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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
Mallpath-Indoor Navigation System
Haripriya S1, Devadharshini T2, Aarthi J2, Atchaya R2, Jayapriya N2
1Assistant Professor, IT, Hindusthan Institute of Technology, Coimbatore
2Student, Second year IT, Hindusthan Institute of Technology, Coimbatore
DOI: https://doi.org/10.51583/IJLTEMAS.2026.150500035
Received: 26 April 2026; Accepted: 01 May 2026; Published: 26 May 2026
ABSTRACT
Indoor navigation within large shopping malls is often challenging due to complex layouts and lack of proper
guidance systems. Visitors frequently struggle to locate stores, restrooms, exits, or other facilities efficiently.
Traditional navigation methods such as static maps and signboards are often insufficient and confusing.
This paper proposes MallPath, an intelligent indoor navigation system designed to assist users in navigating
malls and accurately. The system utilizes digital mall maps, real-time location tracking, and pathfinding
algorithms to guide users to their desired destinations.
The proposed system provides floor-wise navigation, shop categorization, and shortest path suggestions. It
enhances user experience by reducing time, improving accessibility, and offering an interactive interface. The
solution is scalable and can be implemented in modern smart malls.
Keywords: Indoor Navigation, Smart Mall, Pathfinding, User Interface, Location Tracking, Digital Mapping.
INTRODUCTION
Navigation inside large shopping malls can be confusing due to multiple floors, numerous stores, and complex
layouts. Visitors often rely on signboards or security assistance, which is time-consuming and inefficient.
Traditional methods such as static maps and help desks do not provide clear and quick directions. Many users
find it difficult to identify their current location and reach their destination easily, especially during busy hours.
With the advancement of digital technologies, indoor navigation systems have become essential. Unlike
outdoor navigation (GPS-based), indoor environments require specialized systems due to signal limitations.
Therefore, smart solutions using digital maps and algorithms are needed.
This paper introduces MallPath, a smart indoor navigation system that helps users:
Locate shops easily
Navigate floor-wise layouts
Find shortest paths
Search shops by category
Save time and reduce confusion
Improve overall shopping experience
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
The system provides an interactive and user-friendly interface that guides users step-by-step inside the mall. It
can be used by both customers and mall management for better navigation support.
The system ensures efficient navigation through structured data, clear directions, and easy accessibility,
making mall visits more convenient and organized..
LITERATURE REVIEW
Indoor navigation systems have gained significant importance due to the increasing size and complexity of
modern shopping malls. Various technologies and approaches have been developed to improve indoor
navigation and user experience.
Several systems use technologies such as:
Bluetooth beacons
Wi-Fi positioning
QR code-based navigation
RFID systems
Augmented Reality (AR)
Bluetooth beacon-based systems provide location tracking by detecting nearby devices, but they require
additional hardware installation and maintenance. Wi-Fi-based systems are widely used, but they often suffer
from low accuracy and signal interference inside buildings.
QR code-based navigation systems are simple and cost-effective, where users scan codes placed at different
locations to get directions. However, they require user interaction at multiple points, which may not be
convenient.
Augmented Reality (AR) systems provide advanced navigation by overlaying directions on real-world views,
but they are complex and require high processing power and modern devices.
Most existing systems use digital maps combined with pathfinding algorithms such as:
Dijkstra’s Algorithm
A* (A-Star) Algorithm
These algorithms help in finding the shortest path between two locations efficiently.
However, existing systems have several limitations such as:
High implementation cost
Dependence on additional hardware
Inaccurate indoor positioning
Complex user interfaces
Lack of real-time updates
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The proposed MallPath system focuses on a simple and efficient map-based navigation approach. It uses
structured mall data and pathfinding algorithms to provide accurate directions without requiring expensive
hardware.
System Specifications
Software Requirements
The system is developed using modern technologies to ensure scalability and performance:
Operating System: Windows / Android / iOS
Frontend: HTML, CSS, JavaScript
Backend: Python / Node.js
Database: MongoDB / Firebas
Software Description
The system uses web technologies for interface design and backend processing. The database stores mall
details such as:
Shop names
Floor information
Categories
The system processes user input and generates navigation paths dynamically.
System Analysis
Existing System
Current systems include:
Static mall maps
Information kiosks
Manual guidance
Limitations:
Not interactive
Time-consuming
Difficult to understand
Proposed System
MallPath provides:
Digital interactive maps
Floor-wise navigation
Shortest path calculation
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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Shop search functionality
Feasibility Study
Technical Feasibility
Uses simple web technologies and algorithms.
Economic Feasibility
Low cost compared to beacon-based systems.
Operational Feasibility
Easy to use for all users.
System Design
Architecture
The system consists of the following modules:
1. User Interface Module
2. Admin Module
3. Navigation Module
4. Database Module
5. Map Management Module
6. Pathfinding Module
7. Search Module
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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Architecture
Process Flow Architecture
Process Flow Architecture
Input Design
Inputs include:
Mall selection
Shop name
Category
Input Design
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Input Design
Output Design
Outputs include:
Navigation path
Floor details
Shop location
Output Design
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METHODOLOGY
The system follows a structured methodology
Data Collection
Mall data including shops and layouts are collected.
Preprocessing
Data is organized floor-wise.
Pathfinding Algorithm
Shortest path is calculated using algorithms like:
Dijkstra
A*
Navigation Display
Path is visually shown to the user
Implementation and Testing
Implementation
Frontend for user interface
Backend for processing navigation
Testing
Unit Testing
Integration Testing
User Testing
RESULTS AND DISCUSSION
The system successfully provides:
Accurate navigation
Faster shop location
Improved user experience
It reduces confusion and saves time for users.
Future Enhancements
Real-time indoor tracking
Voice navigation
AR-based guidance
Multi-language support
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026
CONCLUSION
MallPath is an efficient indoor navigation system that simplifies mall navigation. It provides a user-friendly
interface, accurate directions, and improves overall shopping experience. The system is scalable and can be
implemented in smart malls.
ACKNOWLEDGMENT
We sincerely thank our project guide Mrs. S. Haripriya, M.E., (Ph.D) for her valuable guidance and support
throughout the development of this project. Her mentorship played a vital role in the successful completion of
this work.
REFERENCES
1. Z. Ayaz, “Digital Advertising Using BLE Beacons,” IEEE, 2025.
2. L. Bouska et al., “Indoor Localization Using BLE and RSSI,” IEEE, 2024.
3. M. C. Sakman et al., “Indoor Navigation for Personalized Shopping,” ScienceDirect, 2023.
4. A. Saeidi et al., “AR-Based Indoor Navigation in Shopping Malls,” IEEE, 2022.
5. N. Narayana et al., “Smart Indoor Navigation Using BLE,” IEEE, 2021.
6. P. Pešlácio et al., “BLE Indoor Positioning Using Neural Networks,” IEEE, 2021.
