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Paper—Low-Cost Gas Leak Detection and Surveillance System for Single Family Homes Using Wit.ai,…
Low-Cost Gas Leak Detection and Surveillance
System for Single Family Homes Using Wit.ai,
Raspberry Pi and Arduino
https://doi.org/10.3991/ijim.v16i09.30177
Ximena Pérez-Palomino(), Karina Rosas-Paredes, José Esquicha-Tejada
Universidad Catolica de Santa Maria, Arequipa, Peru
xperezp@ucsm.edu.pe
Abstract—The current situation in the region of Arequipa (Peru) is an
increase in crime and insecurity; companies that provide private surveillance ser-
vices have increased the costs of equipment and services online. We propose is
to implement a low-cost gas leakage and surveillance system for single-family
houses, implemented with Raspberry Pi3, an Arduino board, SIM 900 module,
sensors, actuators, and peripherals. The system alerts by sending a text message
when an intruder enters the home or when there is a gas leak; it captures the
webcam image that is sent to the homeowner’s email. For voice command recog-
nition, Wit.ai and Firebase are used for communication between the system and
the mobile application. System functionality and usability tests were carried out,
allowing us to know user satisfaction.
Keywords—IoT, surveillance, Raspberry Pi, Arduino, SIM 900 module
1 Introduction
Currently, due to the COVID-19 health emergency, around 10,000 people are unem-
ployed in the city of Arequipa, Peru [1]. This is a determining factor for insecurity in
the city, according to the National Institute of Statistics and Informatics, In January
and March 2021 alone, there has been an increase in the number of crime reports in
Arequipa (5428 crime reports registered) [2]. Furthermore, it is considered that home
security is not only about alerting about the presence of an intruder, but also about creat-
ing a safe environment, as well as other risks that can affect home life, such as res [3].
Therefore, several projects or prototypes have been developed to solve this type of
problem, which can be implemented with minicomputers, boards, sensors, and actu-
ators that are cost-effective and easy to acquire, in order to provide greater safety for
hospitalized patients [4], domotic solutions [5], improve education [6] and the automa-
tion of things (IoT) like watering the garden [7], [8].
According to the proposed scenario, there are solutions such as alarm systems but
with high prices, systems that are difcult to use, with similar sounds, and do not
detect gas leakage. Therefore, a gas leak detection and monitoring system is proposed
using a Raspberry Pi 3 Model B minicomputer, an Arduino UNO board, a microphone,
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a webcam, sensors, and actuators to provide home security that is low cost and low
power consumption, as well as being easy to use and implement.
2 Current situation
In the region of Arequipa, Peru, there are several companies that offer alarm system
services, at high prices and with similar characteristics. Initially, a survey was carried
out to nd out the security needs of the population, using equation (1) provided by [9].
n
Zpq
dN Zpq
N
2
22
1()
(1)
The population size (N) of the region of Arequipa in 2017, is 1 382 730 and the per-
centage of people (0.46) who own a house is 46%, according to the National Institute
of Statistics and Informatics [10]. Therefore, we obtain:
n = (1 382 730*1.6452*0.46*0.54)/(0.12*(1 382 730−1) + 1.6452*0.46*0.54)
n = 67.2
A survey of 67 inhabitants living in the region Arequipa was then conducted to nd
out their perceived level of insecurity and how much they would be willing to pay for
a gas leak detection and surveillance system. (See Table 1).
Table 1. Survey of level of insecurity and payment for surveillance system
Question Percentage Description
Have you ever had your property stolen by
unauthorized income?
63% Were victims of robbery
37% Were not victims of robbery
How much are you willing to pay for a gas leak
detection and surveillance system?
55.8% Pay from $220 to $280
38.5% Pay from $280 to $830
5.7% Pay from $830 to $1390
The questions asked and the result in percentage can be observed. The rst question
allows us to determine the level of insecurity, where 63% were victims of robbery,
a high percentage, which represents the insecurity in the city. The second question
allows to determine how much a person can pay for a surveillance and gas leak detec-
tion system, 55.8% indicated that they would pay between 220 to 280 dollars. This
price will be the basis for developing the system and procuring the sensors, actuators,
and peripherals.
3 Analysis of tools used
Various tools and alternatives were evaluated to develop and implement the gas leak
detection and surveillance system with the best equipment and at a low price.
The main Raspberry Pi models were compared, without considering previous ver-
sions that are not available in the local market: Raspberry Pi 2 Model B, Raspberry Pi 3
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Model B, and Raspberry Pi 4 Model B. In Table 2, when comparing the models, Rasp-
berry Pi 3 Model B was chosen, due to its higher procedural speed, Bluetooth connec-
tion, and Wi-Fi when compared to the previous version, and it has a more accessible
price and lower electricity consumption when compared to the recent version. These
versions have 4 USB inputs to connect different equipment or peripherals.
The Raspberry Pi minicomputer requires an operating system, in this case, Raspbian
was used for being simple, stable, fast, and with an extensive development com-
munity [4]. Raspberry Pi can be programmed with Python and Java, the chosen pro-
gramming language is Python because there are more libraries, the greater contribution
from the community (forums) and it allows the use of GPIO pins to connect the digital
world with the physical world [11], [12].
Table 2. Raspberry Pi model types
Model Raspberry Pi 2 B Raspberry Pi 3 B Raspberry Pi 4 B
Price 40 Dollars 40 Dollars 70 Dollars
SOC Broadcom
BCM2836
Broadcom
BCM2837
Broadcom
BCM2711Bo
CPU Clock 700 MHz 1.2 GHz 1.5 GHz
RAM 1 GB 1 GB 1 GB
USB 444
Wi- No Yes Ye s
Consumption 820 mA 1400 mA 2.5 mA
It is required to use an Arduino board, to connect the components that do not work
at 3.3v in Raspberry Pi and to connect the GSM/GPRS SIM 900 module for send-
ing text messages. Models were compared: Arduino Nano, Arduino Mega, Arduino
Leonardo and Arduino Uno. Table 3 shows the comparison between the Arduino mod-
els, in this case the Arduino Uno model was chosen, since compared to the Arduino
Nano the SIM900 GSM module/GPRS does not t on a shorter board. Compared to
the Nano, Mega and Leonardo models, the Arduino Uno board is more commercial,
and a large number of libraries has only been developed for this model. Arduino Uno is
economical, compatible with GSM/GPRS module and is regular size.
Table 3. Arduino model types
Model Arduino
Nano
Arduino Mega
2560
Arduino
Leonardo
Arduino
Uno
Microcontroller ATmega 328P AVR ATmega 2560
8 bits
AVR ATmega 32u4
8 bits
AVR ATmega 838
8 bits
Input/output
digital pins
14/14 54/54 20/20 14/14
Analog input/
output pins
8/0 16/0 12/0 6/0
Price 6 dollars 42 dollars 16 dollars 11 dollars
Compatible GSM/
GPRS Module
NO YES YES YES
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For speech recognition, the speech recognition software that can be used on Raspberry
Pi are Google Cloud Speech API, Pocket Sphinx and Wit.ai. Which were tested and
compared to determine which is the best for the proposal. In Table 4, the comparison
between speech recognition software is displayed. When comparing Wit.ai with Google
Cloud Speech API, it was observed that in the long term it was going to pay for the use
of the service, by using a large number of characters. Compared to Pocket Sphinx, when
this software was tested, speech recognition was low-quality, and the words being spo-
ken were not understood by the system. For this reason, Wit.ai was chosen for having
better speech recognition, works with several languages (English, Spanish, etc.), has an
interactive interface with the developer, is easy to use, and is free [13].
Table 4. Voice recognition software
Voice Recognition Software Google Cloud Speech API Pocket Sphinx Wit.ai
Internet connection Yes No Yes
Price 4 USD per million
characters
Free Free
Open Source No Yes No
Recognition Level High Medium HIGH
Languages 80 to 110 languages English/Spanish
through bookstores
50 languages:
English, Spanish,
Chinese, etc.
4 System architecture
The gas leak detection and surveillance system have a home automation system
structure, which is made up of sensors, actuators, and a control module.
There are several components necessary to develop the gas leak detection and sur-
veillance system. The following block diagram (See Figure 1) shows the connection of
the components and the architecture of the system
Fig. 1. System architecture
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In the diagram, you can see sensors, actuators, and equipment that connect to
Raspberry Pi. It has 40 pins, to which 2 PIR sensors (pins 11 and 12), a buzzer (pin 7),
a matrix keyboard (pins 29, 31, 33, 35, 32, 36, 38, and 40), and a screen were connected
LCD (pin 3 and 5), a DHT11 temperature and humidity sensor (pin 8) and an MQ4 gas
sensor (pin 13). Also, Raspberry Pi has 4 USB ports, in which it connects: keyboard
and mouse, an Arduino UNO board, a microphone with a Jack to USB port adapter, and
a USB camera. Through a Wi-Fi connection, it communicates with Firebase, the cloud
database, and the communication link between the system and the mobile application,
with the Wit.ai voice recognition software and with the Mutt email client to sending
emails.
Raspberry Pi is responsible for processing data and performing programmed actions.
The Arduino Uno board is made up of 6 analog pins and 14 digital pins. Servomotors
are connected to pins 10 and 9, and the relay module (focus) is connected to pin 11.
The SIM 900 GSM/GPRS module connects on top of the Arduino board and uses the
Tx and Rx pins (pins 8 and 7) for communication and sending text messages. The con-
nection between Arduino Uno and Raspberry Pi is through the USB port, through serial
communication.
5 System development
In order for the system to be interactive with the user, the entry of options through
a control panel is considered, which consists of a matrix keyboard, in which the fol-
lowing options can be entered: option A: activate full alarm (surveillance and detec-
tion gas leak); option B, deactivate the complete alarm; option C, activate only gas
leak detection; option D, disable gas leak detection; option *, shows temperature and
humidity when the systems are not active, and option #, to activate the voice recogni-
tion system.
The surveillance system works like the alarm systems on the market, to give it
greater value, other features were added. The system works when an intruder enters
the property, the alarm emits a sound, sends an alert text message to the owner, turns
on a spotlight from 6:00 pm to 4:00 am (depending on the time), takes a picture, saves
it in a Raspberry Pi folder with the date and time, sends it to the owner’s email, and
issues a message advising that the doors will be closed as a prevention method. So that
many messages or emails are not sent, after 30 seconds have passed after detecting a
presence, the system opens the lock, and if it detects a presence again, it performs the
previous procedure (See Figure 2).
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Fig. 2. Surveillance system
The gas leak detection system works when the gas sensor detects the presence of
methane gas, then it sounds an alarm (buzzer), sends an alert text message, and displays
an alert message on the screen. If the temperature is higher than 35 degrees, it shows the
temperature as an on-screen alert message. In order not to send multiple text messages,
it waits for 15 seconds, when the time passes and the system detects a gas leak again, it
performs the procedure described above.
To activate or deactivate the gas leak detection and surveillance system by the key-
pad, the user is prompted to enter a password. The user has three attempts to enter the
correct password, otherwise, the system hangs for 20 seconds.
To use the voice recognition system, the user can enter the options # via the matrix
keypad. The commands that can be spoken are: “activate alarm”, “deactivate alarm”,
“activate gas”, “deactivate gas”, “tell me temperature”, “tell me humidity”, “turn on
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light” and “turn off light”. For the enable or disable options, the user is prompted to
say the keyword to enter the requested option. The user has three attempts to enter the
code word, if a valid word is not spoken, the system returns to the main menu. To enter
the other commands the system must be disabled, if you say “tell me temperature” or
“tell me humidity” the system displays the temperature and humidity in that room of
the house where the sensor is located, in this case in the kitchen. When you say “turn
off light” or “turn on light”, the light bulb will emit light or turn off, as the case may
be. For the speech recognition system, the speech recognition software Wit.ai was used,
which has the “Speech to Text” service [13]. When a command is spoken, it is recorded
and sent to the Wit.ai servers, where the audio is converted to text, the text is returned
to the system and compared to which the command entered. Speech recognition is a
type of articial intelligence, which it establishes communication between man and an
intelligent device by means of human language [14].
The system has a mobile application (See Figure 3) that allows the user to man-
age and control the system from anywhere in the world with an Internet connection.
Through the mobile application you can change the password used to enter the control
panel (activate or deactivate), change the keyword (voice recognition system), and the
email to which the image is sent. In the application you can view the status of the alarm
(activated or deactivated), you can activate or deactivate any of the systems and you
can observe the temperature and humidity of the home in real-time. Communication
between the system and the mobile application is done through Firebase. Firebase is a
platform that enables a cloud database and other services to develop web and mobile
applications [15]. In the interim of using the Firebase database, when the user makes
any changes to data, it is reected both in the system and in the mobile application.
Fig. 3. “Cautela” mobile application [16]
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6 Results
To test the gas leak detection and surveillance system, a one-story house model was
implemented with the main door and 5 rooms. Sensors, actuators, and equipment nec-
essary for the operation were installed in this model. (See Figure 4).
Fig. 4. Prototype gas leak detection and surveillance system
The proposed system has a cost of 268 dollars. In Table 5, prices for the equip-
ment, sensors, actuators, cables used, and the development of the mobile application
are presented.
Table 5. Gas detection and surveillance system costs
Equipment Price ($)
Raspberry Pi 3 Model B 42
Arduino UNO 10
Module Shield GSM/GPRS SIM 900 14
Peripherals (keyboard, LCD screen, buzzer, microphone, camera) 40
Actuators (Led, Spotlight, Micro servo 9g) 11
Sensors (PIR, Dht11 temperature, MQ4 gas) 13
Cables 7
Mobile app 131
Total 268
Functionality and usability tests were carried out in different scenarios, which are
divided into tests of the surveillance system, the gas leak detection system, and the
voice recognition system.
Table 6 shows the tests carried out on the surveillance system, the tests carried out in
the morning vary with respect to those at night, in relation to shipping times. At night
when the light bulb turns on, it increases the time it takes to send the text message,
causing the other options to delay as well. For the system to detect the presence of an
intruder again, an average of 53 seconds must pass according to the tests carried out.
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Table 6. Time surveillance system test
Test Detect
Presence
Turn on
the Light
Time to Send a
Text Message
(Second)
Time to send
an Email
(Second)
Close the
Door
Mode
Home
P1 10:40 am Yes No 9 secs. 20 secs. 14 secs. Full
P2 11:20 am Yes No 9 secs. 20 secs. 17 secs. Empty
P3 7:16 pm Ye s Yes 17 secs. 19 secs. 21 secs. Empty
P4 7:27 pm Ye s Yes 16 secs. 17 secs. 20 secs. Empty
P5 8:07 pm Ye s Yes 16 secs. 19 secs. 20 secs. Empty
Table 7 shows the tests performed on the gas leak detection system, the time it takes
to detect gas depends on the time the sensor is turned on, this type of sensor needs
50 seconds to heat up. In the rst test made to the sensor it took longer, because the
system had just been turned on. In the other tests the system is stable, therefore gas
detection is faster.
Table 7. Tests performed on the gas leak detection system
Tests Detect Gas Leak Display Message
on LCD Screen
Time to Send a
Text Message Mode Home
P6 Yes 50 secs. 70 secs. Empty
P7 Yes 4 secs. 9 secs. Full
P8 Yes 5 secs. 10 secs. Full
P9 Yes 4 secs. 9 secs. Full
7 Conclusions and future work
The proposed gas leak detection and surveillance system is a good alternative to
provide greater security to the home, easy to implement, has an intuitive mobile appli-
cation, has a voice recognition system, and has better features than other surveillance
systems that exist on the market as detecting a gas leak or alerting the owner by text
message. Despite its size, the Raspberry Pi minicomputer has great processing power
which allowed the development of this project, being necessary to integrate the Wit.ai
voice recognition system, since it works with ambient noise and identies the voice
of a person without much effort. To store data, the Firebase cloud database was used,
which allowed the system to be manageable and controllable from a mobile phone with
an Internet connection. Finally, by using SIM 900 GSM/GPRS module, it was possible
to send alerts through text messages, without depending on an Internet connection.
The tests were carried out on a single-family home model and showed that the system
works in different scenarios. The system is modular and scalable, that is, it allows
adding additional sensors, actuators, and peripherals to provide greater characteristics,
taking into account electricity consumption so as not to overload the Raspberry Pi or
the Arduino board.
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As work future in this time of COVID-19, a digital thermometer could be used that
would work with the MLX90614 sensor. This sensor would measure people’s tempera-
ture as they enter the home, as an automatic prevention method.
8 Acknowledgment
To Universidad Catolica de Santa Maria, for providing the necessary equipment and
the space provided to develop the project
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10 Authors
Ximena Pérez-Palomino is a Systems Engineer from the Universidad Catolica de
Santa María, UCSM (2016). She is currently a Professor at UCSM in the Institute of
Informatic and Tecnical Analist in Bantotal. Researcher in Data Business Inteligence
and Information Technologies. Email: xperezp@ucsm.edu.pe
Karina Rosas-Paredes is a Systems Engineer from the Universidad Catolica de
Santa Maria. Master in Information Systems and Higher Education from the Universidad
Catolica de Santa Maria. Doctor in Systems Engineering from the Universidad Nacional
Federico Villarreal. She is CCNA CISCO certied. Coordinator of the Engineering
Area of CICA-UCSM. Researcher in Information Technologies. Director of Innovation
and Development Vice Rectorate of Research Universidad Catolica de Santa Maria,
Peru. Email: kparedes@ucsm.edu.pe
José David Esquicha-Tejada is a Systems Engineer from the Universidad Catolica
de Santa Maria, UCSM (2008). He holds a Second Specialty Professional Degree in
Systems Auditing and Information Security at UCSM (2019). Master in Strategic Tele-
communications Management at the Miguel de Cervantes European University (2013).
He is a candidate for a Doctor of Environmental Sciences and Renewable Energies at
the Universidad Nacional de San Agustín de Arequipa. He is currently an Assistant
Professor at UCSM in the Faculty of Physical and Formal Sciences and Engineering.
His research interests include the Internet of Things (IoT) and educational technology.
Email: jesquicha@ucsm.edu.pe
Article submitted 2022-02-12. Resubmitted 2022-03-07. Final acceptance 2022-03-08. Final version
published as submitted by the authors.
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