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 Eng. Res. Express 7 (2025) 015429 https://doi.org/10.1088/2631-8695/adbfdd

PAPER

Empowering IoT connectivity with LoRa technology: a deep dive into

RECEIVED
6 November 2024
long-range communication
REVISED
26 February 2025
ACCEPTED FOR PUBLICATION
Monika Kashyap1 , Gourav Verma2 and Vidushi Sharma3,∗
12 March 2025 1
School of ICT, Gautam Buddha University, Greater Noida, India
2
PUBLISHED Electronics and Communication Engineering, Graphic Era deemed to be University, Dehradun, India
3
26 March 2025 Computer Science and Engineering, Graphic Era Hill University, Dehradun, India
∗
Author to whom any correspondence should be addressed.
E-mail: mkashyap856@gmail.com, gaurav.gspindia@gmail.com and vidushisharma2018@gmail.com

Keywords: LoRaWAN, IoT, LoRa architecture, LoRa finite state machine (FSM) model, LoRa applications, LoRa performance analysis

Abstract
This study investigates the potential of LoRa technology as a robust communication solution for
Internet of Things (IoT) applications, focusing on its ability to provide long-range, low-power, and
cost-effective connectivity. A literature review is carried out to uncover various research dimensions
of LoRa. Further, LoRa components and working are explained using the Finite State Machine (FSM)
model. It offers insights into state transitions during data transmission and reception. The paper
further examines the performance of the SX1278 module through field experiments, analyzing key
metrics such as Received Signal Strength Indicator (RSSI), packet loss, and time delay across varying
distances in semi-urban environments. The findings reveal LoRa’s suitability for diverse IoT
applications, including environmental and agricultural monitoring, where energy efficiency and long-
range communication are paramount. The maximum distance of observation in the test field is taken
as 150 m, and the minimum distance of observation is considered 10 m. It is found that at 10 m, the
LoRa test bench provides a time delay of 548 μs, RSSI as −88 dBm, and packet loss is 2%. While at 150
m, the time delay is 600 μs, RSSI is −120 dBm, and packet loss is 12%. Additionally, this work outlines
critical challenges such as energy optimization, scalability, and network congestion while proposing
future research directions to enhance the deployment and efficiency of LoRa-based systems.

1. Introduction

In recent years, wireless and mobile technologies, along with IoT, have contributed significantly to internet
traffic. With a wide range of applications that help society and human civilization, IoT is expected to expand
even more rapidly. Communication technologies such as Wi-Fi, Bluetooth, Zigbee, Cellular (LTE, 5G), NB- IoT
(Narrowband), Sigfox, and LoRa will be used to connect IoT devices to networks [1–3]. These technologies vary
in range, power usage, speed, and cost. However, they need to ensure that IoT devices can communicate
effectively and reliably in different environments. A comparison of these technologies is presented in table 1,
which shows the features of each technology. It is seen in the table that Wi-Fi and Bluetooth are preferred for
short-range applications, offering high bandwidth with significantly less power consumption. Cellular
technologies like LTE and 5G offer extensive coverage and high data rates but are often costly and power-
intensive. On the other hand, Zigbee provides a balance between power efficiency and range, yet it is still limited
when compared to more long-range options [4]. LoRa is emerging as a complementary technology to Wi-Fi,
Bluetooth, and cellular networks with a high range. The use of LoRa is growing quickly due to the various
advantages of Low-Power Wide-Area Networks (LPWAN) designed for long-range communication [5]. LoRa is
also highly energy-efficient, enabling devices to operate on battery power for longer periods of time. In some
applications, even for making it perfect for IoT applications requiring high lifetime [6]. Additionally, LoRa
operates on unlicensed radio frequencies, reducing costs since it doesn’t require expensive licenses like NB-IoT and
other technology, which depends on cellular networks [7]. LoRa’s scalability allows users to quickly set up private

© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Eng. Res. Express 7 (2025) 015429 M Kashyap et al

networks without relying on telecom operators, providing greater flexibility than NB-IoT. While Sigfox offers
limited data transmission capabilities, and NB-IoT is bound to cellular infrastructure, LoRa balances moderate
data rates and bi-directional communication, making it more versatile for diverse IoT applications. LoRa
performs better among LPWAN technologies, such as Sigfox and NB-IoT, due to its superior range, low power
consumption, and flexibility [8].
LoRa can achieve communication distances of up to 15 km in rural environments while maintaining low
energy usage, making it ideal for battery-powered IoT devices with long lifespans. Also, it works several
kilometers in cities, even when hitches like buildings are present. Unlike proprietary networks like Sigfox, LoRa
enables users to deploy private networks, offering greater control over data and security for IoT applications [9].
Furthermore, LoRa operates on the open LoRaWAN standard, which supports interoperability and adaptability,
fostering innovation and broad adoption in applications such as smart cities, agriculture, and industrial IoT.
LoRa has lower data rates, but its ability to transmit data over kilometers at low cost makes it perfect for remote
monitoring and wide-area networks. This makes it ideal for IoT systems in remote or hard-to-reach locations.
LoRaWAN networks are scalable, meaning thousands of devices can be connected to a single system, making
them perfect for large-scale IoT deployments. Their flexibility allows them to work with many IoT platforms,
increasing their adoption in various industries. With its ability to provide long-range, energy-efficient, and
reliable communication at a low cost, LoRaWAN has become a key technology for creating smarter, more
connected IoT systems in everyday life [10].
LoRaWAN is used in many IoT areas, such as smart agriculture, where it helps farmers monitor soil,
weather, and water conditions to improve crop management. Further, LoRa technology, combined with IoT
connectivity, is highly effective for environmental monitoring by enabling sensors to transmit data over long
distances with minimal power usage. This makes it ideal for remote areas where continuous monitoring of
parameters like air quality, temperature, water levels, and pollution is needed.
The real-time data collected supports the early detection of environmental changes, aiding in disaster
management, resource conservation, and sustainable development efforts [19]. LoRaWAN helps track goods
and assets in real-time, ensuring better supply chain management.
LoRa should be considered the optimal choice when an IoT application requires long-distance
communication in remote areas where traditional cellular infrastructure is unavailable or too costly. It is
particularly beneficial for applications such as agricultural monitoring, environmental monitoring, smart cities,
and asset tracking, where large-scale, low-power networks are essential. This makes it perfect for hard-to-reach
areas, like forests or remote locations. It can track air quality, water levels, climate change, temperature, and
pollution. This technology helps gather important information in real-time, making it easier to spot
environmental problems early and take action to protect the environment. LoRa technology has the ability to
bridge the gap between short-range and high-power cellular solutions, offering a highly scalable and flexible
communication framework. It is a compelling choice for IoT systems in diverse industries. Further, the
contribution of the paper is listed below:

• The paper analyses the performance analysis of LoRa Technology by evaluating the SX1278 module through
field experiments conducted in semi-urban environments. Further, the metrics such as RSSI, packet loss, and
time delay were analyzed at varying distances from 10 m to 150 m, providing insights into the behavior of the
technology under different conditions.
• Propose a Finite State Machine model to optimize the operation of LoRa devices and improve network
performance.
• Highlights the LoRa’s suitability for IoT applications, particularly in environmental and agricultural
monitoring, where long-range, low-power communication is essential.
• Identifies critical challenges, including energy efficiency, network scalability, congestion management, and
security, in the context of LoRa-based IoT systems.
• Provide recommendations for future research, focusing on low-power hardware, adaptive algorithms, and
integration with renewable energy sources to enhance the scalability and efficiency of LoRaWAN
deployments.

2. Literature review

Traditionally, Machine to Machine (M2M) networks relied on cellular technologies like 2G. However, with the
phasing out of 2G by some cellular operators in favor of newer technologies like Long-Term Evolution (LTE),
alternative solutions have emerged [20]. In contrast, these newer technologies offer increased bandwidth but

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Eng. Res. Express 7 (2025) 015429 M Kashyap et al

consume more power, rendering them unsuitable for many M2M use cases. This has led to the rise of Low Power
Wide Area Network (LPWAN) technologies such as SigFox, Random Phase Multiple Access (RPMA), NB-Fi,
Weightless, DASH7, and Long Range (LoRa), which compete for market share. Additionally, cellular industry
players are developing LPWAN solutions like NB-IoT and EC-GSM, which operate on licensed cellular
frequencies. Selecting an appropriate LPWAN technology involves comprehensively evaluating several critical
factors: range requirements, power consumption constraints, data rate needs, and deployment costs. For
example, applications that need long-distance communication in remote areas with little infrastructure can
benefit from LoRa technology because it has a more extended range and can better handle obstacles. Conversely,
applications that require higher data rates and denser network coverage might find more suitable cellular-based
LPWAN solutions like Narrowband IoT (NB-IoT) or LTE-M. A thorough evaluation of LPWAN solutions
should consider performance metrics such as coverage range, data throughput, network scalability, and power
efficiency. For example, LoRa typically offers data rates between 0.3 and 50 kbps, sufficient for many IoT
applications involving small data payloads. In contrast, NB-IoT can provide data rates up to 250 kbps, making it
better suited for applications requiring higher throughput. Additionally, factors like regulatory compliance,
spectrum availability, and the support ecosystem (including hardware and software development kits,
community support, and industry adoption) should be considered when selecting the most suitable LPWAN
technology for a specific application. LoRaWAN supports public and private network architectures, offering
businesses flexibility in network design and management.
The LoRa technology generally focuses on LoRa PHY and LoRaWAN as the two basic building blocks,
providing insights into network architecture, communication protocols, operating frequencies, and device
nature. LoRa is used for low signal-to-noise-to-interference ratio, scalability, and secure communication. For
these features, LoRa technology is a suitable choice for IoT applications [21, 22]. A LoRa network can be used for
smart home and healthcare monitoring [23] maritime activities in coastal areas [24]. A LoRa network, with
multi-wireless environment sensor nodes and a LoRa gateway, can be used for agriculture applications. The
researcher [25] highlights the feasibility and benefits of using LoRa technology in agriculture, building on
previous studies in smart buildings, homes, and farms. They introduce a methodology that includes designing
the LoRa network, sensor nodes, and LoRa gateway, focusing on low cost, effectiveness, and high sensing
accuracy. The design, implementation, and real-world testing of a LoRaWAN Gateway across various
configuration scenarios have been presented [26]. It underscores the significance of transmitting data at low
rates over extended distances with minimal power consumption, which is crucial for smart cities, smart parking
lots, and smart agriculture applications. The advantages of LoRaWAN technology include its affordability,
extended Node service life, and potential for ongoing exploration. LoRa technology can be used in the Sailing
Monitoring System—LoRa parameters influence data transmission time and coverage, identifying optimal
settings. A wireless network can be designed with IoT sensors for data collection and a remote monitoring
system via LoRa, allowing access from anywhere. It offers integrated greenhouse control over distances of 2 to 15
km, providing a solution for remote monitoring, particularly relevant during the COVID-19 pandemic.
Leveraging LoRa ensures reliability, low energy consumption, and cost savings, enabling efficient greenhouse
condition monitoring [27]. The study [28] demonstrates system performance in the Brazil Olympics sailing
venue, showing coverage and packet loss rate in the sea area. It discusses trade-offs in designing the system,
emphasizing parameter selection for optimal performance considering coverage, power consumption, data rate,
and transmission delay. Further, the performance of LoRa technology in urban and open areas is investigated
through both theoretical analysis and experimental methods. This concludes that LoRa can achieve
communication distances of up to 3 km in urban environments and up to 10 km in open areas [29]. Further
research is done to enhance LoRa network capacity and enable more practical installations. It discusses criteria
for spread spectrum modulation, highlighting improved modulation approaches and the use of multichannel
multimodem transponders for handling large volumes of data [30]. The authors also focus on the coverage area
and the effects of spreading factors. The spreading factor (SF) is the parameter that influences both the
communication range and data rate. The spreading factor defines the number of bits used to encode the data
symbol, controlling how long the radio signal spreads over time. It directly impacts the signal’s resilience to
noise, communication distance, and time to transmit data. It examines the impact of SF on communication
distance, providing insights for real applications in urban settings with high-elevation paths [31]. In the paper
[32], the authors introduce a LoRa-based mesh network model to enhance coverage and integrate with short-
range networks. They evaluate performance using OMNET++ simulations, focusing on delivery delays and
packet delivery ratios (PDR). The study also analyzes how parameters like SF, bandwidth (BW), and coding rate
(CR) impact data rates, offering insights for low-power IoT applications in LPWANs. The authors [33]
emphasize the significance of LPWAN technologies for smart city applications, including smart metering, smart
grids, smart parking, and environmental monitoring, showcasing the potential of LoRa in addressing these
needs. The paper [34] reviews LoRaWAN MAC layer operations and services per the LoRaWAN Alliance
specifications, emphasizing their importance for communication performance. It provides a detailed

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 Eng. Res. Express 7 (2025) 015429
Table 1. Technologies used in IoT systems.

Technology Range Data rate Power consump- tion Network archi- tecture Latency Band Cost
4

Wi-Fi [11] Up to 100 m Up to 54 Mbps Very low (up to sev-eral years battery life) Star or mesh topol- ogy Low (3 ms to 100 ms) 3ms to 100 ms Un- licensed Low
BLE [12] Up to 100 m Up to 2 Mbps Very low (up to sev-eral years battery life) Star or mesh topol- ogy Low (3 ms to 100 ms) Un- licensed Low
Zigbee [13] 10–100 m Up to 250 kbps Low (up to several years battery life) Mesh network Low (20 to 50 ms) Un- licensed Low
LoRa [14, 15] 15–20 km (rural), 2–5 km (urban) Upto 50 kbps Very low (up to 10 years battery life) Star topol- ogy High (up to several sec-onds) Un- licensed Low
NB- IoT [16] Up to 35 km Up to 250 kbps Low (up to 10 years battery life) Star topol- ogy Mode- rate (1.6 to 10 sec- onds) Licensed Mode- rate
Sigfox [17] 50 km (rural), 3–10 km (urban) Up to 100 bps Ultra-low (up to 10 years battery life) Star topol- ogy High (up to several sec- onds) Un- licensed Low
LTE- M [18] Similar to LTE (up to 100 km) Up to1 Mbps Low to moderate (up to 10 years battery life) Cellular Low (50 to 100 ms) Licensed Mode- rate to high

M Kashyap et al
Eng. Res. Express 7 (2025) 015429 M Kashyap et al

understanding of LoRa technology, focusing on MAC layer operations and modes crucial for optimizing data
exchanges. It also discusses recent research, highlighting challenges in enhancing data exchange performance
and efficiency, and identifies open research issues, offering a roadmap for future IoT communication studies
and optimizations. Recent research on LoRa has focused on analyzing the coverage and capacity of LoRaWAN
gateways for smart gas meter applications. It calculates the number of LoRa gateways needed to cover urban
areas. It configures LoRaWAN devices for smart gas customers using different parameters such as spreading
factors and capacity demand methods. The research determines the optimal number of LoRa gateways required
for effective urban coverage and sets the parameters for LoRaWAN devices to serve all smart gas customers [35].
Further, an investigation [36] has been done to determine the optimal IoT gateway placement at the University
of Zululand’s main campus to maximize building coverage. It evaluates existing algorithms and identifies
locations that enhance network performance regarding throughput, delay, and latency. It also highlights the
importance of effective gateway positioning for efficient IoT network operations. Further, the paper [37]
suggests suitable wireless communication technologies for Precision Agriculture based on their communication
range and power efficiency. In IoT, many wireless technologies, like LoRa, Wi-FI, and Sigfox, have been used to
develop the application. The research compares Wi-Fi and LoRa for rescue monitoring in IoT concepts,
emphasizing energy efficiency and trust in healthcare systems. It proposes an IoT-based architecture using both
technologies, focusing on LoRa’s benefits, like low power consumption and long-range communication for
tracking vulnerable groups [38]. The paper [39] compares and describes the technical differences between LoRa
and NB- INB-IoTerms of physical features, network architecture, and MAC protocol. It discusses the
application scenarios of LoRa and NB-IoT and explains their current status in Korea, Japan, and China. It gives
the advantages and disadvantages of both LoRa and NB-IoT based on their technological principles, highlighting
that each technology has its place in the IoT market, depending on specific application requirements. It
emphasizes the importance of considering factors like quality of service, latency, battery life, coverage, range,
deployment model, and cost when choosing the suitable technology for an IoT application.
Although numerous studies on LoRa have been conducted, several areas still require further investigation,
such as comprehensive studies on real-world deployments, particularly in dense urban areas where interference
and congestion affect performance. Additionally, security measures for LoRa applications remain under-
researched, especially in sectors like healthcare and smart cities. Furthermore, an FSM model for LoRa
communication is not present, limiting the understanding of device behavior and dynamics. Addressing these
gaps could enhance the practical deployment and effectiveness of LoRa technology in IoT applications.

3. LoRaWAN architecture

The architecture of a LoRa network follows a star-of-stars topology, where end devices (sensors or nodes)
communicate directly with LoRa gateways over unlicensed radio frequencies, typically in the ISM (Industrial,
Scientific, and Medical) bands.
The architecture consists of four key components: end devices, gateways, network servers, and application
servers, as shown in figure 1.

1. End Devices: An End Device in a LoRa network is a sensor or actuator responsible for collecting data or
performing actions based on received commands. These devices are deployed in the field and are designed to
operate with minimal power, often lasting several years on battery power. End devices can range from
environmental sensors, like temperature or humidity monitors, to more complex units, like GPS trackers or
smart meters. They communicate wirelessly with LoRa gateways using the LoRa modulation technique,
which allows for long-range data transmission while consuming very little energy. Depending on the
application, these devices can transmit data periodically or in response to specific events. End devices in a
LoRa network are categorized into classes A, B, and C—based on their communication patterns and power
consumption.

• Class A: These class Devices have the lowest power consumption and communicate in a scheduled manner
(uplink transmissions followed by two short downlink windows). Class A devices can send signals whenever
they need to. After sending a message, they wait to receive a reply from the gateway. They open two specific
time slots, called t1 and t2, after sending their message to listen for a response. The gateway can reply during
one of these slots but not both. Devices in Class B and C can also do everything that Class A devices can.
• Class B: Class B devices have open additional receive windows to synchronize downlink messages at scheduled
times. They receive a time-synchronized signal from the gateway, which tells them when to listen. However,
Class B devices cannot do everything that Class C devices can.

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Figure 1. LoRaWAN architecture.

• Class C: Class C devices always listen for messages from the gateway. This means they are always ready to
receive messages. However, they do not have the special features of Class B devices.

2. Gateway: A Gateway in a LoRa network serves as an intermediary between the end devices and the network
server. Its primary function is to receive data transmitted by end devices within its range and forward it to the
network server for further processing. Gateways are equipped with LoRa transceivers to pick up signals from
multiple end devices, often across several kilometers, depending on the environment. Once the gateway
receives the data, it sends it to the network server via a backhaul connection, such as Ethernet, cellular, or
satellite. Unlike traditional routers, LoRa gateways do not process or analyze the data; they simply act as relay
points. A single gateway can manage thousands of end devices, making them scalable for wide-area IoT
deployments. Additionally, multiple gateways can be deployed in a network to provide redundancy and
enhance coverage, ensuring data is reliably transmitted even in challenging environments. By facilitating
long-range communication and connecting dispersed IoT devices to centralized systems, Gateways are
essential for enhancing the efficiency and scalability of LoRa networks.
3. Network Server: The Network Server in a LoRa network is the central control point that manages
communication between end devices and application servers. It plays a crucial role in ensuring the smooth
operation and security of the entire system. When a gateway forwards data from end devices, the network
server processes and validates the information, removing duplicate messages if multiple gateways receive the
same transmission. The network server also handles encryption and decryption of data to ensure secure
communication and manages device authentication, ensuring that only authorized devices are allowed to
communicate on the network. Another essential function of the network server is to optimize network
performance through Adaptive Data Rate (ADR), adjusting the data transmission rate and power settings of
end devices based on signal quality and network conditions. Once the data is processed, the network server
routes it to the appropriate application server for further analysis or action. By orchestrating the data flow and
maintaining network efficiency, the network server acts as the brain of the LoRa network, ensuring that
devices can communicate reliably over long distances.

4. Working of LoRa

The LoRa system comprises several key components that work together to facilitate seamless communication.
First, an end device collects data and encodes this information into a LoRa packet, which is transmitted using
LoRa modulation, which allows for long-range communication while conserving power. This packet is picked
up by one or more LoRa gateways within range. The gateways, acting as relays, forward the data packets to the
network server via a backhaul connection. The network server checks the data for accuracy, removes any
duplicate messages that might have come from multiple gateways, and ensures it is secure. If necessary, the
server also decrypts the data. It performs device management tasks such as authentication and network
optimization through ADR, which adjusts the transmission settings based on network conditions. After
processing, the network server sends the data to the application server. In the present study, the LoRa module,

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Eng. Res. Express 7 (2025) 015429 M Kashyap et al

Figure 2. Component interaction of LoRa system.

Figure 3. FSM for LoRa client side.

Wi-Fi module, network server, and application server (Arduino-based data center) have been used to implement
the system.
The components of the LoRa system are given in figure 2. It analyzed and used that data for various purposes,
such as monitoring or controlling devices. This system ensures that data moves smoothly from the end devices to
the designated center for analysis.

4.1. Finite state machine Model for system

FSM encapsulates the complete behavior and interactions of LoRa devices across diverse operational scenarios.
This model should define clear states and transitions, incorporating elements such as network joining, data
transmission, and acknowledgment. A finite state machine can be used to manage the state transitions of a LoRa
transceiver (transmitter-receiver) during its operation. It shows four states: S0, S1, S2, and S3.figure 3 shows the
FSM for the LoRa client side and presents the operational states and transitions of a LoRa communication
process, focusing specifically on data transmission from the client side. The model starts in the Idle (S0) state,
where the device waits for a connection request (Conn_Req). Upon receiving this request, it transitions to the
Wait for Ack (S1) state, awaiting acknowledgment (Conn_Ack). If the acknowledgment is successfully received,
the device moves to the Ready (S2) state, indicating it is prepared for data transmission.In the Ready (S2) state,
the device can initiate data transmission, transitioning to the Data Transmission (S3) state. Here, the data
transmission occurs, leading to either a successful transmission, which returns the device to the Idle (S0) state, or
a failure, which may necessitate a reconnection attempt to maintain communication
If the acknowledgment is not received (No_Ack), the system can remain in the Wait for Ack (S1) state or
handle reconnection strategies to enhance reliability. Table 2 presents the transition states, such as the present
and next, and the actions for the client-side LoRa communication.
The FSM model, shown in figure 4, outlines the operational states and transitions of a LoRa communication
process on the server side. The model begins in the Idle (S0) state, where the device awaits a connection request
(Conn_Req). Upon receiving this request, the system transitions to the Wait for Ack (S1) state, where it waits for
an acknowledgment (Conn_Ack).
If the acknowledgment is received, the device progresses to the Ready (S2) state, preparing for data
transmission. Conversely, if no acknowledgment is received (No_Ack), the device may remain in this state or be

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Figure 4. FSM for LoRa server-side.

Table 2. FSM for Client-side.

S.no. Present state Action /Input Next state

1. S0 Conn Req S1
2. S1 Conn Ack S2
3. S1 No Ack S0
4. S2 Data transmission S3
5. S2 Disconnected S0
6. S3 Successful transmission S0
7. S3 Failure S2

Table 3. FSM for Server-side.

S. no. Present state Action /Input Next state

1. S0 Conn Req S1
2. S1 Conn Ack S2
3. S1 No Ack S0
4. S2 Data Received S3
5. S2 Disconnected S0
6. S3 Successful Received S0
7. S3 Failure S2

implemented as a retry mechanism. In the Ready (S2) state, the device is poised to send or receive data and can
transition back to Idle (S0) upon failure or to the Data Receiving (S3) state upon receiving data (Received data).
In the Data Receiving (S3) state, successful data reception leads to a return to the Idle (S0) state, while failures
may necessitate reconnection attempts.
Table 3 presents the transition states, such as the present and the next, and the actions for the server-side
LoRa communication. This FSM model effectively captures the key interactions and operational dynamics of
LoRa devices, enabling better understanding and optimization of performance and developing robust error-
handling strategies in IoT applications.

5. Performance analysis of the LoRa-based system

A testing environment is implemented to check the effectiveness of LoRa sender and receivers. The LoRa end
devices are placed on the university campus. Figure 5 shows a campus setup where four LoRa sender nodes are
positioned at different distances from a central LoRa receiver node. The specific distances between the nodes and
the receiver are 70 m, 122 m, 133 m, and 169 m, named Tx position 2, Tx position 1, Tx position 3, and Tx
position 4, respectively. This arrangement allows for testing LoRa communication performance across varying
ranges within a semi-urban environment. The system architecture of the LoRa Module Test Bench is shown in
figure 6. The figure shows that to test the LoRa module, we require one transmitter and one receiver. The
transmitter has the main components, such as a LoRa module, solar module, and MPPT (Maximum Power
Point Tracking), to harvest energy from the solar module and a battery as an energy element. The LoRa module

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Eng. Res. Express 7 (2025) 015429 M Kashyap et al

Figure 5. LoRa field test.

Figure 6. LoRa module test bench.

is transmitting packets that are received by the Receiver module. The Receiver module’s main components are
the LoRa module integrated with the IoT Module, i.e. ESP 32, which has WiFi/Bluetooth Connectivity. So, this
module demonstrates how we can connect it with the IoT cloud. However, in this work, we are only testing the
range and communication efficiency of the LoRa module initiating solar energy. The LoRa receiving module
receives the packet from the transmitter module and provides its acknowledgment.
In this setup, the data set generated is given in table 4. The distance is manually computed, whereas Time
delay, RSSI, Total packet transmitted, and Total Packet Received have been coded and received at the serial port
of Arduino IDE. The result shows that as the distance increases, the RSSI value gets more decreases, the delay
increases, and the packet loss percentage also increases. This configuration is critical for evaluating the
operational capabilities of LoRa in various applications, including environmental monitoring, precision
agriculture, and urban infrastructure management. Conducting tests in an outdoor setting helps in evaluating
signal integrity and performance metrics under varying environmental conditions.
This experimental arrangement highlights the effectiveness of LoRa for reliable long-range communication.
It emphasizes the need for further research to optimize deployment parameters, which can enhance the
robustness of LoRa networks.

5.1. Result analysis

Experiments are carried out in the system, and various performance indices are measured for analysis.

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Eng. Res. Express 7 (2025) 015429 M Kashyap et al

Figure 7. Received Signal Strength Indicator with varying distance between nodes.

Table 4. Dataset sample received at the receiver side.

S.no Distance (M) TimeDelay (μsec) RSSI (dBm) Total packet transmitted Total packet received % Loss

1 10 548 −88 100 98 2

2 20 548 −89 100 98 2
3 30 550 −89 100 99 1
4 40 552 −97 100 99 1
5 50 555 −98 100 97 3
6 60 559 −98 100 97 3
7 70 560 −99 100 98 2
8 80 563 −99 100 98 2
9 90 568 −103 100 96 4
10 100 572 −110 100 95 5
11 110 579 −110 100 96 4

5.1.1. Received signal strength indicator (RSSI)

The Received Signal Strength Indicator (RSSI) is an essential metric in LoRa technology, reflecting the intensity
of a received radio signal. Within LoRa networks.
RSSI is a gauge for the reception quality of a signal sent from a sensor or device to a gateway. RSSI is measured
in decibels relative to a milliwatt (dBm). In LoRa systems, RSSI usually ranges from −120 dBm, showing a weak
signal, to −30 dBm, which indicates a strong signal. The actual RSSI value can change depending on factors like
how close the transmitting device is to the gateway, environmental conditions, and any obstacles that may block
the signal. In our setup, the RSSI values ranged from −88 dBm to −120 dBm as a result. Figure 7 shows that the
RSSI value dropped as the distance between LoRa nodes increased, meaning the signal became weaker. This
highlights that LoRa signals lose strength over long distances, affecting communication reliability. Several
factors influence RSSI, including distance, physical obstructions, transmission power, antenna gain, and
environmental conditions such as weather.

5.1.2. Time delay

After positioning the devices, messages were transmitted, and the resultant time delays were measured in
microseconds. The study highlights the direct relationship between the distance between nodes and
communication latency in LoRa networks, showing how the distance affects message transmission. The results
showed that as the As the distance between the nodes increased, the message reception time also increased (see
figure 8). This increase in delay is primarily due to the propagation delay of radio waves over longer distances and
potentially additional processing times from the network.

5.1.3. Transmitted packets vs Received packets

In this experiment, 100 packets were transmitted over varying distances, and the number of packets successfully
received was recorded. Figure 9 shows a clear pattern that fewer packets are received as the distance between the

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Figure 8. Time delay with varying distance between nodes.

Figure 9. The comparative graph between transmitted and received packets as distance increases.

transmitting and receiving nodes increases. This means that longer distances make LoRa communication less
reliable, likely due to weaker signals and more interference. For example, at short distances, almost all packets
are received, but at longer distances, the number drops significantly. This highlights the need for careful node
placement to reduce packet loss and ensure reliable communication for IoT applications.

5.1.4. Packet loss percentage

As shown in figure 10, distance will impact communication, and more packets will be lost as the distance
increases. This phenomenon is particularly evident in LoRa networks, where the RSSI and signal-to-noise ratio
(SNR) degrade with increasing distance, leading to higher packet loss.
The signal is strong when nodes are near each other, and nearly all packets are sent and received successfully.
However, as the distance between them increases, the signal becomes weaker because of distance, obstacles in the
environment, and interference from other devices. As the signal weakens, there’s a greater chance of errors and
lost packets. LoRa uses different spreading factors to help with long-distance communication. However, these
higher spreading factors slow down the data rate and increase the time it takes to send messages, which can lead
to more signal collisions and further packet loss.

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Figure 10. Comparative graph depicting packet loss percentage with an increase in distance.

6. Discussion

LoRa plays a promising role in IoT applications despite there are many challenges and possible solutions for this
technology, as discussed below:

(i) Scalability- Limited bandwidth and duty cycle regulations are the main challenges when LoRa nodes
increase. Some future aspects of this technology for scalable options require the implementation of ADR
mechanisms, deploying multiple gateways for load balancing, and using hierarchical network architectures.
The development of standard algorithms for scalability in LoRa may also provide better future aspects.
(ii) Network Congestion- Adding more nodes, usually in urban areas, reduces packet loss and performance;
hence, network congestion may become a major problem. To minimize collisions and network congestion,
channel hopping, optimizing transmission intervals, and ensuring proper network planning with frequency
allocation are used.
(iii) Security- Eavesdropping, replay attacks, or key compromise are some of the attacks that may challenge
LoRa technology. We can utilize robust encryption methods (AES-128), ensure secure key management,
and implement firmware updates. Components that may provide better security for the LoRa network can
be used.
(iv) Energy Consumption- LoRa is a low-power communication technology generally used with batteries.
Maximizing the lifetime of the nodes is one of the challenges, and it can be encountered using sleep mode
adjustment, optimizing communication schedules, and dynamically adjusting transmitting power based on
the distance to the gateway. Energy harvesting may also play a significant role in powering the LoRa-based
nodes.
(v) Latency- Time-sensitive applications and challenging real-time applications require a high data rate, but
LoRa is a low data rate module. For such issues, hybrid setups like LoRa with 4G/5G technologies could be
better solutions.
(vi) Privacy Concerns- LoRaWAN transmissions may carry sensitive data, making privacy a concern if data
transmissions are intercepted. Implementing end-to-end encryption, secure data storage, and adhering to
data protection regulations like GDPR or HIPAA may provide better protection against transmission
interception.
(vii) Integration with Emerging IoT Technologies- Integrating LoRa with Emerging IoT Technologies like AI,
edge computing, and other wireless technologies like WiFI, Bluetooth, Zigbee, Zwave, etc, requires
compatibility and interoperability. Standardized APIs, using middleware solutions, and using an algorithm
to process data locally may provide better solutions to integrate the IoT with LoRa.

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6.1. Research observation and future work

In this paper, a system architecture has been developed to deeply investigate the LoRa module so that it can be
deployed in real-world applications with proper system design. Commercial-off-the-shelves have been used to
conduct the whole analysis. LoRa Module (SX 1278), Wi-Fi Module (ESP32), and supporting circuits have been
used to fabricate the proper architecture of LoRa communication. The investigation has been carried out in an
open field, so the experiments related to this research can be used for environmental monitoring or in
agriculture. Essential factors like RSSI, Packet loss, and Time delay have been closely monitored and observed.
This work is a clear example of implementing the LoRa module for various IoT applications. ESP32 has been
used with a LoRa module at the receiver or base station and can transmit the data even on the IoT cloud. An FSM
model has been developed better to understand the implementation and working of LoRa module
communication. The test field where the whole test is conducted has the coordinates at 30.223183 Lat,
78.076473Long. We fixed the receiver position and moved the transmitter, which has been discussed in table 2.
Table 2 provides the details of parameters like RSSI, time delay, and packet loss with respect to distance. The
value of RSSI decreases (figure 7) with respect to distance, which is theoretically correct. Further, due to
differences in RSSI with respect to distance, the value of time delay (figure 8) increases, and packet loss (figure 10)
increases. The results of LoRa can be considered for large-distance point-to-point communication at the 433
MHz frequency band, as per our investigation. It is observed that the power consumption of the fabricated
circuit at a transmission power of + 20 dBm (PA BOOST mode) is nearly 0.5 W and at reception time, it is
33 mW.
In future work, the transmitter will require too much attention to energy optimization for environmental or
agriculture monitoring. At the node level and integration with IoT systems, an important consideration of
power-optimized algorithms, Energy harvesting arrangements, and low-power electronics can be considered
a priority. Efficient scheduling, Dynamic Adaptive Data Rate, and Sleep-wake algorithms can help to optimize
the circuit in terms of power for IoT-based systems. Energy efficient network design, data processing, and
compression, and cross-layer optimization (QoS-Energy Trade-offs) should be considered in future work. We
have used solar panels to harvest solar energy. Further experiments can be carried out to find the energy
harvesting efficiency to power LoRa based system.

7. Conclusion

This research highlights the effectiveness of LoRa technology in enabling IoT applications that require long-
range communication and low power consumption. A comprehensive literature review of LoRa is presented.
LoRa components were explained, and a communication model using FSM was presented, which gave deep
insight into LoRa functioning. Further, the proposed FSM model provides a structured approach to
understanding device interactions and optimizing network performance. Further, an experiment was
conducted with LoRa devices placed at different locations on a university campus, which was semi-urban in
nature. Field experiments demonstrated that LoRa devices provide reliable communication over significant
distances, although factors such as RSSI degradation, packet loss, and increased latency at longer ranges need
careful consideration. The results showed that LoRa communication is best suited for IoT communication
where each node could not be the data center in the environment and agriculture applications. The LoRa
provides good communication at a direct line-of-site. It could be deployed to fetch the macro-level information
from the test field. Future work must address energy optimization through low-power hardware, adaptive
algorithms, and integration with renewable energy sources. Furthermore, advances in network scalability,
congestion management, and security measures will be crucial to fully realizing the potential of LoRa technology
in large-scale IoT deployments.

Funding

All authors declare that no funds, grants, or other support were received during the preparation of this
manuscript.

Conflicts of interest

The authors declare no conflict of interest or competing interests.

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Consent to publish

I affirm that all individuals involved in this research have provided their informed consent for the publication of
the research findings, including any personal or sensitive information, images, or data.

Data availability statement

The data cannot be made publicly available upon publication because no suitable repository exists for hosting
data in this field of study. The data that support the findings of this study are available upon reasonable request
from the authors.

ORCID iDs

Monika Kashyap https://orcid.org/0000-0003-0204-8094

Gourav Verma https://orcid.org/0000-0002-4431-4859
Vidushi Sharma https://orcid.org/0000-0002-1727-8960

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