Development of a Sustainable Internet of Things-Based System for Monitoring Cattle Health and Location with Web and Mobile Application Feedback

The implementation of IoT-based cow health monitoring systems was suggested in studies [3-5] to monitor cattle health. The provision of information to users about individual animals was enabled through Internet of Things connectivity and android applications. The main advantage of these systems is that the day-to-day monitoring of animal conditions and the early detection of diseases by farmers were easily enabled. However, the implementation of these systems was found to be very expensive. The detection of all kinds of diseases and the tracking of animal locations were not possible due to the lack of a GPS system.

For the tracking of cattle grazing and the prevention of rustling, the use of GPS and GSM systems was proposed in studies [6-8]. Through the attachment of collars to cattle, the sending of SMS with the exact collar location to farmers was enabled, allowing the tracking of cattle location. The generation of location coordinates by the GPS module and their delivery to farmers as SMS along with warning messages through the GSM module were enabled by the collar. The main advantage of this system is that it can be used in large areas due to the use of GPS and GSM modules. The implementation of this system was found to be very affordable. However, the obtaining of livestock coordinates and the transmission of SMS messages were found to face delays due to the low quality of service supplied by local networks in some areas.

According to the authors of the study [9], the real-time monitoring of livestock whereabouts was enabled through an IoT-based system integrating limited-power wide-area technologies, the cloud, and virtualization services. The capture of location and activity data on a periodic basis and its transmission to the cloud system through LoRaWAN gateways for storage, processing and management were enabled, allowing easy access by users. The major advantage of this system is that little power is consumed and LoRa and LoRaWAN were used for long-range communication links.

In studies [10, 11], the use of sensors and GPS modules for the collection of cattle information and the tracking of their whereabouts was proposed. In the first study, the monitoring of pulse rate, body temperature, heart rate and respiratory rate was enabled through sensors. The transmission of signals from sensors to a server application for processing was allowed. Additionally, the tracking of a cow's whereabouts at any time was enabled through the use of a chip inserted into the cow connecting with GPS. The transmission of signals from sensors on cattle to the cloud through routers and then to farmers/veterinarians as SMS was enabled. The main advantage of this device is that information about cattle can be obtained from anywhere as all data is stored in the cloud, and it has a 3-layer ANN enabling accurate decision making. It was highly effective as a sophisticated cloud-based system detecting cattle health abnormalities and location to avoid rustling.

In study [12], a LoRa sensor network for monitoring pastured livestock location and activity was described. The sensor in this system comprises a low-cost CPU compatible with Arduino, a generic MPU-9250 motion sensor with 3-axis accelerometer, 3-axis magnetometer and 3-axis gyroscope, a generic GPS receiver and an RFM95W generic LoRa radio. The open-source Arduino IDE software was used to modify the central processing unit sampling frequency. The open-source Arduino IDE software was utilized in the programming of the sensors utilized in wearable sensors. The provision of specific information about each animal's behaviour throughout the day and the places where most time was spent on the farm by sensors was enabled. The advantages of using this system are that animal location can determine in real-time based on GPS data; it provides an inexpensive method to deploy flexible and efficient precision technologies for pastured livestock; it has obvious appeal as a low-risk solution and real-time data transmission about animals is provided. A disadvantage is that the system shape requires consideration due to the potential for animal hair to be caught in sensors, and size requires consideration due to the potential for damage from animals rubbing their heads against fences.

In the study [13], the use of a GPS system to monitor animal whereabouts was proposed. The study also included the use of temperature sensors and accelerometers to track animal temperature and movements, as well as the use of IoT to monitor general animal health. The transfer of data from the microcontroller to a PC through ZigBee transceivers to assess the severity of detected animal health issues was enabled. The accessing of information from any device on which the software was installed by farmers has been allowed. The advantages of this system are the use of IoT technology and GPS to track animal location and prevent theft. The disadvantages are that there are not enough sensors to provide accurate information about animal health, and the proposed system lacks water resistance, risking damage upon contact with water.

In the study [14], the aim was to monitor the heart rates and temperatures of unwell and distressed animals, as well as animal patients recovering from surgery or early therapy. The communication with the system using a web server and clients was enabled. The saving of data from heart rate and body temperature sensors on MySQL servers and display on a web browser upon receipt was enabled by the system. The warning or notification of veterinarians via messaging including a line messaging application when an animal's heart rate or temperature exceeds or falls below the normal range was enabled. The system design, extensive content and user-friendliness of the application provide acquired information from all users with a mean value. The total user satisfaction rating for the sick animal monitoring system was found to be 4.38, which is a respectable score. Performance of the device was found to be satisfactory and consistent with veterinarian best practices.

In the study [15], an electronic system for the wireless recording and transmission of micro-ruminant heart rates and skin temperatures was proposed. The monitoring system comprises a mobile base unit handled remotely and a stationary base unit placed on the animal under investigation. Communication between the mobile and stationary base units through an Xbee Series 2 radio frequency transmitter was enabled. The easy recording and transmission of physiological data by the electronic monitoring system were enabled. The proposed device tracked physiological reactions, especially heart rate monitoring. However, limited energy autonomy and inability to be used where mobile communication was not possible e.g., enclosed cages due to technical limitations were disadvantages. The system's distinguishing feature was the combination of data capture in memory and mobile connectivity. The use of a cellular connection to obtain physiological parameters decreased stress from the measurement method, improving data quality and enabling real-time data collection as animals were not tethered.

A method for farmers to detect and contrast their cattle's current health metrics to regular healthy reference parameters through real-time input value detection using data from pulse, humidity and temperature sensors sending them to an MSP430 microprocessor for control was proposed in study [16]. The transmission of an alarm message by the GSM module in the event of an emergency and the display of information on an LCD were enabled. The initial heart rate, temperature and humidity of the animals in this study were 77, 32.42 and 69, respectively. If these values change, an alarm was transmitted to the farmer's cell phone through GSM for further action. The theft of the wearable device could pose a severe security risk. A digital theft detecting system could be installed on the device or hardware to inform the user if a damaged belt was detached from cattle.

The investigation of cattle grazing patterns and behaviors in the grazing field studied the use of a water-resistant GPS monitoring collar device (TR20) for cows which tracks the activity of each cow during grazing through a networked system [17]. Following resolution of ambiguity, location fixes were accurate for almost one day with 95% accuracy for 8 minutes. The precise prediction of animal behavior was achieved, with active categorization of grazing location fixes evenly dispersed, while inactive fixes near water or in popular resting areas were clustered. The study focused on the deployment of a GPS tracking collar capturing data more efficiently through a cloud-based network. The movement of cattle in the feeding area creates a binary linear graph which can be used to determine components reacting to cattle. Animals exhibit a wide range of behaviors depending on species, so animal classification does not guarantee similar behavior. Thus, tracking changes in these animals' behavior is difficult.

In the study [18], the development of an automated monitoring system automatically tracking pig movement and assessing activities like locomotion, feeding, standing and drinking from 3D trajectory data was proposed. While predictions of drinking, feeding and standing were shown to be accurate, predictions of locomotion activities were not. An automated system using only one type of sensor, a video camera, tracked 3D pig locations and quantified several diagnostically valid behaviors non-invasively. The monitoring of behaviors like standing, feeding, drinking and not-feeding not-drinking (NNV) were enabled. The advantages of this system include the real-time monitoring of animal behavior providing insight into changes in welfare and allowing routine daily check-ups and observation of animals and livestock using a color video camera revealing changes in specific behaviors. However, the system requires many cameras to capture all animal movements and behaviors, making it very expensive to install and maintain.

The purpose of the research in the study [19] was to develop the first reliable telemetry system for the remote and continuous single-metabolite monitoring of glucose, lactate, glutamate, and ATP in mouse models using BioNano-Sensors. The project's control unit comprises a microcontroller in the center of the device. Software incorporated into the microcontroller enables responses to sensor inputs. Wi-Fi was used to send data collected by sensors. The procedure has been applied to domestic animals, cattle and other poultry species. It was easy to use, required less upkeep, and cost less. Furthermore, with advancements in technology, the majority of the modules.

A test was conducted to verify the functionality of the electronic head-strap device. Cattle were worn the prototype of the developed head-strap and guided by a hired herder around the campus environment with the permission of appropriate authorities and the results obtained are as presented in this session. From Figure 5, you can see it was attached to the head of a cattle to collect data from the cattle. Figure 6 is the mobile application (Ismart app) used to read the result. Ismart app is a mobile application software pre-programmed platform for researchers and other commercial vendors to integrate and test their devices. The orange circle reads the results of the body temperature test; the red reads the environmental temperature; the blue reads the agility test and below that is a map showing the location of the cattle. Table 1 displays data extracted from website. All data displayed on the website is in real-time and data history is also displayed. From the various tests carried out, the mobile network functions as intended and works properly. The results of the data on the mobile app and website for the temperature sensor, the ambient temperature, the agility test, and the GPS are gotten in real time. When the satellite module was tested in the absence of GPRS, it was discovered that it takes approximately 2 minutes for the Iridium satellite to send the message to the Rockblock site and have an impact on the mobile Apps (Ismart). This reason for the delay is as novelty modelled and derived mathematically in section 4.1.

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Figure 5. The device testing process

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Figure 6. Mobile (Ismart) application

Table 2 shows the comparative analysis of previous related works with the newly developed system. The criteria used in benchmarking include health status (ambient temperature, body temperature, magnetometer, agility), location tracking (GPS), mode of transmission (GPRS modem and Satellite modem) and mode of display (web dashboard and mobile applications). Of the twenty papers compared it was discovered that none of these papers deployed all the nine metrics as compared with our newly developed system that deployed all the nine.

4.1 A novel mathematical model of the delay on the satellite channel used

A novel mathematical modelling of the delay on the satellite channel deployed was derived and explained as fellows. Suppose a transmitted located in free space radiate power (P_t) uniformly in all direction, thus isotropic radiation. In practice, such a situation is not possible in satellite communication because it cannot create transverse polarized electromagnetic waves. So, in practice, directional antennae are used to concentrate the energy into a given direction and its directivity. The distance between the transmitter and receiver is called R. For such a system the power density (F) at a distance R is given as shown in Eq. (1).

$\mathrm{F}=\frac{P_t G_t}{4 \pi R^2}\left(\mathrm{watt} / \mathrm{m}^2\right)$             (1)

where,

P_t=Power transmitted by the transmitter

G_t=Gain of the transmitter

R=Distance between the transmitter and receiver antennas

The power Intercepted by the receiving antenna is equal to power density (F) multiplied by the capture area (A_r) as shown in Eq. (2).

$P_r=F * A_r$           (2)

With A_r some of the signals will be absorbed and some will be reflected backwards, hence the antenna has an efficiency factor.

Let the efficiency factor of the receiving antenna be Z_r (In the range of 50%-70%).

Power received (P_r) is as shown in Eq. (3).

$P_r=F A_r Z_r=\frac{P_t G_t A_r Z_r}{4 \pi R^2}$          (3)

Normally, we can show that the capture area (A_r) of the receiving antenna is directly related to the antenna gain (G_r) as shown in Eq. (4).

$G_r=\frac{4 \pi A_r Z_r}{\lambda^2}$          (4)

where,

$\lambda=\frac{c}{f}$

λ = Wave length

c=speed of light, f=operating frequency

Making A_r the subject of formulae as shown in Eq. (5).

$A_r=\frac{G_r \lambda^2}{4 \pi Z_r}$           (5)

Substituting Eq. (5) into Eq. (3), therefore

$\begin{gathered}P_r=\frac{P_t G_t Z_r}{4 \pi R^2} * \frac{G_r \lambda^2}{4 \pi Z_r} \\ P_r=P_t G_t G_r * \frac{\lambda^2}{(4 \pi R)^2}\end{gathered}$           (6)

Eq. (6) is the Friis equation of a radio link.

The link loss is the $\frac{\lambda^2}{(4 \pi R)^2}$ section and the only reason, where P_t=P_r is because of $\frac{\lambda^2}{(4 \pi R)^2}$.. The path in communication comes from here, because of the presence of R, as shown in Eq. (7).

$L_{f s}=\left(\frac{4 \pi R}{\lambda}\right)^2$           (7)

where,

$L_{f s}=\left(\frac{4 \pi R}{\lambda}\right)^2$ is the Friis’ path loss.

Substituting Eq. (7) into Eq. (6) result in Eq. (8).

$P_r=\frac{P_t G_t G_r}{L_{f s}}$            (8)

Therefore,

$P_r=\frac{\text { EIRP } * \text { Receive Antenna Gain }}{\text { Path Loss }}$

where, EIRP is the Effective Isotopic Radiation Power.

EIRP is define as radiated power with the gain of the antenna taken into consideration Eq. (8) in Decibel, is as shown in Eq. (9).

$P_r(d B)=\operatorname{EIRP}(d B)+G_r(d B)-\operatorname{Loss}(d B)$            (9)

where,

$\begin{gathered}E I R P=10 \log P_t G_t(d B) \\ G_r=10 \log \left(\frac{4 \pi A_r Z_r}{\lambda^2}\right) d B \\ L_{f s}=10 \log \left(\frac{4 \pi R}{\lambda}\right)^2 d B \\ L_{f s}=20 \log \left(\frac{4 \pi R}{\lambda}\right) d B\end{gathered}$

Eq. (9) refers to as idea situation in practice. There are other losses in satellite communication as shown in Eq. (10).

Total Loss $(L)=L_{f s}+L_{a d d}$           (10)

where, L_add=additional Losses:

$P_r=\frac{P_t G_t G_r}{L_{f s}}=\frac{P_t G}{L_{f s}}$          (11)

where, G=G_t G_r.

Total Loss of a satellite link is given as:

$L=\frac{L_{f s}}{G_t G_r}$              (12)

Substituting Eqns. (4) and (7) into Eq. (12).

$\begin{aligned} & L=\frac{\left(\frac{4 \pi R}{\lambda}\right)^2}{\left(\frac{4 \pi A_t Z_t}{\lambda^2} * \frac{4 \pi A_r Z_r}{\lambda^2}\right)} \\ & L=\frac{4^2 \pi^2 R^2}{\lambda^2} * \frac{\lambda^2}{4 \pi A_t Z_t} * \frac{\lambda^2}{4 \pi A_r Z_r} \\ & \lambda={ }^c / f \\ & \end{aligned}$             (13)

$L=\frac{R^2 \lambda^2}{A_t Z_t A_r Z_r}$            (14)

Substituting Eq. (13) into Eq. (14),

$L=\frac{c^2 R^2}{F^2} * \frac{1}{A_t Z_t A_r Z_r}$            (15)

In some cases, A_rand Z_r may not be known but diameter of the antenna will be given, hence, $A_r=d_r^2$ and $A_t=d_t^2$.

$L=\left(\frac{C R}{F}\right)^2 * \frac{1}{A_r Z_r d_t^2 d_r^2}$            (16)

Eq. (16) is the Satellite link equation.

In satellite the additional losses are as shown in Eq. (17).

$L_A=L_{t a}+L_{r a}+A_{a I}+L_{\text {point }}+A_{\text {pol }}+A_{\text {rain }}$               (17)

where,

L_A=Additional losses

L_ta=Losses associated with transmitted antenna

L_ra=Losses associated with receiver antenna

A_aI=Attenuation by atmosphere and Ionosphere

L_point=Losses causes as a result of antenna depointing

A_pol=Attenuation cause by polarization mismatch between transmitting antenna and receiving antenna

A_rain=Attenuation cause by rain (cloud)

These factors in Eq. (17) vary from time to time due to weather condition a particular area.