 30
L/Min
1 a 60
L/Min
Pressure
(Máx)
0.8 MPa
1.75 MPa
1.2 MPa
Voltage
DC 5~18
V
DC 5~18 V
DC 5~18
V
Temperature
≤ 80°C
≤ 80°C
≤ 80°C
In the case of IoT platforms, there is currently a
high availability of solutions that offer data storage
and visualization features for an end-to-end IoT
solution. The Ubidots account platform allows the
analysis and processing of data and the
programming of events, data analysis, and automatic
execution of actions can be carried out. This
platform is also compatible with various devices
such as Arduino, Raspberry Pi, ESP, Particle, etc. It
is for these reasons that it is chosen for the
implementation of the system.
4.2 Sensors Nodes
Two kinds of nodes were implemented: (i) the end
user node, which was located at the residence of
each water service customer to measure flow and
consumption, and (ii) the administrative node whose
purpose is to control the flow of water, allowing the
passage of water, blocking and metering.
Each end user node has a YF-S201 flowmeter,
which internally has a rotor that generates pulses
sent to the microcontroller housed on the Arduino
Uno board (Fig. 3). Through a program written in C
language, the calculation of the flow and water
consumption is performed. Subsequently, these data
are sent to the Dragino LoRa Shield module for
transmission to a Gateway Lora device. The
administrative node is like the one described above,
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but it has a solenoid valve to remotely control the
flow of water over certain sections of the network.
In this case, communication through LoRa
technology is bidirectional and a 12 Volt source is
required to power the solenoid valve, in addition to
the 6 Volt battery that powers the rest of the
circuitry (Fig. 4).
4.3 Flow Sensor Reading
The internal vanes of the rotor of the YF-S201
sensor are fully insulated to prevent water leaks and
externally to the camera it has a Hall effect sensor
allowing it to detect the magnetic field generated by
the magnet, the vanes, and the movement of the
rotor. As water circulates through the body of the
flow meter it turns the turbine inside it and the
magnet located in the turbine generates a positive
pulse each time it passes the Hall effect sensor. In
this way, you can know the revolutions per minute
generated by the propeller and then calculate the
water flow.
The flow sensor uses the Hall effect to measure
the flow according to the equation: f (Hz)=7.5 x Q
(L/min), where the variable f is the frequency of the
generated signal and Q is the amount of water per
minute, with a conversion factor of 7.5. An
algorithm is developed that reads the pulse signal of
the sensor in a time range "t" of 5 seconds. The flow
diagram (Fig. 5) shows how the volume of water is
calculated based on the flow multiplied by the
difference in the sampling time of the water flow.
Fig. 3: End user node diagram.
Fig. 4: Administrative node block diagram
START
READ sensor
Freq
calculate water flow
Q = Freq/Factor
calculate volume
V = Q*dt
During “t” seconds
Fig. 5: Flow rate calculation
4.4 Data Transmission
This process is performed in the two kinds of sensor
nodes described above. For this, the Arduino Uno
board is used together with the Dragino Lora card to
establish communication with the Gateway module
using the 915MHz frequency (free band intended
for IMS services throughout Latin America). To
control the transmission module, the “LoRa.h”
library and the transmission functions “LoRa.print”
and “LoRa.begin” are used for any type of data. The
implemented end user node (Fig. 6) sends the
information to the system gateway every 5 seconds.
Fig. 6: End user node
4.5 Solenoid Valve Control
The control of the 12V solenoid valve (model
VALV-SOL-1P2-12V) is carried out by the
administrative node which has an actuator
controlled by means of an output pin from the
Arduino hardware to activate or deactivate it. The
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circuitry is based on a 5V relay that will close the
circuit that feeds the solenoid valve from a 12V
source. As the relay has a minimum operating
current of 70mA, it will be necessary to use a
current amplifier, using a model 13002 transistor
(Fig. 7). The control process is started via pin 5 of
the Arduino to inject a digital signal to turn the
valve on. The 13002 transistor has a gain factor of
10, enough to guarantee a minimum operating
current of 70mA. This is calculated using the 550-
ohm resistor, ensuring a collector current of 78mA,
enough to turn on the relay and put the solenoid
valve into operation. This process is controlled by
the microcontroller that enables the solenoid valve
when it receives the indicated signal from the
system gateway.
The solenoid valve is activated when the
indicated signal is received from the system
Gateway via LoRa wireless communication. The
administrative node uses the “LoRa.receive()”
command to receive the data from the Gateway. If
the package has the message “LOW”, pin 5 will go
to the low state (0V) and if it has the message
“HIGH” it will go to the high state (5V) turning on
the solenoid valve. In this case, 6V is used for the
hardware modules and 12V for the solenoid valve
circuit.
Fig. 7: Solenoid Valve Drive Circuit
4.6 LoraWAN Gateway Module
The gateway has a WiFi communication interface
through the ESP8266 module which is controlled by
the Arduino card to transmit the information from
the sensor nodes to IoT servers in the cloud. Its
functions include receiving messages from the IoT
server and sending them to the nodes via LoRa to
execute an action on the solenoid valve (Fig. 8).
The Ubidots Web service is used to receive data,
on which the Gateway sends and receives
information using variables registered in the Web
platform through the Ubidots REST APIs
Fig. 8: Gateway LoRa
5 Results
The validation of the system built through a
structure that simulates a water distribution network
using 1/2" pipe circuits was conducted. The node is
installed near the potable water supply of each home
together with the YF-S20 flow sensor. On the other
hand, the Gateway module is located on the lower
floor of the test area, and due to the good
penetrability of the signal, wireless communication
does not represent problems (Fig. 9).
In the tests, the node is turned on and a constant
flow of water is generated, executing the data
transmission process towards the Ubidots Web
service. The data is observed using a graph whose
horizontal and vertical axes represent the time and
the flow, expressed in liters per minute (Fig. 10).
One way to visualize the data is through an online
dynamic table, specifying the value read and the
date the sample was taken, where the message
arrives at the server every 20 seconds. This is due to
the configuration of the gateway that is working in a
bidirectional mode in communication with the
server, being able to modify the sampling range to
have readings every minute.
The tests of the administrative node were carried
out utilizing a model placing the solenoid valve and
the flow sensor in the water distribution circuitry
(Fig. 11). This node was turned on and the
communication with the server for the remote
control of the valve was checked.
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Fig. 9: End User Node Deployment
Fig. 10: Flow variation in Ubidots
Fig. 11: Administrative Node Tests
Valve control tests were carried out from the
server, verifying the change in the flow rate
registered by the sensor (Fig. 12, Fig. 13), where
the Web interface has a control button to prevent the
flow of water from passing. (Fig. 13) shows the
presence of a registered flow rate, whose behavior
shows a higher pressure in the piping circuit and
then a reduction in this pressure.
Fig. 12: Valve in OFF state in Ubidots (No water
flow)
Fig. 13: Valve in ON state in Ubidots (With water
flow)
6 Discussions
The importance of the administrative node, in a
potable water distribution system, is based on the
contribution to monitoring the flow in sections by
the operator to manage the water supply. In this
way, it is possible to estimate the value of the
normal flow of a common day by analyzing the data
collected and by observing the graphs obtained on
the Web platform, allowing the detection of possible
leaks. In addition, attention to this type of event
would have a much shorter response time, reducing
economic losses due to non-revenue water.
In the case of the use of Lora technology, it
allowed having records of the flow and remote
control of the closing and opening of a section of the
network in case of any unwanted eventuality. This
technology has a long range with minimum power
consumption for transmissions between the node
and the implemented Gateway. Furthermore, the
YF-S201 flow sensor is ideal for water distribution
networks in residential applications.
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7 Conclusions
The end customer benefits from the system, by
having the ability to improve water management
through real-time monitoring and verification of
invoiced consumption, avoiding false readings or
human errors during the sampling process of these
variables. On the other hand, with the information
generated, it is also possible to identify, after the
flow decreases without reason for leakage, that it
could be vandalism due to clandestine water
connections and to identify the precise area of this
event, based on the location of the administrative
node. As a recommendation, the 12V normally open
(NO) solenoid valve should be used to reduce
energy consumption and the integration of batteries
that guarantee an operating autonomy of years.
In future works, the implementation of machine
learning techniques can be considered to perform
the detection of anomalous patterns or anomalies in
all the data collected from different sensor nodes.
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WSEAS TRANSACTIONS on SYSTEMS and CONTROL
DOI: 10.37394/23203.2023.18.8
Ricardo Yauri, Martin Gonzales, Vanessa Gamero
E-ISSN: 2224-2856
82
Volume 18, 2023