IoT-Enabled Cattle Health and Location Monitoring System
THISURA RAJAPAKSE
Department of Computer
Engineering,
General Sir John Kotelawala
Defence University,
SRI LANKA.
MWP MADURANGA
Department of Computer
Engineering,
General Sir John Kotelawala
Defence University,
SRI LANKA.
MB DISSANAYAKE
Department of Electrical &
Electronic Engineering,
Faculty of Engineering,
University of Peradeniya,
SRI LANKA.
Abstraction - In human-animal interactions, the capabilities of the IoT concept become a promising game
changer. The owners of animal farms can already utilize smart sensors to identify ways to keep an eye on the
health, whereabouts, behavior, and/or environment of their animals. On the other hand, despite this notion being
in use for years, there are still some concerns that need to be resolved. In this work, we contribute to the design
of a state-of-art wearable collar for cattle that can monitor the location and health conditions remotely in large-
scale farms. The proposed collar is designed on a microcontroller with GSM/GPRS communication modules.
Therefore, the wearable collar can communicate directly with the IoT server via 3G/4G or 5G mobile base stations
and can be used in robust environments. Further, power-saving techniques have been investigated to prolong the
lifetime of the wearable collar. Evaluation of the performances of the devices has been presented in this paper.
Keywords: - Internet of Things (IoT), Animal Health Monitoring, 5G, Smart Farming, Sensors
Received: April 9, 2022. Revised: October 29, 2022. Accepted: November 24, 2022. Published: December 31, 2022.
1. Introduction
Advances in Technology have altered the
agriculture sector to improve the service rendered to
the customers as well as the suppliers. Most
processors in this domain have become more
accessible, and time- and labor-efficient with the use
of sensors, equipment, gadgets, and information
technology. By implementing technology-ready
devices, farmers create new avenues and operate in a
productive and efficient atmosphere. Animal
husbandry will be more convenient and manageable
thanks to new technological instruments and
techniques. Additionally, daily management
decisions at the farm and animals can be efficiently
configured using modern technology tools. The
profitability and quality of the final output will be
directly impacted by humans.
Animal and milk production volumes are
influenced by a variety of variables, including the
animal’s genetic makeup, environment, exposure to
diseases, nutrition, climate, age, and season. The
stability of the economy and the safety of
the nation's food supply will be maintained by
keeping an eye on animal health and taking the
required action when necessary. Due to trade
restrictions, animal slaughter, and subsequent disease
eradication measures, animal disease outbreaks can
have a direct impact on the nation's economy.
Additionally, this may have an impact on world trade,
agricultural sector stability, and public health.
Animal health is typically monitored manually on
farms. The primary factor reducing daily production
in farms is the manual health monitoring system. The
disadvantages of a manual system are numerous.
There is a paucity of expertise regarding early disease
impact detection in animals. For instance, a late
diagnosis of a disease will result in expensive
medical treatment. In large farms, manual animal
data management is a time-consuming, labor-
intensive task. Additionally, in the manual system,
the decision-making process is frequently erroneous
and imprecise, and it is highly subjective. Some
systems are highly expensive, and some farmers find
it difficult to afford such systems. Furthermore, most
of these tech-based systems are designed for large
farms and it is practically challenging to adopt them
in small farms. Therefore, this research mainly
focused on creating a monitoring system that is
accurate and economical.
This research proposes an IoT-based Animal
Health Monitoring System, to address above
mentioned issues encountered when raising
livestock. This system was designed and created as
individual nodes, making it inexpensive for small-
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2022.1.8
Thisura Rajapakse, Mwp Maduranga, Mb Dissanayake
E-ISSN: 2945-0454
64
Volume 1, 2022
scale farmers to buy the exact quantity needed. The
farmer can manually install the device and attach it to
the monitoring web portal fairly easily by following
the instructions.
The idea of a base station is entirely dropped in this
design. Using GSM/GPRS technology, Node can
interface directly with IoT servers. Direct
communication means that there are no restrictions
on distance. If the system notices an abnormal animal
health condition, it will automatically send an alarm
to the farmer through email notification. Because of
this particular feature, the system is more suitable for
small farms where the individual animal monitor is
practical.
Also, in our proposed design, contrary to other
designs in the literature, we measure the body
temperature and heart rate of the animal. These two
measures alone, help to diagnose the presence of
infection in animals. Early diagnosis of potential
infections would assist the farmers in better
managing their herd and stopping the spreading of the
disease through isolation of the infected.
Also, a gas sensor is included in this system to
identify the air quality around the cattle. Maintaining
good oxygen levels in the cowshed will help to
increase productivity, while poor air quality
indication would again assist the farmers to take
preventive measures and improve the management of
their livestock. Furthermore, this system is energy-
efficient, cost-effective, and easy to use.
The paper is structured as follows, Section 1 gives
an introduction to the research problem, and section
2 presents the literature review highlighting the
related works. It is followed by the design and
implementation of experimental results, and future
works. The conclusion section concludes the paper.
2. Related Works
Different challenges are addressed by the
systems that are now in use. Different components,
characteristics, and technologies were used by these
systems. The below summarizes specific IoT systems
from the literature about the issue examined in this
literature.
Vannieuwenborg et al. contributed to Designing
and evaluating a smart cow monitoring system from
a techno-economic perspective, This study is about
developing an IoT-based precision dairy monitoring
technology to overcome various factors that affect
overall economic performance in the dairy industry
such as reproductive health problems, diseases, and
low detection rate of insemination movement. This
system consists of several components and
applications. Smart collars include a temperature
sensor, Ultra-Wide Band base location tag,
acceleration sensor, and barometer. Using a magnetic
induction collar is capable of wireless charging. For
data communication between the collar and base
station, this system used LoraWAN protocol and
technology. The base station will upload the
communicated data to the cloud. Because of
LoraWAN technology collar can do instance over-
the-air firmware updates. Smart ear tags use to collect
the temperature data and communicate with the collar
via MI. NFC function is included in this tag for setup,
paring, and scanning processes. Charging points are
installed on drinking points feeding points and
milking points to charge supercapacitors in the collar.
Management platforms will help farmers to keep
tracking their cows. If any threshold value is
exceeded, the farmer will get notified. Mobile
applications will help to locate specific cows on the
farm. Using NFC farmers can access cow journals
and data history. The cloud will work as the main
repository and analyze all data. This allows system
management via various types of interfaces
(management platform and mobile application)[1].
Faruq et.al. carried out a study for a health
management system for dairy cows, that is capable of
detecting and handling diseased cows. This system
consists of a monitoring and detecting system. For
application utilization purpose system combine the
Internet of Things (IoT) with Intelligent System
technology. The main purpose of the monitoring
system is to collect data from the sensors at each
node. Each node consists of Arduino Mini Pro,
ESP32, MAX30100 Heartrate sensor, and
MLX90614 Temperature sensor. Raspberry PI is
used in this system as a gateway. All the nodes send
data to the gateway before sending it to the server
(MQTT protocol). Raspberry PI will send the average
value of each data and each node to the server every
30-second time gap (HTTP protocol). An intelligent
system is a combination of collecting data, training
data, classification algorithms, and classification
prediction. Training data is defined as attributes for
symptoms of the disease (Table 1) and eight types of
disease have 23 attributes to be trained (Table 2).
Further, this system uses two data storing methods
such as MongoDB and stored as files. Frontend also
consists of web-based and application-based user
interfaces. To identify that the system is working
perfectly several experiments were carried out in this
study. The technical experiment is done to identify
that functionality of the system runs well. The
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DOI: 10.37394/232029.2022.1.8
Thisura Rajapakse, Mwp Maduranga, Mb Dissanayake
E-ISSN: 2945-0454
65
measuring experiment aims to compare the results of
the heart rate sensor and temperature sensor are
suited to real values. According to the results of
temperature sensor average values and real values
0.6-degree Celsius difference was recorded. They
assumed that sensor detects temperature properly.
The Heartrate sensor value difference compared to
the real value is 3.5 beats per minute. Intelligent
system experiment focuses on determining
symptoms based on early-defined data. Based on the
experiment result system accuracy is 90%. Using this
system breeders can identify the diseased cow in the
early stage and provide them with medical treatments
on time[2]Mhatre et. al. have researched to develop a
system to measure the milk production of the cattle.
This system reads temperature and humidity values,
heart rate values, and rumination values to predict the
milk yield of the cow in liters per day. DHT11
temperature and humidity sensor, Kg011 heart rate
sensor, and ADXL345 absolute motion sensor are
used in this system to read data. The sensor values are
read using Atmega328p and sent through serial
communication to NodeMCU then send these
collected data to the ThingSpeak channel. Universal
Asynchronous Receiver and Transmitter (UART) is
used in serial communication. Network connectivity
to NodeMCU is required as it consists of the
ESP8266 model. ThingSpeak is an online cloud
platform for IoT analysis. Here we can analyze and
visualize data with the help of a built-in MATLAB
execution system. These four data fields are plotted
in ThingSpeak in the form of Graphs and Histograms,
then using its MATLAB Analysis Tool system to
predict the milk yield. Then it will send to
ThingSpeak. Temperature and humidity data were
read from the sensor with ±2°C and 5% accuracy.
When compared to human cattle has very thick skin.
Because of that heart rate sensor used in this system
cannot measure the heart rate directly. An amplifier
with metal electrodes is used to get a clear picture.
The designed amplifier has a gain of 1000 which will
amplify weak signals received from electrodes in the
range between 1mV- 2mV.
If the predicted milk yield drops below 7 liters per
day. Then the system will notify the user through
email. In this system, this process will be scheduled
to occur once per hour[3].
Minnaert et. al. have done research that is mainly
focused on the energy storage solutions for the
wearables that are attached to the cow. The power
consumption of a wearable is an important obstacle.
This will reduce the lifetime of the device and
farmers need to regularly change its batteries. As a
solution, this paper suggests wirelessly charging the
device at the feeding point using inductively
coupling. Also, this paper mentioned what is the most
preferable energy buffer between rechargeable Li-ion
batteries and supercapacitors. In 63s of time period
supercapacitors stored 168 J. Rate of energy transfer
is not static due to the motions of the cow. At the
same amount of time Li-ion battery stored only 100
J. If the system requires high charging time and high
energy density it is better to use a hybrid system. This
combination will provide a high energy density of Li-
Ion batteries and a high-power rate of
supercapacitors[4].
Pratama carried out a study in Indonesia for a cattle
health monitoring system using the Internet of
Things. The system consists of three parts a Collar
device to gather data from cattle, a Base station for
local server management, and a Web application for
analyzing and managing the health of the cattle.
Collar capture temperature data, heart rate data, and
accelerometer data. Furthermore, it has a 5v solar cell
to recharge the battery. Raspberry PI use in this
system as a base station that will communicate with
collars and get data. Next, it will upload the data to
the cloud server. Collected data will analyze and
classified through the Machine Learning algorithm
into three sections such as normal, less normal, and
abnormal. Users will be able to monitor these
analyzed data through the web application and keep
track of the dairy cow’s health[5].
Wang et. al. did a study that describes an early
warning system based on cattle activity mainly
focusing on this system. The main data source used
for this system is activity detection and GPS
positioning data. For activity detection, they have
used an MPU6050 sensor module with six-axis
motion tracking and communication using the I2C
protocol. NEO-6M module is used to collect position
data. Using the ESP8266 Wi-Fi transmission module
the system communicates with the PC. If the system
detects any abnormal behavior in the cow built-in
alarm buzzer will give a sound reminder to the user.
Moreover, the STM32 single-chip microcomputer is
used as the core main control unit. The system detects
abnormal behaviors in two stages when the steps per
hour are less than 2160 and higher than 7200. The
system will update the data on the PC and trigger the
alarm buzzer if the cow shows abnormal
behaviors[6].
Brahim et. al. contributed to a project that is about
monitoring the behaviors of the dairy cow. In this
system, they have proposed an energy-efficient and
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DOI: 10.37394/232029.2022.1.8
Thisura Rajapakse, Mwp Maduranga, Mb Dissanayake
E-ISSN: 2945-0454
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reliable method to gather real-time behaviors of dairy
cows. Using an accelerometer sensor detects motion
activities with the time period. As a power
consumption method, this system uses three steps
data processing method and a sleep/wake-up method.
Components used in this system are Arduino Nano
v3.1, MPU-9250 (accelerometer), and nrf24L01
(wireless communication) module. In data
processing, three steps are data selection, execution
of the classification, and transmission of the result.
The main purpose of data selection is to minimize the
calculation process and reduce power consumption.
The main purpose of this study is to identify the
inclination of dairy cows’ backs. When an
accelerometer detects high inclination system detects
it as a transition otherwise the movement is in motion
or stationary [7].
P. Chens’ study is about developing a cow estrus
monitoring system using the Narrow Band IoT (NB-
IoT) communication method. The system monitors
the body temperature and amount of exercise of the
cow frequently. Data will be uploaded to the server
using NB-IoT communication and the user can
access the data at any time. The system monitors the
behaviors of the temperature and exercise amount
and determines whether the cow is in the estrus
period base on the sudden increase in the data values.
Image recognition technology is also used as an
auxiliary means of estrus detection. When designing,
components are selected to reach the lowest power
consumption in the system. STM32 microprocessor
is used in this system as a central processing unit. BC-
95 module used for NB-IoT communication.
ADXL345 accelerometer sensor is used for motion
detection and the DS18B20 temperature sensor to
gather temperature data[8].
3. Design and Implementation
From the implementation point of view, this
system can be divided into three subcategories. They
are Smart collar, Web applications, and IoT servers.
Fig.1 illustrates the complete system overview.
3.1 Smart collar
The main purpose of the Smart collar is to
capture information from cattle, such as heart rate,
body temperature, the air quality of the surrounding,
and the GPS coordinate of its location, then upload it
into the cloud server using an inbuilt IoT system.
Task-oriented different types of sensors and modules
are embedded into the smart collar to capture these
data. The Smart collar is mounted on the cow by
placing the collar strap around the animal just after
the first two legs where the heart is located in the
cattle body.
Microprocessor Board – It is based on the ESP32
microprocessor, a single chip that supports Wi-Fi,
and Bluetooth (BLE). Antenna and RF baluns, power
amplifiers, low-noise amplifiers, filters, and a power
management module are also utilized along with the
ESP32. The proposed circuitry uses the least amount
of space on the printed circuit board as a whole. This
circuit board uses TSMC 40nm low power 2.4MHz
dual-mode Wi-Fi and Bluetooth chips, which have
the best power and RF attributes and are secure,
dependable, as well as adaptable to a range of
applications [9].
Heart rate sensor – Magene H64 heart rate sensor
is used to get heart rate readings of the cattle. The
electrode sensors on this heart rate monitor are
capable of detecting the electrical activity of the
beating heart. Low energy communication is
provided by the application that supports Bluetooth
BLE protocol compatibility. This also extends the
lifetime of the battery. Furthermore, this device is
made from strong, abrasion-resistant materials that
won't tear, crack, or break. It is IP67 water resistance
certified. The user-replaceable CR2032 battery used
in the Magene H64 heart rate monitor lasts for an
average of 1000 hours [10].
Temperature Sensor – The MLX90614
temperature sensor is chosen to measure the body
temperature of the cattle. A non-contact infrared
thermometer is present in the MLX90614 for
measuring the temperature. The signal conditioning
ASIC and the IR-sensitive thermopile detector chip
are both incorporated in a single TO-39 container.
The MLX90614's low noise amplifier, 17-bit ADC,
and potent DSP unit work together to provide the
thermometer with significant accuracy and
resolution. The thermometer has a digital SMBus
output that is factory calibrated and offers full access
to the measured temperature across the whole
temperature range(s) with a resolution of 0.02°C. The
digital output can be set up to use pulse width
modulation by the user (PWM). The 10-bit PWM is
Fig.1 – System Overview
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DOI: 10.37394/232029.2022.1.8
Thisura Rajapakse, Mwp Maduranga, Mb Dissanayake
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typically set up with an output resolution of 0.14°C
to continually convey the measured temperature in
the range of -20 to 120°C [11].
Air Quality Sensor – MQ2 gas sensor is chosen
to measure the air quality. MQ2 gas sensor measures
the number of gases in the air, including LPG,
propane, methane, hydrogen, alcohol, smoke, and
carbon monoxide. A gas sensor of the type MQ2 is
a metal oxide semiconductor. A voltage divider
network included within the sensor is used to
determine the gas concentrations in the air. The
sensor requires 5V DC power to operate. It is
capable of detecting gases with concentrations
between 200 and 10,000 pp. The gas sensing
component is made primarily of ceramic with an
aluminum oxide base coated in tin dioxide and
surrounded by a stainless-steel mesh. The sensing
element is supported by six interconnecting leads
where two are used for heating while the other four
are employed to generate output signals [12].
SIM Module – SIM808 board provides cellular
GSM and GPRS data in addition to GPS technology
for satellite navigation. The board's ultra-low power
consumption during sleep mode supports
extraordinarily lengthy standby intervals present in
the application. Additionally, a LiPo battery-
compatible onboard battery charging circuit is
present. With 22 tracking and 66 acquisition
channels, the GPS receiver is highly sensitive. It also
supports assisted GPS (A-GPS) for indoor
localization. The board supports 3.3V and 5V
logical levels and is controlled by AT commands
sent through UART. It comes with a small GSM and
GPS antenna; however, it does not require a battery.
The board makes use of 2G GSM networks, neither
3G nor LTE [13].
Voltage Regulator – In the proposed design the
MLX90614 and MQ2 sensors operating voltage is
maintained at 5V whereas ESP32 doesn’t have 5V
output and consists of 3.3V output. To power up
these two sensors directly from a battery, a voltage
regulator is used, where an 8.4 V supply is stepped
down to 5V. In the process of buck converters, the
fixed dc input signal is converted into a different
lower-value dc signal [14], [15].
Transistor and Resistor – Due to the direct power
supply connection from the battery to MLX90614
and MQ2 sensors, a mechanism should be included
to turn off those two sensors when not in use. A
MOSFET transistor (IRFZ44N)[16] is used as a
switch to turn off the sensors, when not in use. The
resistor is used to pull up the signal provide from
ESP32 to the gate terminal.
Fig.2 shows the connection diagram between the
main processor and the other modules. As shown in
Fig.2 all the components in the proposed circuitry are
directly communicating with the main processor
through wired lines except for the heart rate sensor.
The selected heart rate sensor is capable of
connecting with the processor through
ANT+/Bluetooth 4.0 compatible devices. ESP32
module has an inbuilt Bluetooth system that supports
Bluetooth Low Energy protocol (Bluetooth 4.0) [17].
Therefore, the heart rate sensor and the
microprocessor are connected through Bluetooth for
data communication.
3.1.1 PCB design and device
The Circuit board for the smart collar is designed
using DXP Developer software. The schematic
diagram of the PCB design is shown in Fig.3, Fig.4,
and Fig.5. The printed PCB of the Smart collar is
shown in Fig.6.
Fig.3 – Schematic of Power Unit
Fig.4 – Schematic of Microprocessor
Fig.2 – Connection Diagram
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DOI: 10.37394/232029.2022.1.8
Thisura Rajapakse, Mwp Maduranga, Mb Dissanayake
E-ISSN: 2945-0454
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The housing of the circuitry boards of the Smart
collar is 3D printed using watertight filament. Fig.7
and Fig.8 show the 3D printed model (housing) and
Smart collar design.
3.1.2 Process of a smart collar
Initially, the operating cycle of the smart
collar starts with the creation of a Bluetooth Low
Energy (BLE) server by the ESP32 microcontroller
board to establish the connection with the Magane
H64 heart rate sensor. Once the server is created in
the ESP32, it will scan for the specific Universal
Unique Identifier (UUID) of the specific device.
Once the device is located, the connection is
established for communication.
If the device cannot be found, the server
continues to scan for 15 seconds in each period. If the
device is out of reach within this period, the ESP32
resets to begin another round of communication.
Once the connection is established the heart rate
sensor will not send data directly to the ESP32 until
enabling Notify function in the device. To enable
notify function, ESP32 needs to transmit enabling
code of 0100 to the heart rate monitoring device.
Once the notify function is enabled the heart rate
reading is transmitted to the ESP32 board. This
reading contains a set of information, specifically
heartbeat, battery level, position, and signal strength.
EPS32 is programmed to filter the data and to obtain
only the heart rate data. In general, within 20 seconds,
10 readings are received and the average is
considered as the. Once the average is calculated that
value is stored in a variable till the transmitted IoT
server. After reading the heart rate the ESP32
terminates the BLE server and disconnects from the
sensor. After another 10 seconds, the sensor moves to
sleep mode until it receives another call from the BLE
server.
After getting the heart rate reading ESP32
will move to get data from the MQ2 gas sensor and
MLX90614 temperature sensor. The power supply
for both of these sensors is controlled by using a
MOSFET transistor. To power, the sensors the
applied voltage at the Gate terminal will be
terminated. Temperature data will be read first. It will
give enough time (minimum 6 seconds) to heat the
aluminum-oxide-based ceramic, coated with Tin
dioxide (sensing element) to get the readings. The
temperature sensor will get ten readings with 1
second time intervals and then calculate the average
temperature. This will be stored in a variable as cattle
body temperature to transmit later. The gas sensor
also captures environmental parameters as the
temperature sensor and is saved in a variable for later
use. Both sensors will calculate the data within 20
second time duration. Once the calculation is done
supply voltage at the gate terminal in MOSFET to
turn off both sensors.
SIM808 module will move from power down mode
to active mode to locate cattle in real-time location
and carry out the data transmitting process. First
Fig.6 – Printed PCB
Fig.8 – Smart Collar
Fig.5 – Schematic of SIM808
Fig.7 – 3D Printed Housing
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2022.1.8
Thisura Rajapakse, Mwp Maduranga, Mb Dissanayake
E-ISSN: 2945-0454
69
using the built-in function GPS mode will be
activated. The microprocessor will read the power
supply status for the GPS function. If the GPS is not
powered up properly microprocessor will try to
power up it for 5 seconds repeatedly. If it fails, then
the entire function will be restarted. Once the power
is up SIM808 module will try to track the satellite to
retrieve longitude and latitude data. When location
data is read and stored, the GPS function will
terminate.
When all the necessary data collect and store in
variables SIM808 will start to build the network
connection through GPRS for data communication
between the IoT server channel. AT commands will
be used to identify the protocols, and network
provider, and establish the connection. The collected
data will be sent to the IoT server through a link
assigning specific variable data to specific fields.
After sending the data to the IoT server the network
communication will be terminated. Then the SIM808
module will move to a power-down state until it
receives a power-up command from the
microprocessor.
After completing the data transfer ESP32
microprocessor will move to Hibernation mode for 2
minutes. In this mode entire module functions will be
disabled such as ESP32 Core, ULP coprocessor, Wi-
Fi and Bluetooth, and Peripherals. A real-time clock
is the only function that is enabled. This is used to
wake up the microprocessor to begin a new cycle.
The total time for one complete cycle execution
including the sleep period is around 180 seconds.
3.2 IoT server
In this system, the ThingSpeak IoT server is used to
store the data received from Smart collar. Gather,
visualize, and analyze real-time data streams in the
cloud with the ThingSpeak IoT analytics platform
service. Using online services like Twilio and
Twitter, you can send alerts, send data to
ThingSpeakTM from your devices, and instantly
visualize live data. You can create and run MATLAB
code with ThingSpeak's MATLAB analytics to carry
out preprocessing, visualizations, and analysis.
Engineers and scientists can prototype and construct
Internet of Things devices with ThingSpeak without
setting up servers or creating web applications.
User needs to create a channel in ThingSpeak for
their device. This is one-to-one communication for
each device it is required to create a different channel.
Once the channel is created and synced up with the
device the data will be displayed on the application
once it is received. Fig.9 will show the interface of a
channel that feeds data from Smart collar.
As shown in Fig.9, once the channel is created user
will get a unique Channel ID and API Key for the
channel. Users can use those IDs and Keys to sync
with the device and also add new cattle to the system.
A single user can create any number of channels in
the ThingSpeak IoT server.
ThingSpeak provides storage space for its channels.
This space is up to 8000 data points for a channel.
Once it reaches the 8000 range the older data will be
removed from the system. Those data can no longer
be retrievable. Each field visualized the data points,
this data point containing the value, date, and time it
received. Threshold values can be added for the
fields. If a value exceeds or reduces from threshold
vale system can trigger a notification to the user
specifying the reason and relevant details.
3.3 Web Application
The web application is used to manage individual
cow profiles in the same place. The web application
is developed using the Laravel framework. Laravel is
an open-source PHP-based application framework.
Programmers utilize it voluntarily and it is fully
documented. It makes use of the Model-View-
Controller (MVC) design, which makes the code
clear, comprehensible, and organized and makes
website building incredibly simple. When a large
group of people collaborates on an application, this
Fig.9 – ThingSpeak Channel
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2022.1.8
Thisura Rajapakse, Mwp Maduranga, Mb Dissanayake
E-ISSN: 2945-0454
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style of design is also advantageous. Because Laravel
was created using Symfony's components, both web
frameworks share the functionalities.
Fig.10 shows the interface of the dashboard.
Initially, a new user account needed to create by the
user in the web application using a username, valid
email address, and password. Once the user creates
the account successfully, the user will redirect to the
dashboard of the web application.
The dashboard contains cattle details, farm location,
user account, and other functions. Once the user adds
a cattle to the application the data will be shown in
the application. The graphs can be expanded and
minimized whenever the user needs them. Using this
application users can manage one or more farms and
track data at once. Cattle will be grouped according
to the relevant farm. When the user clicks on the
cattle he/she wants to see stats the data will be
expanded and also the relevant cattle's real-time
location will be shown on the map.
When the user clicks on the Farm tab, it will direct to
the Farm page shown in the above Fig.11. In here
user can add a new farm to the system, or updating a
new farm in the system can be done. Users can add a
new farm by providing a name and marking the
geographical area on the map. Also, an existing farm
can be updated by changing its name or editing its
geographical area. When the user needs to delete a
farm, first the cattle that are assigned to that farm
need to be updated to another farm or removed from
the system. Then the user can delete the relevant
farm.
Fig.12 will show the adding new cattle to the system
page. Users can add cattle to the system by filling in
the required details. Also, the user can update the
details of an existing cattle or delete the cattle details
from the system. Before adding a new cattle user
needs to create a channel for the cattle in the
ThingSpeak IoT server. Once the channel is created
Channel ID and API key needs to provide to the
system to retrieve data from the IoT server. If cattle
are added to a new farm that is not in the system it is
required to add the farm first before adding cattle
details unless selecting the relevant farm that the
cattle is belong to is not in the list.
4. Experimental Results
When it comes to portable devices power
management is a main concern to increase the
lifetime of the device on one charge. Various power-
saving methods have been implemented in the Smart
collar to increase the lifetime. Starting from the
ESP32 microcontroller, the clock speed of the
microcontroller is reduced from 240MHz the
80MHz. Below Fig.13 describes the current
consumption at different clock speeds.
The yellow color line presents the current
consumption of the microcontroller at the clock
speed of 240MHz. The reason for the peek value at
starting point is that the microcontroller creates a
BLE server and searches for a heart rate monitor to
connect. This process requires a higher amount of
current.
Fig.10 – Application Dashboard
Fig.11 – Farm Tab
Fig.12 – Cattle Tab
Fig.13 – Current Consumption with Clock Speed
Fig.12 – Current Consumption with Sleep Mode
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2022.1.8
Thisura Rajapakse, Mwp Maduranga, Mb Dissanayake
E-ISSN: 2945-0454
71
The orange color line presents the current
consumption of the microcontroller at the clock
speed of 160MHz. There is a 12mA to 15mA current
difference when compared to 240MHz clock speed.
The blue color line presents the current consumption
of the microcontroller at the clock speed of 80MHz.
This consumes a low amount of current when
compared to the other two clock speeds. When the
microcontroller enters Hibernation mode (sleep
mode) the current consumption is the same at all three
modes because only RTC is active.
Esp32 supports five different types of power modes.
They are Active mode, Modem sleep mode, Light
sleep mode, Deep-sleep mode, and Hibernation
mode. Each mode consumes a different level of
current consumption. Below Fig.14 describes the
current consumption at each mode.
The blue color line presents the microprocessor in an
active state without processing any commands. All
the chip's functionalities are still operational in this
mode. Since everything is always running in active
mode, including the Wi-Fi module, CPU core, and
Bluetooth module, more current is needed to run the
chip. Additionally, it has been noted that using Wi-Fi
and Bluetooth together might occasionally cause
excessive power spikes to emerge.
The orange color line presents the microprocessor in
Light sleep mode. The majority of the RAM, the
CPU, and the digital peripherals are clock-gated
while in light sleep mode. The CPU is put into light
sleep mode by turning off its clock pulse, although
the RTC and ULP-coprocessor are still in use. Wi-Fi,
Bluetooth, and Radio are completely active in this
mode. As a result, less energy is used than in active
mode.
The yellow color line presents the microprocessor in
Deep sleep mode. All digital peripherals and the
CPUs are turned off while the microprocessor is in
deep sleep mode. The only components of the chip
still in use are RTC Peripherals, ULP Coprocessor,
RTC Controller, and RTC fast and slow memory.
The purple color line presents the microprocessor in
Hibernation mode. In contrast to deep sleep mode,
the chip turns off its own 8 MHz oscillator and ULP-
coprocessor in hibernation mode. Since the RTC
recovery memory is also off, we are unable to
preserve any data in hibernation mode. Only one
RTC timer (on the slow clock) and a few RTC GPIOs
are active because everything else is inactive. They
are responsible for waking up the microprocessor
from Hibernation mode.
This Fig.15 will show the current and voltage
variation in the microprocessor for one complete
cycle. The blue color graph describes the current
variation and the red color graph describes the
voltage variation. This graph is sketched by setting
the clock speed at 80MHz. The microprocessor is
capable of running much lower clock speeds such as
60MHz, and 40MHz. When setting the frequency
lower than 80MHz communication between the
microprocessor and SIM808 interrupts. 80MHz is the
lowest working frequency for the SIM808 module.
Fig.16 shows the entire system current and voltage
consumption for one cycle. The blue color graph
presents the current variation and the red color graph
presents the voltage variation. When it comes to
current there are higher peak values. In the beginning,
the system consumes more power to power all units
including creating a BLE server and connecting to the
heart rate sensor. The peek value between the 20s –
40s is to calculate the readings from the temperature
sensor and gas sensor. Gas sensors consume more
power between 150mA to 165mA to heat the sensing
element in the sensor to get readings. The peek value
Fig.15 – Microprocessor Current & Voltage Variation
Fig.15 – Smart Collar Placed on Cattle Body
Fig.14 – Microprocessor Current & Voltage Variation in
Sleep Modes
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2022.1.8
Thisura Rajapakse, Mwp Maduranga, Mb Dissanayake
E-ISSN: 2945-0454
72
in the 50s is used by SIM808 to get GPS coordinates.
In this processing power the GPS function, search,
and lock satellite carry out. The 70s - 90s peek current
values are also used by SIM808 to connect to the
network for data communication. According to the
calculations, the size of one data set is 10kB. SIM808
supports 2G network coverage and it has a speed of
0.1Mbps. The time taken to upload one data set to the
IoT server is less than 1 second (10kB/100kbps = 0.8
seconds).
To determine the lifetime for the Smart collar below
parameters measured.
Current consumption of Smart collar per hour =
0.117A
The capacity of the used battery = 20Ah
The lifetime of the device = (20 / 0.117) / 24
= 7.12 Days
This device can fun approximately 7 days from one
charge. The following figures are taken during the
testing phase.
5. Discussion
The authors intended to add a Smart collar to the
Smart collar communication mechanism in further
works to construct a sensor network inside the farms.
The proposed solution is low-cost and could use in
robust environments. The designed sensor node will
be expanded to monitor other animals, even though
the first deployment was created primarily to support
the cow farms. Depending on the demand, the web
programs will be upgraded to monitor two or more
animal species at once.
6. Conclusion
This paper presents an IoT-based wearable device to
monitor the health conditions and locational of cattle
in large-scale farms. The proposed design is based on
GSM/GPRS communication and can directly
communicate the data to the IoT cloud server with
mobile base stations without having an intermediate
device. Therefore, the proposed wearable collar can
be used in robust environments. This system has a
design that considers reducing power consumption.
Thus, a few power-saving strategies were applied,
and evaluated their performance.
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International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2022.1.8
Thisura Rajapakse, Mwp Maduranga, Mb Dissanayake
E-ISSN: 2945-0454
73
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Contribution of individual authors to
the creation of a scientific article
KATD Rajapakse: Design and implementation.
MWP Maduranga and MB Dissanayake:
conceptualized, reviewed, and edited the paper.
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2022.1.8
Thisura Rajapakse, Mwp Maduranga, Mb Dissanayake
E-ISSN: 2945-0454
74
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