INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3883 www.rsisinternational.org
Development of Low-Cost Iot for Tree Instability Detection System
and Early Hazard Notification
Abd Shukur Jaafar1, Nur Alisa Ali, Mohd Harris Misran, Abd Majid Darsono, Zul Atfyi Fauzan Md
Napiah, M. N. Hazeq, N. Syafiqa, N. Ain Faqihah.
1Centre for Telecommunication Research & Innovation (CeTRI)
1Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK) Universiti Teknikal Malaysia
Melaka (UTeM)
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000315
Received: 14 October 2025; Accepted: 22 October 2025; Published: 11 November 2025
ABSTRACT
Urban tree failures have emerged as a growing safety issue in Malaysia, with nearly 5,000 reported incidents in
2023 that resulted in fatalities and considerable damage to surrounding infrastructure. Conventional monitoring
methods, including manual inspections and GIS-based management systems, remain largely reactive, time-
consuming, and costly to maintain. In response to these challenges, this study presents an Internet of Things
(IoT)–based early warning system capable of detecting potential tree instability in real time. The system
integrates MPU6050 motion sensors with ESP32 microcontrollers and employs LoRa communication
technology for long-range, low-power data transmission. Collected data are automatically uploaded to cloud-
based Google Sheets for continuous recording and analysis. When the measured tilt angles surpass a defined
threshold, immediate alerts are transmitted to responsible personnel through a Telegram Bot interface. This
integrated approach provides a practical, low-cost, and scalable solution that improves detection accuracy,
reduces reliance on manual observation, and facilitates more proactive management of urban trees. The use of
open-source platforms and readily available components also enhances system accessibility, making it suitable
for implementation by local authorities and community-based environmental initiatives.
Keywords: Tree monitoring system, internet-of-things (IoT), alert system.
INTRODUCTION
The increasing unpredictability of weather in Malaysia, characterized by sudden thunderstorms, heavy
downpours, and strong winds, has contributed to a worrying rise in incidents involving falling trees, particularly
in densely populated urban environments [1], [5], [12]. From January to July 2024, the Malaysian Fire and
Rescue Department recorded 2,575 cases of fallen trees, leading to 8 deaths and 27 injuries nationwide.
Urban trees are an important aspect of metropolitan life. They help cool the urban microclimate, enhance air
quality, and provide visual and psychological benefits to communities [1]. However, in recent years Malaysia
(and comparable urban settings) have seen a notable increase in tree-fall incidents under extreme weather such
as sudden storms, heavy rain, and strong winds [5], [12]. These incidents have damaged property, impeded
traffic, and, tragically, caused injuries and deaths. Over 2,500 tree-fall incidents were reported in the first seven
months of 2024 alone, with eight people killed and 27 injured. These figures highlight a major safety concern
that requires urgent attention, reinforcing calls for better monitoring of tree stability and failure risk [3], [4], [5],
[12]. One key reason these occurrences persist is that most tree monitoring is still conducted manually, with
alerts typically triggered only after public complaints or an incident has occurred; while some municipalities
employ GIS-based inventories, these tend to be costly, maintenance-heavy, and not designed for real-time alerts
[3]. With many aging trees near busy roads, buildings, and power lines, the risk profile remains elevated under
increasingly volatile weather [3], [5].
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3884 www.rsisinternational.org
As a result, hazardous trees often remain undetected until they fail [3], [4], [5]. This monitoring gap leaves local
governments with few options; some remove potentially risky trees based on precaution or general condition
even when they may still be structurally sound, resulting in unnecessary loss of healthy urban greenery that
mitigates heat islands, absorbs pollutants, and supports community well-being. Such practices also run counter
to broader sustainability goals that emphasize data-driven stewardship of urban trees.
To address this, there is a pressing need for a low-cost, scalable, and intelligent solution that provides real-time
insight into tree stability and detects early structural imbalance—an area where IoT/LPWAN and integrated
sensing have shown clear promise [1], [2], [6], [7], [9], [14]. In the context of tree movement, two key indicators
of risk are changes in pitch (forward/backward tilt) and roll (side-to-side tilt); these angles reflect how a tree
sways or leans and can serve as early warning signs of uprooting, especially during high winds or soil instability
[4], [5], [7]. Complementary modalities—UAV/remote sensing for canopy and phenology monitoring, machine-
learning models for physiological signals, rainforest sensor networks for harsh environments, and specialized
field devices—can reinforce a comprehensive monitoring architecture [8], [10], [11], [13], [15].
Beyond mitigating immediate hazards, a modern tree-monitoring architecture should fuse low-cost inertial
sensors for tilt/sway with sap-flow and stem-water measurements to capture physiological stress preceding
mechanical failure [4], [7], [14]. LPWAN (e.g., LoRa) enables multi-kilometer connectivity at low power for
continuous reporting and threshold-based alerts to municipal dashboards or messaging systems [2], [6], [9].
Overhead sensing from UAVs and high-resolution imagery can flag canopy dieback and phenological anomalies
at the individual-tree level, guiding targeted ground inspections [8], [13]. Machine-learning models further
improve forecasting of risk under volatile weather by learning patterns linking environmental drivers to
instability signals [10], while distributed sensor networks hardened for tropical conditions demonstrate
feasibility at scale in complex rainforest and urban environments [11].
METHODOLOGY
A. System Design
The process of development tree monitoring and alert system involves with three main phases which are
developing, testing, and implementation. The process of developing the system starts with a thorough analysis
of which suitable sensors such as MPU6050 which consist of accelerometer and gyroscope module. The
gyroscope measures the rotational velocity (rad/s) which is the change of the angular position over time along
the X, Y, and Z axis. The accelerometer measures the acceleration (rate change of velocity object) over X, Y,
and Z axis. There are three main components in the system which is the Node where the MPU6050 is connected,
the transceiver, and gateway as shown in Figure 1.
Figure 1: Three main components in system development.
The MPU6050 sensor will be connected to microcontroller as Node. This node will collect data in X, Y and Z
axis as roll, pitch, and yaw. Figure 2 shows the direction of roll, pitch and yaw to detect the movement direction
from a tree.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3885 www.rsisinternational.org
Figure 2: Direction of roll, pitch and yaw.
The formula for roll and pitch is given as below from accelerometer reading ax, ay, az:
In this project, only roll and pitch data are collected from each node as the yaw data is not necessary. These roll
and pitch equation then are embedded into microcontroller on each node to get new parameter roll, and pitch
parameter which represent rotation from front to back for roll, and rotation from side to side for pitch. All reading
both are in translate in degree unit.
Figure 3 depicts the subsystem of three main components nodes, LoRa transceiver, and LoRa gateway. The
nodes consist of ESP32 microcontroller, connected to MPU6050 to collect roll and pitch data. These data then
are transmitted to LoRa transceiver through ESP-Now protocol. The development of transceiver consists of
ESP32 and LoRa module. Data receive from node then is re-transmit using LoRa communication for long range
capability up to several kilometre range. At the gateway, LoRa module receive roll, and pitch data together with
node id. These data read by ESP32 micrcontroller and push data to the Googlesheet through Wi-Fi connectivity.
Notification will be sent to Telegram if the changes of roll and pitch is bigger than threshold setting which is
1.5.
Figure 3: Subsystem of Nodes, LoRa Transceiver and LoRa gateway.
ESP-NOW is a proprietary wireless communication protocol introduced by Espressif for its ESP32 and ESP8266
microcontrollers. Operating at the data-link layer of the OSI model, it bypasses several upper layers of the
conventional network stack, enabling lower latency and reduced communication overhead. The protocol is
optimized for transmitting short data packets, allowing rapid and efficient exchange of small amounts of
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3886 www.rsisinternational.org
information. Communication in ESP-NOW is based on MAC addresses, utilizing the unique hardware identifiers
of each ESP device for peer-to-peer connectivity. ESP-NOW is highly suitable for applications that demand fast,
low-power, and direct wireless communication between ESP devices. It is particularly advantageous in scenarios
involving small data packets and minimal network infrastructure, eliminating the need for routers or complex
network configurations.
Several experiment for range is test between Nodes and Transceiver using ESP-Now protocol. From actual tree
environment, it is found that the range of ESP-Now can dropped from 50m (in Line-of-sight) range to just 6.79m
if the environment which have lot of trees block the signal. This condition is considered as heavy Non-Line-of-
Sight condition (NLOS). From our record, in this situation the length can be up to just 6.79m before the
communication between nodes and transceiver failed. The Nodes can be placed in star-network topology
transmit data consist of node Id, roll, and pitch as shown in Figure 4.
Node Id (Refer to which tree) Roll data Pitch data
Figure 4: Node payload data send to transceiver.
LoRa, or Long-Range Wide Area Network (LoRa WAN), represents a significant advancement in wireless
communication technology, particularly within the Internet of Things (IoT) ecosystem. It employs Chirp Spread
Spectrum (CSS) modulation, which offers high resistance to interference and supports long-distance
communication with extremely low power consumption. In this project, the SX1278 series LoRa module was
selected primarily due to its cost-effectiveness for prototype implementation. Specifically, the LoRa RA-02
module from AI Thinker was chosen, and its key specifications are presented in Table 1.
Table 1. Specifications Of Lora Ra-02
Parameter Value/Range
Operating Frequency 433MHz
Transmission output power 18+/-1dBm
Receiver Sensitivity -141dBm
Antenna gain 0dBi
At the transceiver end, LoRa communication is employed to enable long-range data transmission to the LoRa
gateway. Based on testing under Line-of-Sight (LoS) conditions, the LoRa communication achieved a maximum
range of approximately 2.77 km. This indicates that the gateway can be strategically positioned within a Wi-Fi
coverage area, while the transceiver units can be deployed in tree-covered environments where Wi-Fi
connectivity is unavailable. Figure 5 illustrates the potential communication range and multi-network integration
within the tree monitoring system.
Figure 5: Implementation of tree monitoring system on tree area.
Gateway
ver
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
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Each node will be installed on a tree trunk. At an area, up to 5 nodes can be placed and will collect data of tree
Id, roll, and pitch and send it to the same transceiver. All nodes are connected to the LoRa transceiver in star
network topology. The range from nodes/tree to transceiver can be from 6.79m in worst case condition and in
line-of-sight condition up to 50m range.
B. System Development & Implementation
Figure 6 shows the node is installed on the tree trunk, serving as the primary sensing unit for data collection and
transmission within the monitoring system.
Figure 6: The node is installed on the tree trunk
The LoRa transceiver functions as the core communication module, responsible for transmitting and receiving
data between the sensor node (mounted on the tree trunk) and the LoRa gateway. It enables long-range, low-
power wireless communication, ensuring reliable data exchange even in remote or obstructed environments.
Figure 7: The LoRa transceiver functions as the centre communication module
Figure 8 depicts LoRa gateway that was developed in the WiBNet laboratory, serving as the central hub for
receiving data from multiple LoRa transceivers. It aggregates and forwards the collected information to the server
or cloud platform for further analysis, forming the backbone of the long-range communication network.
Figure 8: The LoRa gateway was developed in the WiB Net laboratory.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
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C. Line-of-Sight and non-Line-of-Sight Packet Loss for LoRa Data Transmission
The subsequent experiment investigated the impact of distance on packet loss in LoRa module data transmission
under both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions. In this setup, data were transmitted
from a LoRa Wi-Lo transceiver to a LoRa Gateway receiver using the SX1278 transceiver chip to establish
connectivity between the two devices. The LoRa communication parameters were configured with a default
Spreading Factor of 7 (SF7), providing an optimal balance between data rate and transmission range. The results,
illustrating the number of lost packets and corresponding RSSI values for both LOS and NLOS scenarios at
varying distances, are presented in Figure 9.
Figure 9: Packet Loss in LOS & NLOS Condition for Transmission Using LoRa
CONCLUSION
This research has successfully developed and demonstrated an IoT-based Early Warning Tree Monitoring
System aimed at improving the safety and sustainability of urban environments. By combining MPU6050 motion
sensors, ESP32 microcontrollers, LoRa long-range communication, and cloud-based tools such as Google Sheets
and Telegram, the system provides a practical and affordable solution for detecting tree instability in real time.
The results indicate that continuous monitoring of tilt variation particularly pitch and roll angles can serve as an
effective early warning signal before structural failure occurs. Beyond enhancing situational awareness, the
system reduces dependence on manual inspections and enables quicker responses to potential hazards, thereby
supporting proactive management of urban greenery. In a broader sense, the proposed approach reflects how
low-cost IoT technologies can strengthen municipal resilience and align with Malaysia’s vision for sustainable
and data-informed city planning. Overall, this work contributes to the growing field of intelligent environmental
monitoring by bridging technology and sustainability for safer, smarter urban ecosystems.
ACKNOWLEDGMENT
The authors would like to express gratitude to Centre for Telecommunication Research & Innovation (CeTRI),
Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka
for their invaluable support and resources provided throughout this research studies.
REFERENCES
1. W. Y. Chau, "AI-IoT integrated framework for tree tilt monitoring," IFAC-PapersOnLine, vol. 56, no. 8,
pp. 472-477, 2023, doi: 10.1016/j.ifacol.2023.03.472
2. F. Zanotto, "Wind-tree interaction: Technologies, measurement systems, and monitoring," Measurement,
vol. 184, 2024, doi: 10.1080/02757540.2024.1392398.
3. K. Frediani, "Tree Management System (TMS)," University of Dundee, 2025.
4. S. Patil, M. Kothari, M. Patrawala, and O. Madrewar, "Tree Health Monitoring and Management System
using IoT," IJNRD, 2025.
5. A. Abdella et al., "Towards tree-based systems disturbance monitoring of tropical mosaic landscapes,"
Chemosphere, vol. 320, 2023, doi: 10.1016/j.ces.2023.103484.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3889 www.rsisinternational.org
6. S. Abbas et al., "Tree tilt monitoring in rural and urban landscapes of Hong Kong," Ecological Indicators,
vol. 130, 2020, doi: 10.1016/j.ecolind.2020.106773.
7. A. Gupta et al., "GreenScan: Toward large-scale terrestrial monitoring of urban trees," IEEE Trans.
Geosci. Remote Sens., 2024, doi: 10.1109/TGRS.2024.10529969.
8. J. Breidenbach et al., "Tree species from space: a new product for German forest monitoring,"
International Journal of Remote Sensing, 2025, doi: 10.1080/01431161.2025.2530236.
9. A. Ouaknine et al., "OpenForest: a data catalog for machine learning in forest monitoring,"
Environmental Data Science, 2025, doi: 10.1017/eds.2025.5.
10. J. Francis et al., "Monitoring canopy quality and improving equitable urban heat equity with trees,"
Ecological Indicators, vol. 153, 2023, doi: 10.1016/j.ecolind.2023.108466.
11. N. Bagheri et al., "Appropriate vegetation indices and data analysis methods for orchard trees," ISPRS
Journal of Photogrammetry and Remote Sensing, 2025, doi: 10.1016/j.isprsjprs.2025.12.005.
12. International Tree Mortality Network, "Towards a global understanding of tree mortality," New
Phytologist, 2025, doi: 10.1111/nph.20407.
13. S. Abbas et al., "Tree tilt monitoring system using smart sensing," Sustainable Cities and Society, 2020,
doi: 10.1016/j.scs.2020.102267.
14. S. Patil et al., "IoT-based tree health monitoring system with remote data access," IJNRD, 2025.
15. F. Zanotto, "Advances in wind-tree interaction and monitoring technologies," Measurement, vol. 184,
2024, doi: 10.1080/02757540.2024.1392398.