| Journal of Smart Sensors and Computing
Received: 25 June 2025; Revised: 10 September 2025; Accepted: 23 September 2025; Published Online: 26 September 2025.
J. Smart Sens. Comput., 2025, 1(2), 25211 | Volume 2 Issue 2 (September 2025) | DOI: https://doi.org/10.64189/ssc.25211
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
Design and Evaluation of a Real-Time IoT-Enabled Zoo
Navigation and Surveillance System
Ganesh Pise,
1,*
Ayush Kale,
2
Arnav Kale,
2
Khushi Kale,
2
Arya Kale
2
and Aayush Kalamkar
2
1
Department of Information Technology, Vishwakarma Institute of Technology, Vishwakarma Institute of Technology, Pune,
Maharashtra, 411037, India
2
Department of Engineering, Sciences and Humanities (DESH), Vishwakarma Institute of Technology, Pune, Maharashtra, 411037,
India
*Email: ganeshpise143@gmail.com (Ganesh Pise)
Abstract
Zoos often present complex layouts that make navigation challenging for visitors, leading to missed exhibits and
reduced engagement. This study proposes a real-time, IoT-enabled navigation and surveillance system designed
for zoological parks, with implementation at the Rajiv Gandhi Zoological Park (Katraj Zoo), Pune. The system
integrates GPS data, OpenStreetMap (OSM), and Leaflet.js to dynamically map visitor positions and generate
optimal walking paths to animal enclosures. Each enclosure is geotagged and selectable via a user interface,
enabling real-time route plotting and recalculation based on location updates. Unlike conventional navigation
platforms, the proposed system incorporates informal pathways, thereby improving spatial accuracy and
usability. Field testing demonstrated improved visitor orientation and a 3040% reduction in travel time
between exhibits, along with higher user satisfaction. The approach is scalable and adaptable to other
environments such as national parks, wildlife reserves, and botanical gardens.
Keywords: Zoo navigation; Real-time location; IoT; Shortest path; Geospatial visualization.
1. Introduction
2. Literature review
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3. Methodology
The software architecture consists of three layers: the frontend interface developed using HTML, CSS, JavaScript,
and Leaflet.js; a backend server built using Flask or Node.js to handle API requests; and the data layer, which
includes a manually defined graph structure representing walkable paths within the zoo. Fig. 2 graph includes
both formal paved routes and informal but frequently used trails such as dirt or red-dotted paths, which are
identified using site surveys and satellite imagery. Each node in the graph represents an animal enclosure,
intersection, or key point, and edges denote navigable paths with associated distances. The edge weights are
calculated using the Haversine formula to ensure accuracy in real-world distances. The application's navigation
uses the A* search algorithm to find the shortest path from the user to an animal's enclosure. It combines actual
distance with a heuristic estimate to quickly find an efficient route. The route is displayed on a Leaflet map as a
series of coordinates, and the user is guided with animated markers and labels. Fig. 3 represent steps the visitor
navigation and animal location system.
Fig. 1: Proposed animal location tracking system.
Fig. 2: Snapshot of the output from the proposed real-time zoo navigation and animal tracking system.
Fig. 3: Visitor navigation and animal location system.
The system features an alphabetical dropdown menu for all animal enclosures, which automatically zooms the
map and highlights the optimal path upon selection. Enclosures are marked with clear, labeled icons for
readability. The system uses real-time location updates to recalculate the route if the user deviates from the
path. Fig. 4 shows the shortest path computation and live navigation process ensuring accurate navigation.
This multi-modal approach, combining IoT tracking, web mapping, and graph-based navigation, forms a robust
and scalable solution suitable for any large open-space environment like zoos, parks, or campuses. The
methodology ensures that users can intuitively find their way while also enabling authorities to manage foot
traffic more effectively. Fig. 5 shows layered system architecture of the proposed IoT-enabled zoo navigation
framework.
Fig. 4: Shortest path computation and live navigation process.
Fig. 5: System architecture of the proposed IoT-enabled zoo navigation framework.
4. Results and discussion
The proposed real-time zoo navigation system was implemented and tested at a simulated layout of the Rajiv
Gandhi Zoological Park (Katraj Zoo) using both live GPS data and mock location inputs. The system was
deployed in a browser-based environment and accessed via mobile and desktop platforms. Additionally,
prototype IoT hardware, built using an ESP32 microcontroller and NEO-6M GPS module, was tested to validate
the performance of hardware-based tracking under various conditions within the zoo's geography.
The real-time location of the user was accurately captured using the browser's Geolocation API in most
smartphone devices with an average positional error of approximately 58 meters shown in Table 1. In
contrast, the IoT-based GPS module demonstrated a slightly improved accuracy (35 meters) Table 2 in open
areas but was prone to signal loss under dense foliage or near animal enclosures built with overhead shelters.
This observation suggests that a hybrid location tracking strategy combining browser-based geolocation with
optional IoT support offers a balanced solution for diverse user groups.
Table 1: Comparison of tracking methods.
Tracking
Accuracy
Limitations
Browser Geo API
5-8
Slight error in dense areas
IoT GPS Module
3-5
Signal loss under
foliage/shelters
Table 2: Visitor navigation efficiency.
Parameter
Without
System
With Proposed
System
Improvement
(%)
Avg. Time to Reach
Encloser
12 min
8 min
33%
No. of Missed Encloser
3
0
100%
Avg. User Satisfaction
Score (1-5)
3.1
4.5
45%
Fig. 6 represent the A* algorithm, applied to a
pre-defined graph representing zoo walkways (including both paved and dotted red paths), consistently
provided the most efficient routes. Tests with different origin-destination pairs showed that the system
responded with optimal paths in under 100 milliseconds, which is satisfactory for real-time applications shown
in Fig. 7. Visualizations rendered using Leaflet.js offered clear and intuitive path overlays. Paths were marked
with smooth polylines, and labelled markers for animal enclosures provided additional clarity. The interface
allowed users to select any animal from a dropdown, upon which the system smoothly zoomed into the relevant
region and highlighted the route. Feedback from test users emphasized the system's ease of use, particularly
for first-time visitors unfamiliar with the zoo's layout. One key result from testing was the improved visitor
orientation and reduced time spent searching for specific enclosures. Informal observations indicated that
users using the navigation system reached enclosures on average 3040% faster compared to unassisted
navigation. Additionally, the ability to identify alternate, less congested paths helped reduce crowding in central
pathways. A limitation encountered during testing was the challenge of maintaining consistent GPS accuracy in
forested or sheltered zones. This occasionally led to off-path recalculations. Future improvements may include
integration with Bluetooth beacons or Wi-Fi-based indoor localization to address this shortcoming.
Fig. 6: Graphical representation of response time across different enclosures.
Fig. 7: Graphical representation of comparison of average visitor navigation time.
Overall, the results confirm that the integration of IoT-based location tracking, graph-based routing, and
interactive map interfaces can significantly improve user experience and operational management in zoological
parks. The system shows promise for broader deployment in similar environments such as botanical gardens,
heritage campuses, and large amusement parks. In Fig. 8, we have used GPS NEO-6 M module connection with
Arduino UNO for real-time position in Katraj Zoo Pune.
Fig. 8: GPS NEO-6M module connection with Arduino UNO for real-time position.
5. Conclusion and future scope
This work presents a scalable, cost-effective IoT-enabled navigation system for zoological parks that enhances
visitor experience through real-time GPS tracking, OpenStreetMap geodata, and Leaflet.js visualization,
addressing the limitations of static maps and generic navigation apps. Field-tested at Katraj Zoo, the system
proved its practicality by improving navigation accuracy and user satisfaction, even on informal or unmarked
paths. Future improvements include integrating IoT-based GPS or RFID modules for real-time animal tracking,
adaptive routing considering terrain and crowd density, multilingual voice assistance, augmented reality
features, and a dynamic admin dashboard for zoo authorities. The modular design also supports extensions to
educational content, live event updates, and interactive quizzes, transforming visits into immersive learning
experiences. Beyond zoos, the framework can be adapted for national parks, campuses, botanical gardens, and
archaeological sites, contributing to smart, sustai
informal or unmarked zoo paths often not included in standard mapping tools significantly enhances the
accuracy and utility of the app in the zoo context. Additionally, the low hardware footprint and browser-based
deployment model make it feasible for adoption in resource-constrained environments. Beyond zoological
environment, the proposed system architecture and logic are highly adaptable for wide range of smart tourism
and spatial management applications. This can be effectively used National parks with wildlife trails to guide
visitor to guide visitor along optimized and safe route. Also, this can be used in large campus such as university
or corporate to felicitate outdoor navigation. This system can be implemented to enhance visitor experience in
botanical gardens, eco-tourism resorts, and archaeological sites featuring multiple open-air exhibits. This
research lays the foundation for further innovation in location-aware systems for education, recreation, and
sustainable tourism.
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Data Availability Statement
The data supporting the findings of this study, including geospatial coordinates, route mapping data, and system
performance evaluation results, are available from the corresponding author upon reasonable request. Some
data are subject to institutional or site-specific restrictions related to Rajiv Gandhi Zoological Park and therefore
are not publicly available.
Conflict of Interest
There is no conflict of interest.
Artificial Intelligence (AI) Use Disclosure
The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement,
grammar improvement, and manuscript structuring purposes during the preparation of this work. All technical
content, experimental implementation, results, and interpretations were independently developed and verified
by the authors.
Supporting Information
Not applicable.
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