Journal of Threatened
Taxa | www.threatenedtaxa.org | 26 April 2026 | 18(4): 28662–28667
ISSN 0974-7907 (Online) | ISSN 0974-7893 (Print)
https://doi.org/10.11609/jott.9750.18.4.28662-28667
#9750 | Received 14 March 2025 | Final received 15 March 2026| Finally
accepted 09 April 2026
Impact analysis of SMS-triggered elephant activity alert lights
Sanjoy Deb 1 ,
Sannasi
Chakravarthy Surulimani Ramaraj
2 , Sharmila
Arumugam 3 & Saravana Kumar Radhakrishnan
4
1,2,3 Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam,
Tamil Nadu 638401, India.
4 School of Electronics
Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127,
India.
1 sanjoydeb@bitsathy.ac.in, 2 sannasi@bitsathy.ac.in,
3 sharmilaa@bitsathy.ac.in, 4 r.saravanakumar@vit.ac.in
(corresponding author)
Editor: Heidi Riddle, Riddle’s
Elephant and Wildlife Sanctuary, Arkansas, USA. Date of publication: 26 April 2026 (online & print)
Citation: Deb, S., S.C.S. Ramaraj,
S. Arumugam & S.K. Radhakrishnan (2026).
Impact analysis of SMS-triggered elephant activity alert lights. Journal of Threatened
Taxa 18(4): 28662–28667. https://doi.org/10.11609/jott.9750.18.4.28662-28667
Copyright: © Deb et al. 2026. Creative Commons Attribution 4.0 International License.
JoTT allows unrestricted use, reproduction, and
distribution of this article in any medium by providing adequate credit to the
author(s) and the source of publication.
Funding: We sincerely thank the Bannerghatta National Park Forest Department for their financial and logistical support in successfully carrying out the project. We also gratefully acknowledge DST-SERB (CRG/2023/005596) for funding support for technology development.
Competing interests: The authors declare no competing interests.
Author details: Dr. Sanjoy Deb is a professor and active researcher with strong contributions in R&D and technology development. As Principal Investigator, he has secured 16 projects worth over Rs. 1 crore and led more than 50 consultancy works, generating over Rs. 50 lakhs. He has filed 12 patents (including 3 granted) and commercialised around 20 products. He has also led several national and international collaborative projects. He has published over 50 journal and conference papers in reputable venues. Dr. Sannasi Chakravarthy Surulimani Ramaraj is currently working as an Associate Professor at Bannari Amman Institute of Technology, Sathy. He has 14 years of teaching and research experience, with more than 50 quality publications and 6 granted patents. His research interests include artificial intelligence, signal & image processing, embedded systems, and human–elephant conflict projects. Ms. Sharmila Arumugam is an Assistant Professor at Bannari Amman Institute of Technology, Sathyamangalam. She has 7-years of experience in teaching, with 32 publications, book chapters. Her research interests include Embedded systems, image processing, and deep learning. Dr. Saravana Kumar Radhakrishnan is an Associate Professor at VIT Chennai, Tamil Nadu, India. He has 18 years of teaching and research experience, with 40 publications, three books, and six book chapters. His research interests include VLSI, embedded systems, and human–elephant conflict projects.
Author contribution: SD: Conceptualisation, Methodology, Resources, Supervision, Project administration, Writing – original draft. SCSR: Investigation, Data curation, Field deployment, Performance analysis. SA: Methodology, Investigation, System development and implementation. SKR: Validation, Formal analysis, Writing – review & editing, Correspondence.
Acknowledgements: We sincerely thank the Bannerghatta National Park Forest Department for their financial
and logistical support in successfully executing the project.
Abstract: Human-elephant negative
interactions typically arise when elephants enter human settlements in search
of food, water, migratory routes, or other resources. Each year, accidental
encounters with elephants in areas near reserve forests result in the deaths of
hundreds of people. To address this challenge, the Bannerghatta National Park
Forest Department has implemented a solution based on a straightforward
conflict management approach, utilizing an SMS-based light and sound alert
system to notify locals of elephant activity. To help reduce the risk of such
encounters, 40 SMS-triggered elephant activity alert lights have been
strategically placed across four ranges. We have partnered as the technology
provider for this initiative. This paper presents an overview of the system’s
hardware architecture, the site selection process, the implementation strategy,
and an evaluation of its technical performance and effectiveness over an
eight-month period. This large-scale implementation of an elephant alert system
offers valuable insights into potential usage in other conflict-prone areas.
Keywords: Accidental encounters,
Bannerghatta National Park, forest department, human-elephant negative
interactions, implementation strategy, socio-economic issue, sound alert
system.
Introduction
Human-elephant negative
interaction is a significant socio-economic issue in many parts of the country,
particularly near reserve forest areas (Shameer et
al. 2024; Natarajan et al. 2025), where ongoing habitat degradation has led to
increasing elephant intrusion into settlements in search of food, water, and
other necessities. Large-scale degradation of forests and the expansion of
human settlements into traditional elephant corridors have intensified these
interactions (Wilson et al. 2015; Majumder 2022). Elephant intrusions often
lead to property damage and injuries/fatalities of humans (Talukdar et al.
2024) and elephants alike. Providing early alerts of potential elephant
activity could help to prevent such incidents and reduce the risks to both
humans and elephants (Borah et al. 2020; Ramkumar & Deb 2021; Deb et al.
2025). In most cases, information concerning elephants leaving the forest comes
from forest guards, watchers, local residents, and other spotters, many of whom
have field experience in predicting elephantmovements
(Ramkumar & Deb 2022). The challengeis to
effectively communicate this information to the those in the local community
who may be at risk of encountering elephants (Borah et al. 2020). In this
context, the SMS-triggered elephant alert lights offer a highly effective
approach to warn of local elephant activity and prevent potential conflicts.
Recognizing the need for such a
system, the Bannerghatta National Park Forest Department has launched a project
to install elephant activity alert lights (EAAL) at sensitive locations across
four ranges of the park to improve human-elephant interaction management. As
the technology partner, we have provided the design, installation, and
maintenance support for 40 such system units. These EAAL units feature a PCB
controller unit, GSM modem, battery, solar panel, light, buzzer, and other
components. In collaboration with the forest department, 40 suitable locations
were identified, and units were installed on 6 m (20 ft) iron poles for
long-range visibility. The installation was completed in March 2024, and all of
the units are now operational. This manuscript provides an overview of the project
to date.
Design
Architecture and Steps
The EAAL system hardware architecture
comprises four primary subsystems: a controller unit, a light & buzzer
unit, a power supply unit, and a communication module as shown in Figure. 1.
The controller unit is equipped with a microcontroller that stores the program
and integrates two relay switch modules to operate the light & buzzer unit.
The system is programmed to activate the light & buzzer unit for varying
durations upon receiving specific coded SMS messages, enabling short (10 min),
medium (60 min), and long-duration (6 h) alerts as needed. The system also
provides a stop code which needs to be communicated with the system to
interrupt and stop the alert instantaneously. The light & buzzer unit
consists of a 12 V waterproof module with two bright red lights and a compact
90 dB buzzer. The power supply unit features six 3 V rechargeable batteries in
a parallel-series configuration, charged by a 12 V, 5-watt solar panel,
ensuring a stable 12 V output and uninterrupted operation for 18–24 hours on a
single charge. The communication module includes a GEM modem with a SIM slot,
facilitating connectivity with the controller unit. The controller unit PCB is
uniquely designed, while the system program has been developed based on inputs
from key project stakeholders, including forest department staff and local
residents, to align with the project’s specific requirements. Figure 2 shows
different steps of EAAL hardware system design.
Implementation
Strategy
The EAAL system is designed to
alert local residents about elephant movements, allowing them to take timely
safety measures. To maximize its effectiveness, the system units are installed
in or near human settlements at the forest boundary, preferably on elevated
ground along frequently used paths. During field surveys, appropriate
installation sites are chosen, often at village entrances or exits in
high-conflict zones. The site selection process considers input from local
villagers and forest officials. During installation, the system’s functionality
is thoroughly explained and demonstrated to the community. The system is
triggered when a
specific SMS code is received from any sender. As such, the contact number for
the system and the activation code are shared with the designated forest guard
and a few authorized local individuals. Each designated range is equipped with
10 system units. To ensure quick and direct communication regarding
system-related issues, four WhatsApp groups, specific to each range, have been
established, involving rangers, forest guards, and local community members.
Pictures of system installation in collaboration with the forest department
team and local residents are given in the Figure 3.
System
Performance & Result Analysis
The performance of the EAAL
system is significantly impacted by the strength of the mobile network and the
positioning of the units for optimal long-range visibility. While some sites
are strategically located for visibility from human settlements and elephant
intrusion paths, they may suffer from weak mobile network coverage. Based on
the current analysis, the network strength parameter is categorized according
to call connectivity percentage: call connectivity of 80% or above is
considered a strong network, 50–80 % connectivity is classified as a moderate
network, and connectivity below 50% is regarded as a weak network. Therefore,
choosing the best installation site requires balancing location suitability
with network strength, necessitating careful optimization. To understand how
this factor influences system performance, an evaluation of each unit’s
location in relation to mobile network strength has been conducted for all
installed units, as shown in Table 1. The table indicates that the Bannerghatta
Range, being near a major city, benefits from excellent mobile network
coverage, with only one unit located in a low-network area. In contrast, Kodahalli, located at the interstate border, faces poorer
network performance, with seven units in low-network zones. Harohalli
and Anekal rank second and third, respectively, in
terms of network weakness. However, system locations on elevated or open
terrain offer superior long-range visibility and stronger mobile network
connections, making them ideal for such light-based alert systems. Although Kodahalli has weak network coverage, its remote and
sparsely populated nature makes it suitable for more system installations. On
the other hand, Bannerghatta, which is densely populated, has most of its
system units within the village itself. The other two ranges, Harohalli and Anekal, present
intermediate conditions for system installation suitability.
As a real-time system, response
time is crucial in determining performance, along with the number of failed
triggering. Thanks to a strong mobile network, the Bannerghatta system units
have an average response time delay of seven seconds, with three failed
triggering, as shown in Figure 4. In contrast, the Kodahalli
units, impacted by slow mobile connectivity, experience a higher average
response time delay of 20 seconds, with seven failed triggering per unit. The
other two ranges, Harohalli and Anekal,
exhibit intermediate performance, with Harohalli
ranking second and Anekal in third place.
Over the eight-month period of
April–December 2024, the system’s performance was analyzed based on the number
of systems triggering via SMS, the number of units experiencing network
failures, and the number of malfunctions, as presented in Table 2. Due to the
quality of the network, the Bannerghatta system units were triggered more
frequently, as shown in the table, compared to the other ranges. The data on triggerings, failed attempts, and units going out of
network, was collected from forest officials assigned to each unit and updated
on an ongoing basis through a WhatsApp group. Less than 20% of the system units
malfunctioned during this phase, and these issues were primarily due to weather
conditions and other factors, rather than circuit failures.
Although there are only four
circuit failures, these circuits have been thoroughly investigated, and it has
been determined that the 5 V relay switch is causing momentary high power consumption from the 5 V regulator during
switching, leading to regulator damage due to overheating. As a result, the
next generation of the EAAL system will be designed with a 12 V relay, powered
directly from the battery, bypassing the regulator. To address the mobile
network failure issue, the simplest solution is to restart the system to
restore the network connection. Therefore, in the first phase of system
maintenance, the units will be equipped with external hanging switches for easy
restarts, which has significantly reduced the occurrence of systems going out
of network.
During the installation and
maintenance phases of the system, a survey was conducted to evaluate its impact
on the local community, asking the simple question: “Do you think this system
is useful and will have an impact?” The sample sizes for Bannerghatta, Harohalli, Kodahalli, and Anekal were 100, 60, 50, and 30, respectively. The
responses are shown in Figure 4 and Figure 5. Residents of Bannerghatta, a more
developed and technology-friendly area, had a clear understanding of the
system’s benefits and gave positive feedback. However, in Harohalli
and the other ranges, the responses were more mixed. This was due to a lack of
technical understanding of the system and a preference for a deterrent system
rather than an elephant alert system.
Conclusion
This research provides a
comprehensive overview of a simple yet effective method for managing
human-elephant conflict through a large-scale pilot project. The main goal is
to alert local communities about elephant activity using an SMS-operated light
and sound alarm system called ‘Elephant Activity Alert Lights,’ allowing them
to take safety precautions. A total of 40 units of this system have been
installed across four ranges of Bannerghatta National Park as part of a forest
department initiative, with our technical support. The system is uniquely
designed, and this paper outlines its architecture, design process, algorithm, and
operation in detail. For the system to be effective, two critical
factors—mobile network availability and long-distance visibility—must be
considered. This paper includes detailed tables and graphs showing how these
factors influence the system’s performance and suggests possible ways to
optimize it. The system’s performance over an eight-month period is analyzed,
including the number of times the system was triggered via SMS, the number of
units experiencing network failures, and the frequency of malfunctions. These
findings are presented with accompanying graphs and tables. Additionally, some
technical issues observed during this period are discussed, and potential
solutions for correcting them are identified. Local feedback on the system’s
usefulness on the ground is also gathered and presented. Given the significant
socioeconomic and dangerous impact of human-elephant conflicts in many parts of
India, this project, as detailed in this paper, will serve as a valuable
reference for implementing similar solutions in other conflict-prone areas.
Table 1. Range-wise numbers of
unit with respect to network strength and suitable
location.
|
|
Range |
Numbers of unit w.r.t network
strength |
Numbers of units w.r.t suitable
location |
||||
|
Low network |
Moderate
network |
Standard
network |
Village
entry/exit |
Village
inside |
High-land/
open area |
||
|
1. |
Harohalli |
3 |
5 |
2 |
4 |
3 |
3 |
|
2. |
Bannerghatta |
1 |
3 |
6 |
2 |
7 |
1 |
|
3. |
Kodahalli |
7 |
2 |
1 |
2 |
2 |
6 |
|
4. |
Anekal |
5 |
3 |
2 |
3 |
2 |
5 |
Table 2. Range-wise system performance analysis.
|
|
Range |
Number of system trigger |
Number of units out of range in days |
Number of units suffered malfunction |
|||
|
+50 |
+100 |
+150 |
Count |
Cause of malfunction |
|||
|
1. |
Harohalli |
10+ |
2 |
3 |
0 |
3 |
Circuit damaged (1 no) ant
nests (1 no), water infiltration (1 no). |
|
2. |
Bannerghatta |
30+ |
2 |
0 |
0 |
1 |
Light damaged due to water (1
no). |
|
3. |
Kodahalli |
5+ |
1 |
4 |
6 |
4 |
Circuit damaged (2 no), a tree
branch fell (1 no), and the pole was knocked down by strong winds (1 no). |
|
4. |
Anekal |
7+ |
2 |
4 |
4 |
2 |
Circuit damaged
(1 no), water infiltration (1 no). |
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