IoT-Driven Livestock Monitoring: Leveraging LoRaWAN for Behavior

Analysis and Enhanced Farm Management

Khadijah Febriana, Rahul Thakur and Sudip Roy

Indian Institute of Technology Roorkee, India

Keywords:

Livestock Monitoring, IoT, Sensors, LoRaWAN.

Abstract:

Cattle play a crucial role in farming by providing essential resources such as milk, meat, leather, and labor,

contributing signiﬁcantly to both economic and social stability in rural areas of India. This work develops an

energy-efﬁcient IoT system based on LoRaWAN to monitor and analyze livestock behavior. The system em-

ploys an MPU6050 sensor and TTGO T-Beam microcontroller to capture livestock’s movement and positional

data. This data is continuously transmitted via a mesh network, utilizing The Things Network and ThingSpeak

for remote analytics. A neural network with two hidden layers and ReLU activation functions is trained with

sparse categorical cross-entropy loss. Validation on a 20% subset of the training data demonstrates high accu-

racy in classifying complex animal behaviors. Classiﬁcation results, including F1-scores, precision, and recall

metrics, highlight the model’s strong capability in behavior differentiation. Overall, this system enhances an-

imal health and welfare, improves farm productivity, promotes environmental sustainability, and strengthens

India’s food security.

1 INTRODUCTION

The Internet of Things (IoT) is a transformational

technology that connects numerous items over Inter-

net networks, allowing for real-time data collection

and exchange (Lu et al., 2024). This technology has

transformed several industries, particularly agricul-

ture, by driving innovation to improve productivity,

resource efﬁciency, and sustainability. In crop farm-

ing, IoT systems monitor crucial characteristics such

as temperature, humidity, soil moisture, and nutri-

ent levels to optimize resources and increase yields

(Chamara et al., 2022). Similarly, IoT devices provide

real-time monitoring of animal behavior, health, and

location in cattle farming, giving farmers actionable

data to improve herd management and farm produc-

tivity (McClune et al., 2014) (Ladha et al., 2013).

Agriculture and livestock production are critical

components of India’s economy, employing more

than half of the population and contributing signiﬁ-

cantly to the country’s GDP. Despite its crucial rel-

evance, the sector faces several obstacles, including

the negative consequences of climate change, volatile

commodity prices, and limited access to sophisticated

technologies. Livestock farmers, in particular, face

challenges in successfully monitoring animal health

and well-being, preventing strays and theft, and man-

aging their resources. These difﬁculties are exacer-

bated in rural settings, where animals frequently graze

freely throughout large distances, making them harder

to follow and protect. IoT offers a disruptive solution

by allowing for real-time monitoring of cattle con-

ditions, detecting behavioral abnormalities, and of-

fering early warnings of health risks (Farooq et al.,

2022). This enables farmers to take prompt, proactive

steps to improve animal care, cut losses, and improve

overall farm management.

While IoT applications in livestock husbandry are

still in their early stages in India, its potential to trans-

form traditional techniques is becoming more widely

recognized. Farmers can use IoT-based systems to

enhance resource utilization, reduce livestock losses,

and detect diseases early, supporting efﬁcient and sus-

tainable farming methods. Monitoring animal move-

ments and behavior can provide crucial information

about health and welfare. Changes in activity pat-

terns frequently indicate stress, illness, or injury, al-

lowing for early intervention (Atthari, 2017). This is

especially critical in rural India, where livestock roam

freely and are more likely to get lost or stolen. IoT-

enabled tracking solutions can help to reduce these

hazards, increase farm security, and boost economic

stability in farming communities.

LoRaWAN (Long Range Wide Area Network),

Febriana, K., Thakur, R. and Roy, S.

IoT-Driven Livestock Monitoring: Leveraging LoRaWAN for Behavior Analysis and Enhanced Farm Management.

DOI: 10.5220/0013296600003944

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 10th International Conference on Internet of Things, Big Data and Security (IoTBDS 2025), pages 273-280

ISBN: 978-989-758-750-4; ISSN: 2184-4976

273

Application

Data

pre-processing

Dataset

structure

Data collection

TTGO T-Beam

MPU6050

The Things Indoor

Gateway

Network Server: TTN

App Server: ThingSpeak

LoRa end-node Gateway Network & App Server

Set of Parameters

Freq: 868 MHz-915 MHz

Transmit power: 14 dBm

Spreading Factor (SF): 12

Bandwidth: 125 kHz, 250 kHz

Tabular Data

Scikit-Learn Train-test-split

Active Searching

Figure 1: Key Steps in the Proposed Method.

a wireless communication protocol, has emerged

as a game changer for IoT applications in remote

places, thanks to its long-range, low-power, and cost-

effective features. Previous research has demon-

strated LoRa’s effectiveness in tackling connectivity

difﬁculties in rural areas. (Joshitha et al., 2021) in-

vestigated a LoRa-based system for remote data trans-

mission, emphasizing its potential for IoT in locations

without standard communication networks. Similarly,

(Joshitha et al., 2021) created a long-range tracking

device that uses LoRa to ensure independence from

third-party networks while lowering operational ex-

penses. These tests demonstrate LoRa’s appropri-

ateness for livestock tracking, as it provides depend-

able, long-distance communication even in resource-

constrained environments.

This work’s primary IoT-driven livestock monitor-

ing device was the TTGO T-Beam microcontroller.

This device integrates LoRa, GPS, WiFi, and an

MPU6050 sensor, making it ideal for remote areas

with limited connectivity and power supply. The

integration of GPS allows exact location tracking,

which is critical for animal safety and risk mitiga-

tion during free-range grazing (Angriawan and Anu-

graha, 2019). The WiFi feature enables seamless data

transmission to cloud systems, which allows farmers

to access real-time information remotely (Mahaputra

et al., 2019) (Putra and Romahadi, 2021). Further-

more, the MPU6050 sensor, which can measure ac-

celeration and angular velocity, gives extensive data

on animal motions and orientation, allowing the de-

tection of anomalous behavior that may signal distress

or disease (Fedorov et al., 2015). This combination of

technologies provides India’s scalable, efﬁcient, and

cost-effective livestock management system.

LoRaWAN is chosen because of its unrivaled ben-

eﬁts in rural and agricultural environments. Its long-

range communication capability, low power con-

sumption, and use of unlicensed frequency bands

make it perfect for large-scale applications such as

livestock tracking (Davcev et al., 2018). LoRaWAN

offers a dependable, low-cost alternative to standard

communication technologies in areas with limited in-

ternet and electricity. Using the TTGO T-Beam, this

study intends to address the unique issues that live-

stock producers in India confront by providing an in-

tegrated system to integrate real-time monitoring, lo-

cation tracking, and behavioral analysis. This tech-

nology improves farm management and enables farm-

ers to improve animal care, to cut losses, and to in-

crease economic resilience in order to face the chang-

ing agricultural problems.

2 MATERIALS AND METHODS

Our methodology involves a series of key steps: data

collection and processing, data enhancement, model

implementation, and results analysis. These stages

are designed to ensure a comprehensive approach to

the system’s development and evaluation. Fig. 1 de-

picts the proposed methodology.

IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security

274

2.1 Data Collection

The primary goal of this data collection is to develop

and evaluate a low-cost localization system using the

TTGO T-Beam device, equipped with a GPS sensor,

MPU6050, and connected to a LoRa Gateway. The

dataset collected consists of 1000 data points trans-

mitted over the IN865-867 frequency band, which is

speciﬁcally suited for LoRaWAN communication in

this region. The data collection site is situated on the

IIT Roorkee campus in Uttarakhand, India, with co-

ordinates 29

◦

′

′′

N, 77

◦

′

′′

The IIT Roorkee campus, spanning approximately

1.48 km

, features a mix of academic buildings, labo-

ratories, dormitories, and extensive green areas, with

the majority of the terrain being ﬂat. Despite the over-

all ﬂatness, the presence of tall buildings and dense

vegetation within the campus can signiﬁcantly impact

LoRa signal propagation. These physical structures

cause signal blockage, multipath fading, and interfer-

ence from other wireless communication systems op-

erating in the same frequency band. Such environ-

mental factors limit the effective transmission range

of the LoRa signals, particularly when the signals

must pass through or around buildings. These chal-

lenges highlight the importance of considering the ter-

rain and the potential obstacles when designing and

deploying a wireless communication system, as they

directly affect signal reliability and the performance

of the localization system.

By collecting data in such an environment, we aim

to simulate real-world conditions that may be encoun-

tered in urban or semi-urban settings, providing valu-

able insights into the system’s ability to overcome

these challenges and ensure reliable communication

and localization over long distances.

Figure 2: IoT Hardware Prototype.

2.2 Hardware Setup

The hardware setup utilizes a TTGO T-Beam powered

by a lithium battery, along with the NEO-6M GPS

module and communication options such as WiFi,

Bluetooth, and LoRa to build an IoT-based monitor-

ing system (Sugiarto et al., 2023). The TTGO T-

Beam serves as the main component for data process-

ing and networking (Sugiarto et al., 2023).

The MPU6050 sensor, connected via I2C, gathers

motion and orientation data (acceleration and angu-

lar velocity) to measure activities. The LoRa module

provides long-range, dependable data transmission in

rural locations, with WiFi serving as a backup dur-

ing LoRa disturbances to improve reliability. WiFi

is only enabled in speciﬁed situations, such as LoRa

transmission failures, ﬁrmware updates, or large data

transfers. Deep sleep mode on the TTGO T-Beam de-

tects LoRa failures and shuts idle WiFi, boosting en-

ergy economy, system stability, and cost savings.

The TTGO T-Beam processes data from the

MPU6050 and GPS modules before transmitting it

over LoRa to The Things Indoor Gateway, which

sends it to the cloud for analysis. This system pro-

motes real-time monitoring with a streamlined con-

ﬁguration, as seen in Fig. 2, Table 1 and Table 2. The

TTGO T-Beam is equipped with a Neo-Block based

GPS module (usually based on the u-blox NEO-6M

chip or similar). Sky conditions, obstructions, and

signal interference can affect GPS accuracy. GPS ac-

curacy on the TTGO T-Beam is usually around 2.5

- 5 meters in open conditions with a good signal. At

the same time, the MPU6050 sensor resolution for the

accelerometer is 16384 LSB/g in the ±2g range, and

for the gyroscope, the resolution is 131 LSB/°/ s in

the range ±250°/s. Unlike (Islam et al., 2024), which

used advanced signal analysis, this work focuses on

practical IoT applications for real-time monitoring.

Table 1: Speciﬁcations of Node.

Model TTGO T-Beam v1.1 ESP32

ISM bands (MHz) IN865-867

Semtech SX1276

Transmit power 20 dBM

SF 7, 12

BW (kHz) 125 kHz, 250 kHz

Power consumption

Active mode (GPS & LoRa transmission):

100-150 mA

Idle mode: 10-15 mA

Deep sleep mode: 1-2 mA

Weight 52 g

IoT-Driven Livestock Monitoring: Leveraging LoRaWAN for Behavior Analysis and Enhanced Farm Management

275

Table 2: Speciﬁcations of the Gateway.

Model The Things Indoor

Gateway

Frequency EU868

TX power 20 dBm

Chipset SX1308

Dimensions 90 × 80 × 40 mm

LoRaWAN Spec Version V.1.0.3

2.3 Connection Diagram

This work employs an MPU6050 sensor connected to

a TTGO T-Beam board via the Inter-Integrated Cir-

cuit (I2C) protocol, a widely used two-wire interface

with Serial Clock Line (SCL) and Serial Data Line

(SDA). I2C enables multiple devices to connect to

a single bus using unique addresses, making it efﬁ-

cient for systems with many peripherals (Nguyen and

Dugenske, 2018), (Jouhari et al., 2023). Compared

to SPI, I2C uses fewer pins. It offers the ﬂexibility

of multiple master-slave conﬁgurations, making it an

ideal choice for reliable communication between the

MPU6050 sensor and the TTGO T-Beam board (Chen

and Huang, 2023).

The TTGO T-Beam board also includes a GPS

module connecting to the ESP32 microcontroller

via the Universal Asynchronous Receiver-Transmitter

(UART) protocol. UART is a simple serial communi-

cation protocol that sends data between the GPS mod-

ule and the microcontroller via the TX (transmit) and

RX (receive) pins. The GPS module provides crit-

ical position data such as latitude, longitude, time,

and speed, which the ESP32 processes before trans-

mitting over LoRa or cloud storage. Unlike I2C and

SPI, UART does not require a clock signal and instead

transfers data at synchronized baud rates (e.g., 9600

or 115200 bps) (Chen and Huang, 2023) (Sharma

et al., 2018).

By integrating I2C for sensor connectivity and

UART for GPS data sharing, the TTGO T-Beam ef-

fectively gathers, processes, and sends data, making

it ideal for IoT-based monitoring systems.

2.4 Proposed System Model

We have developed an IoT-based system model that

monitors the location and movement of livestock by

integrating the MPU6050 sensor with the TTGO T-

Beam module. The MPU6050 sensor measures three

components of acceleration (Accel x, y, z) and three

components of angular velocity (Gyro x, y, z), as

shown in Fig. 3. These measurements allow for a de-

tailed analysis of the livestock’s movement and orien-

Figure 3: Data Collection for Monitoring and Tracking.

tation, which are critical for understanding their be-

havior. Additionally, the system collects data on lo-

cation (latitude and longitude), movement speed, and

time, which are essential for tracking the livestock’s

geographic position and activity patterns. The com-

bination of acceleration and angular velocity data,

along with GPS data for location tracking, provides

a comprehensive overview of the animal’s behavior

and movement.

As shown in Fig. 3, the GPS data is represented

by a red line for latitude and a blue line for longi-

tude, which tracks the livestock’s geographic move-

ment over time. This visualization allows for easy

tracking of the animal’s movement across various ter-

rains, and helps monitor changes in their behavior that

may indicate stress, illness, or other signiﬁcant events.

The ability to collect both movement (acceleration

and angular velocity) and location data in real-time

provides valuable insights into livestock behavior and

can aid in early detection of potential issues, improv-

ing overall animal welfare and farm management.

The TTGO T-Beam, equipped with a GPS mod-

ule, serves as the central device in this monitoring

system. The data collected by the system, as shown in

Fig. 4, is crucial for real-time monitoring and perfor-

mance analysis of the livestock. The analysis of ani-

mal movement patterns, activities, and geographic lo-

cations helps farmers make better-informed decisions

regarding the care and management of their livestock.

The MPU6050 sensor provides detailed movement

and orientation data, while the GPS module tracks the

geographic location, both of which are essential for

accurate monitoring.

This system is speciﬁcally designed to operate in

rural areas, where cellular network infrastructure may

be sparse or non-existent. By utilizing the LoRa com-

munication protocol, the system ensures reliable data

transmission over long distances, even in areas with

limited connectivity. This capability is critical for

monitoring livestock in remote regions, where tra-

ditional communication networks may not be avail-

able. The ultimate objective of this system is to enable

more informed decision-making in livestock manage-

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276

(b)

(a)

(c)

Figure 4: Data collection over time from (a) Accelerometer, (b) Gyroscope and (c) GPS.

Figure 5: Location Tracking.

ment, enhancing farm productivity, improving animal

welfare, and fostering sustainable farming practices.

Consistent and accurate monitoring of the animals’

movements and behaviors through this system helps

ensure that any changes or issues are quickly detected

and addressed. This system helps monitor the posi-

tion of livestock, whether they are moving and in the

right group or area or if their position deviates from

the range location, as shown in Fig. 5.

2.5 Networking

This system employs LoRaWAN technology for long-

range, energy-efﬁcient data communication. TTGO

T-Beam devices collect data and transmit it via The

Things Indoor Gateway, which connects to the inter-

net through The Things Network (TTN). LoRaWAN’s

low-power, wide-area capabilities suit rural areas

where cellular networks are unreliable or unavailable.

Once data reaches the cloud, it is automatically

integrated into ThingSpeak using webhooks. ThingS-

peak is a good platform for livestock monitoring

with TTGO T-Beam and MPU6050 sensors, includ-

ing real-time visualization options like movement

graphs and GPS mapping. Its MATLAB interoper-

ability allows for sophisticated behavior prediction

and anomaly detection analytics. ThingSpeak makes

IoT data administration and visualization easier be-

cause of its low cost, user-friendly interface, and aca-

demic popularity.

ThingSpeak’s real-time dashboard allows contin-

uous livestock monitoring by providing graphical data

on movement patterns and activity levels. This user-

friendly interface makes tracking animal behavior

easier and detecting irregularities, both of which are

necessary for effective farm management. Figure 6

shows how data ﬂows from the TTGO T-Beam to the

cloud for analysis.

The system uses a mesh network architecture as

shown in Fig. 7 to improve coverage and dependabil-

ity in remote regions. According to (Jain et al., 2021),

mesh networks provide redundancy by enabling de-

vices to interact directly and take over if a link breaks.

This decentralized structure increases data transmis-

sion reliability, making the system more durable and

resilient for monitoring cattle under difﬁcult condi-

tions.

3 RESULT ANALYSIS

This work uses temporal data from a GPS module and

MPU6050 sensor to track location and movement.

The model pipeline classiﬁes livestock behaviors us-

ing neural network analysis of multidimensional sen-

sor data such as accelerometer, gyroscope, GPS, and

temporal characteristics as shown in the Fig. 4. Fol-

lowing data preprocessing and feature scaling, the

neural network, which consists of two hidden lay-

ers with ReLU activation, is trained for 100 epochs

and veriﬁed on a 20% subset of training data to en-

sure performance stability. The ﬁnal test accuracy

and classiﬁcation report—which includes precision,

recall, and F1-scores for each class—demonstrates

the model’s ability to distinguish between distinct an-

imal behaviors. High metrics reﬂect the model’s ef-

IoT-Driven Livestock Monitoring: Leveraging LoRaWAN for Behavior Analysis and Enhanced Farm Management

277

Figure 6: Network Connection for The IoT-based System for Livestock.

LoRa Coverage Area

LoRa

Gateway

Figure 7: Mesh Network.

ﬁcacy in behavior recognition, but lower scores for

certain classes may identify locations where sensor

signal overlap or data imbalance affects classiﬁcation

precision. Overall, the results support the model’s ro-

bustness for animal behavior categorization, while the

speciﬁc performance metrics provide insights for fur-

ther improvement in terms of feature representation

optimization or dataset diversity enhancement.

From Fig. 8 (a), each line or series of points repre-

sents a distinct metric (precision, recall, or F1-score),

allowing for an intuitive comparison of how well the

model performs in each class. Precision values show

the accuracy of positive predictions for each class, in-

dicating how many of the model’s positive predictions

are right. Recall, on the other hand, measures the

model’s ability to capture all true positives, indicat-

ing how well it recognizes examples of each class.

Finally, the F1-score, a balanced measure that com-

bines precision and recall, gives a complete picture of

the model’s accuracy in each class. Because all metric

values are scaled between 0 and 1, with 1 representing

the optimum score, the y-axis remains ﬁxed within

this range. This arrangement allows us to rapidly

identify which courses are tough for the model, as

lower points or dips in the line indicate regions where

the model may be underperforming.

The training and validation loss curves as illus-

trated in Fig. 8 (b) provide essential information about

the model’s learning dynamics and generalization

ability. Initially, both training and validation losses

are dramatically reduced, indicating effective feature

learning and model adaptation to the dataset. How-

ever, at epoch 10-15, the validation loss begins to

diverge from the training loss, gradually increasing

while the training loss decreases. This divergence,

which widens the gap between training and valida-

tion losses, indicates overﬁtting. The model appears

to be learning speciﬁc patterns and noise from train-

ing data rather than extracting generalizable charac-

teristics relevant to unseen data, as indicated by the

ﬂuctuations and overall increase in validation loss.

Early halting, dropout regularization, or weight pe-

nalization could all be used to reduce overﬁtting and

improve generalization. Furthermore, extending the

dataset or using data augmentation may enhance the

model’s ability to generalize. This analysis empha-

sizes the necessity of monitoring loss trends to detect

overﬁtting, which is necessary for deploying robust

models in real-world applications.

The sparse categorical cross-entropy loss func-

tion, which is used in the model’s training phase, cal-

culates the difference between the model’s projected

probability for the true class and the actual class label.

The loss for a single data point is as in the Eqn 1:

L = − log(p

) (1)

where p

represents the expected probability of the

true class y, and y represents the instance. For a

dataset with several samples and classes, the aver-

age sparse categorical cross-entropy loss over all in-

stances is given by Eqn 2:

Loss = −

∑

i=1

log(p

(i)

) (2)

where N is the total number of samples in the dataset,

and p

(i)

is the estimated probability of the correct

class y

for the i-th sample. In this case, for each data

instance, the model generates a probability distribu-

tion across all potential classes. The sparse categor-

ical cross-entropy loss function penalizes the model

according to how far the predicted probability for the

true class deviates from 1. The closer p

(i)

is to one for

the correct class, the lower the loss for that instance.

The loss function encourages the model to pro-

vide a high probability to the correct class and a

low probability to the incorrect class. During train-

ing, the model’s weights are modiﬁed to reduce the

average loss across all occurrences, enhancing the

model’s ability to predict the proper class label. The

IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security

278

Figure 8: (a) Classiﬁcation Metrics and (b) Training and Validation Loss.

training and validation loss curves in Fig. 8(a) reﬂect

the sparse categorical cross-entropy loss estimated at

each epoch. The training loss steadily drops as the

model optimizes to better suit the training data, how-

ever the validation loss gradually increases after a

certain point, indicating probable overﬁtting. By de-

creasing this loss function, the model increases clas-

siﬁcation performance until it begins to memorize the

training data as evident from the rising validation loss.

4 CONCLUSIONS

In this paper, we have designed an IoT-based sys-

tem that can effectively track and monitor livestock

animals, ensuring low power consumption and long-

distance communication capabilities. Hence, this sys-

tem is suitable for remote areas with limited cellular

network access. Such real-time monitoring systems

can prevent theft and loss of livestock animals, which

are common issues in livestock farming. This system

can help identify the speciﬁc activities based on the

behavior and movement patterns of the animals by

analyzing the GPS, accelerator, and gyroscope data.

Future enhancements of this system could involve

improving the range and quality of LoRa signals in

challenging environments like dense forests. Opti-

mizing the power consumption for better battery life

and incorporating additional sensors to monitor an-

imal health and environmental conditions could fur-

ther enhance the capabilities of the proposed system.

Applying machine learning-based techniques to ana-

lyze large datasets may provide valuable insights into

the movement of livestock animals and their health

conditions. Moreover, features like voice commands

or automated report generation will improve the user

interaction and decision-making process in the next

versions of the system in the future.

ACKNOWLEDGEMENTS

This work was partially supported by the Doctoral

Fellowship in India (DIA) program of the Ministry

of Education, Government of India.

REFERENCES

Angriawan, R. and Anugraha, N. (2019). Sistem Pelacak

Lokasi Sapi dengan Sistem Komunikasi LoRa. Inspi-

ration: Jurnal Teknologi Informasi dan Komunikasi,

9(1):33–39.

Atthari, A. (2017). Sistem Tracking Position Berdasarkan

Titik Koordinat GPS Menggunakan Smartphone. Ju-

rnal Infomedia: Teknik Informatika, Multimedia, dan

Jaringan, 2(1):2549–1180.

Chamara, N., Islam, M. D., Bai, G. F., Shi, Y., and Ge,

Y. (2022). Ag-iot for crop and environment monitor-

ing: Past, present, and future. Agricultural systems,

203:103497.

Chen, J. and Huang, S. (2023). Analysis and Comparison of

UART, SPI and I2C. In 2023 IEEE 2nd International

Conference on Electrical Engineering, Big Data and

Algorithms (EEBDA), pages 272–276.

Davcev, D., Mitreski, K., Trajkovic, S., Nikolovski, V., and

Koteli, N. (2018). Iot agriculture system based on lo-

rawan. In 2018 14th IEEE International Workshop on

Factory Communication Systems (WFCS), pages 1–4.

Farooq, M. S., Sohail, O. O., Abid, A., and Rasheed, S.

(2022). A survey on the role of iot in agriculture for

the implementation of smart livestock environment.

IEEE Access, 10:9483–9505.

Fedorov, D., Ivoilov, A. Y., Zhmud, V., and Trubin, V.

(2015). Using of measuring system mpu6050 for

the determination of the angular velocities and lin-

ear accelerations. Automatics & Software Enginery,

11(1):75–80.

Islam, K. Z., Murray, D., Diepeveen, D., Jones, M. G., and

Sohel, F. (2024). Lora localisation using single mobile

gateway. Computer Communications, 219:182–193.

IoT-Driven Livestock Monitoring: Leveraging LoRaWAN for Behavior Analysis and Enhanced Farm Management

279

Jain, B., Trivedi, K., Agarwal, S., and Thakur, R. (2021).

MeshSOS: An IoT Based Emergency Response Sys-

tem. In HICSS, pages 1–10.

Joshitha, C., Kanakaraja, P., Bhavani, M. D., Raman, Y.

N. V., and Sravani, T. (2021). LoRaWAN Based Cattle

Monitoring Smart System. In ICEES, pages 548–552.

Jouhari, M., Saeed, N., Alouini, M.-S., and Amhoud, E. M.

(2023). A survey on scalable lorawan for massive iot:

Recent advances, potentials, and challenges. IEEE

Communications Surveys & Tutorials, 25(3):1841–

1876.

Ladha, C., Hammerla, N., Hughes, E., Olivier, P., and

Ploetz, T. (2013). Dog’s life: wearable activity recog-

nition for dogs. In Proceedings of the 2013 ACM inter-

national joint conference on Pervasive and ubiquitous

computing, pages 415–418.

Lu, J., Li, W., Sun, J., Xiao, R., and Liao, B. (2024).

Secure and Real-Time Traceable Data Sharing in

Cloud-Assisted IoT. IEEE Internet of Things Journal,

11(4):6521–6536.

Mahaputra, I., Agung, I., and Jasa, L. (2019). Rancang

Bangun Sistem Keamanan Sepeda Motor dengan GPS

Tracker Berbasis Mikrokontroler dan Aplikasi An-

droid. Majalah Ilmiah Teknologi Elektro, 18(3):361–

368.

McClune, D. W., Marks, N. J., Wilson, R. P., Houghton,

J. D., Montgomery, I. W., McGowan, N. E., Gormley,

E., and Scantlebury, M. (2014). Tri-axial accelerome-

ters quantify behaviour in the eurasian badger (meles

meles): towards an automated interpretation of ﬁeld

data. Animal Biotelemetry, 2:1–6.

Nguyen, V. and Dugenske, A. (2018). An I2C Based Archi-

tecture for Monitoring Legacy Manufacturing Equip-

ment. Manufacturing Letters, 15:67–70.

Putra, A. and Romahadi, D. (2021). Sistem Keamanan

Sepeda Motor Berbasis Internet Of Things (Iot) Den-

gan Smartphone Menggunakan Nodemcu. Jurnal

Teknologi Terpadu, 9(1):77–87.

Sharma, R., Pandey, B., Jha, V., Saurabh, S., and Dabas, S.

(2018). Input–output standard-based energy efﬁcient

uart design on 90

anm fpga. In System and Architec-

ture: Proceedings of CSI 2015, pages 139–150.

Sugiarto, E., Suhendi, A., Abdussalam, M. Y., Husniah,

Z. A., and Lestari, A. P. (2023). Prototype design of

a ﬁshing boat safety monitoring system using lora and

microsensor devices. JMECS (Journal of Measure-

ments, Electronics, Communications, and Systems),

10(2):77–85.

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