Guided Navigation Using Embedded System

International Journal of Emerging Trends in Engineering and Development Issue 15, Vol.6, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.18014749 Original Article @2025 RS Publication, [email protected] 413 Guided Navigation Using Embedded System Arpitha Vasudev Assistant professor, Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, INDIA,  [email protected] Mohammed Adnaan Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, INDIA, [email protected] Malla Ashish Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, INDIA, [email protected] Madhu HR Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, INDIA,  [email protected] Uthkarsh M Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, INDIA [email protected] International Journal of Emerging Trends in Engineering and Development Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249 - 6149 ARTICLE INFO ABSTRACT ©2025 RS Publication Paper ID: IJETED6947A0C6CE8C4 Published: 2025-12-22 DOI: https://dx.doi.org/1 0.5281/zenodo.180147 49 Page No: 413-423 The Next-Generation Exploration (NGE) project aims to design an intelligent indoor–outdoor navigation system tailored for university and college campuses. Traditional GPS systems perform poorly indoors and lack the precision needed for complex building layouts. To address this gap, the NGE system integrates ESP32 modules functioning as Bluetooth/Wi-Fi beacons to create a reliable, low-cost, and scalable positioning infrastructure. These beacons act as checkpoints distributed across campus, allowing users to receive accurate navigation guidance through a mobile application without depending solely on GPS signals. The proposed system captures real-time proximity data from ESP32 beacons and processes it to determine the user’s location with improved reliability. Using these coordinates, the mobile application generates the shortest path to the desired destination and presents it through an intuitive AR-based interface. This AR module displays directional arrows and route overlays similar to simulation and gaming environments, significantly enhancing user experience and reducing confusion in complex campus layouts. Furthermore, the system leverages existing campus Wi-Fi networks where possible, enabling extended range and reducing the number of physical beacons required. By combining low-power embedded hardware, wireless communication, and mobile AR technologies, the NGE project provides a modern, accessible solution for campus navigation challenges. The system is cost-effective, easy to deploy, and scalable across institutions of varying sizes. Its potential applications extend beyond student navigation to include visitor guidance, emergency routing, and infrastructure management. Ultimately, NGE demonstrates how embedded systems can transform spatial awareness in educational environments, offering a robust foundation for future smart-campus initiatives. General Terms Embedded Systems, Wireless Communication, Indoor Navigation, Augmented Reality, Localization, Mobile Computing. Keywords ESP32, Bluetooth Low Energy (BLE), AR Navigation, Campus Guidance System, RSSI Localization, Route Optimization, Embedded Beacons. Cite This Paper: Arpitha Vasudev, Mohammed Adnaan, Malla Ashish, Madhu HR and Uthkarsh M (2025). "Guided Navigation Using Embedded System". INTERNATIONAL JOURNAL OF EMERGING TRENDS IN ENGINEERING AND DEVELOPMENT (IJETED), vol. 15, no. 6, 2025, pp. 413-423. DOI: https://dx.doi.org/10.5281/zenodo.18014749 International Journal of Emerging Trends in Engineering and Development Issue 15, Vol.6, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.18014749 Original Article @2025 RS Publication, [email protected] 414 I. INTRODUCTION 1.1. Background Modern university campuses continue to expand in both physical size and structural complexity, creating environments where students frequently move between multiple buildings, laboratories, and administrative blocks. Traditional campus maps and signboards provide only static information and require manual interpretation, making them inefficient during time-sensitive transitions such as class changes or examinations. Digital navigation tools, though widely available, remain limited indoors because GPS signals degrade significantly within closed architectural spaces 1.2. Need for Indoor/Outdoor Micro-Navigation Micro-navigation refers to guiding users through fine-grained routes such as corridors, staircases, intersections, and departmental lanes—areas where conventional maps fail to provide real-time precision. Campuses require micro-navigation for new students, visitors, event participants, and emergency situations. A unified system capable of assisting both indoor and semi-outdoor movement can greatly improve accessibility, reduce confusion, and streamline campus activities. 1.3. Challenges in College Navigation Navigation in academic institutions is influenced by multiple constraints: Frequent room reassignments during exams, seminars, and practical sessions; Multi-floor structures that complicate vertical navigation; Inconsistent signage and non-standard building layouts; Poor indoor GPS accuracy, rendering commercial navigation ineffective; High deployment costs associated with proprietary indoor-positioning systems. These issues collectively justify the need for a flexible, low-cost embedded navigation infrastructure. 1.4. Motivation for Using ESP32 + AR The ESP32 platform offers Wi-Fi and Bluetooth Low Energy (BLE) connectivity, low power consumption, and affordability, making it suitable for use as a campus-wide beacon network. BLE beaconing provides stable indoor coverage and requires minimal maintenance. Meanwhile, Augmented Reality (AR) on smartphones enables intuitive, visually guided navigation through arrow overlays, reducing user cognitive load and providing game-like, immersive directional cues. Combining ESP32 beacons with AR results in a modern, accessible, and cost-efficient navigation solution. 1.5. Problem Statement Existing navigation tools fail to accurately guide users inside campus buildings and connecting corridors due to the absence of reliable indoor positioning sources. There is a need for a scalable, embedded-systembased navigation framework that identifies user position, computes optimal routes, and provides real-time, easy-to-follow AR visual guidance within university environments. 1.6. Objectives of the Study This research aims to: Develop a BLE-based beacon network using ESP32 modules for micro-level campus localization. Design an Android navigation application incorporating AR directional guidance. Create a hybrid localization model using RSSI proximity mapping for improved indoor performance. Implement an efficient path computation algorithm for dynamic route generation. Experimentally validate system accuracy, latency, and usability within real campus conditions. 1.7. Research Contribution Summary The study introduces a low-cost, deployable guided navigation system for educational campuses, International Journal of Emerging Trends in Engineering and Development Issue 15, Vol.6, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.18014749 Original Article @2025 RS Publication, [email protected] 415 integrating embedded hardware with AR-based mobile interfaces. Key contributions include: A custom beacon deployment strategy optimized for university layouts; A hybrid RSSI-based localization technique; A real-time AR navigation module for enhanced user experience; Experimental performance evaluation across varied indoor environments. The system demonstrates the feasibility of replacing expensive commercial indoor-navigation solutions with accessible embedded technologies. II. LITERATURE REVIEW Indoor navigation has been an active area of research due to the limitations of GPS in enclosed environments. Several technologies such as Bluetooth Low Energy (BLE), Wi-Fi fingerprinting, RadioFrequency Identification (RFID), Ultra-Wideband (UWB), and vision-based systems have been explored to provide reliable positioning, each offering distinct advantages and constraints. This section reviews existing approaches and identifies gaps that motivate the proposed guided navigation solution using ESP32-based beacons and augmented reality (AR). 2.1. BLE-Based Indoor Navigation Systems BLE beacons have gained popularity due to their low energy consumption, ease of deployment, and compatibility with consumer smartphones. Systems such as iBeacon and Eddystone frameworks demonstrate the feasibility of broadcasting unique identifiers that mobile applications can interpret for proximity estimation. Research shows that RSSI measurements can approximate user distance, though accuracy fluctuates due to multipath interference and environmental noise. Existing BLE-based solutions often require dense beacon placement or advanced filtering techniques to achieve stable localization, increasing deployment complexity. Despite these limitations, BLE remains one of the most cost-effective solutions for indoor positioning, especially in large institutions. 2.2.Wi-Fi Fingerprinting and Hybrid Localization Wi-Fi fingerprinting techniques create signal-strength maps of indoor spaces and match real-time device measurements against these fingerprints to estimate position. Although effective in many use cases, this method is labor-intensive due to the need for periodic recalibration as signal landscapes change. Hybrid approaches combining Wi-Fi and BLE have been explored to mitigate the weaknesses of each technology; however, these systems often depend on existing Wi-Fi infrastructure with unpredictable access point density and orientation. For campus environments where router placement varies significantly, pure WiFi methods may not guarantee reliable fine-grained navigation. 2.3. RFID, UWB, and Vision-Based Systems RFID-based systems offer robust object tracking but require dedicated readers, tags, and line-of-sight interactions, making them less practical for human navigation in open public spaces. UWB provides high localization accuracy but suffers from high deployment costs and increased hardware complexity, limiting its feasibility for educational institutions seeking scalable solutions. Vision-based systems using SLAM (Simultaneous Localization and Mapping) leverage camera sensors to build spatial maps; however, they impose heavy computational loads on mobile devices and are sensitive to lighting variations. These constraints reduce their practicality for continuous, campus-wide usage. 2.4. AR Navigation Technologies Augmented Reality has recently gained traction as a tool for enhancing navigation clarity by overlaying 3D directional cues onto real environments. AR toolkits such as ARCore and ARKit provide device tracking, plane detection, and rendering capabilities that allow developers to build immersive navigation experiences. Prior work demonstrates that AR improves user comprehension and reduces mental effort compared to reading static maps. Yet, most AR-based navigation solutions rely on pre-built indoor maps, International Journal of Emerging Trends in Engineering and Development Issue 15, Vol.6, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.18014749 Original Article @2025 RS Publication, [email protected] 416 LiDAR data, or high-precision localization systems, which are not readily accessible in typical academic campuses. This gap motivates the integration of AR with a lightweight, embedded beacon infrastructure. 2.5. Identified Research Gaps From the reviewed literature, the following gaps emerge: Many existing systems rely on expensive or proprietary hardware. Wi-Fi and BLE hybrid systems are limited by unpredictable infrastructure layouts. Vision-based localization requires significant processing power, unsuitable for all user devices. AR navigation frameworks often depend on precise localization sources not available in standard campus environments. These gaps highlight the need for a low-cost, scalable, embedded-system-driven navigation architecture that can function effectively within the constraints of university campuses. The proposed system addresses these limitations by combining ESP32 BLE beacons with an AR-enabled mobile application, offering a lightweight yet efficient navigation experience. III. RESEARCH METHODOLOGY The research methodology outlines the systematic approach adopted to design, implement, and evaluate the proposed guided navigation system. It integrates embedded hardware deployment, wireless communication strategies, mobile application development, and AR-based user experience design. The methodology is divided into hardware, software, and experimental layers to ensure clarity and replicability. 3.1. System Architecture The overall system architecture is composed of three primary modules: Beacon Layer (ESP32 Nodes): Each ESP32 module is programmed to operate as a BLE beacon, broadcasting unique identifiers at fixed intervals. Mobile Application Layer: An Android-based application scans for nearby beacons, interprets RSSI values, determines user position, computes optimal routes, and provides AR navigation overlays. Backend Data Model: A lightweight campus map database stores node coordinates, building layouts, and graph connections linking pathways, corridors, and intersection points. The architecture follows a distributed model, where beacons require no intercommunication, and all computations occur on the mobile device to reduce back-end dependency. 3.2. Hardware Methodology 3.2.1 ESP32 Configuration Each ESP32 DevModule is configured to function as a BLE beacon using manufacturer-specific advertising packets. Transmission power, advertisement frequency, and channel parameters are calibrated to ensure stable signal patterns across typical indoor distances. 3.2.2 Beacon Placement Strategy Beacons are deployed at strategic points such as corridor intersections, staircases, block entrances, and long hallways. Placement is determined based on: Signal propagation patterns observed during preliminary testing Structural geometry of the campus Minimizing overlap while ensuring coverage Nodes are mounted at consistent heights to reduce multipath interference and maintain standardization. 3.2.3 Power and Maintenance Considerations The ESP32 units are powered either via USB modules or rechargeable cells depending on location. A lowpower mode is enabled during idle periods to extend overall operational life. International Journal of Emerging Trends in Engineering and Development Issue 15, Vol.6, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.18014749 Original Article @2025 RS Publication, [email protected] 417 3.3. Software Methodology 3.3.1 Mobile Application Workflow The Android application performs the following operations: BLE scanning and identification of nearby beacons RSSI smoothing using a moving-average filter Localization by mapping beacon signal strength to predefined zones Path computation using graph-based algorithms Real-time AR arrow rendering for directional feedback The application is developed using Android Studio, integrating ARCore for motion tracking and scene rendering. 3.3.2 Navigation Algorithm A directed graph is constructed from the campus layout, where each node represents a spatial point and edges denote walkable paths. The Dijkstra algorithm is used to compute shortest routes based on node distances. The system also supports dynamic recomputation when a user deviates from the intended path. 3.3.3 AR Rendering Pipeline The AR module uses the smartphone’s camera feed and inertial sensors to anchor virtual arrows to the environment. The rendering pipeline includes: Plane detection and orientation estimation Arrow placement relative to the user’s forward direction Smooth animation transitions at route junctions 3.4. Data Flow Model The data processing sequence follows these stages: Signal Acquisition: Mobile device receives BLE advertisement packets. Filtering and Smoothing: RSSI values are processed to reduce noise and sudden fluctuations. Position Estimation: The application infers the user’s current zone and nearest navigation node. Route Generation: A shortest path from the current node to the destination is computed. AR Guidance Output: Visual cues are rendered, and real-time feedback is provided until arrival. This modular data pipeline ensures low latency and supports on-device processing without reliance on external servers. The operational sequence followed by the system is summarized in Fig. 1. Fig 1 International Journal of Emerging Trends in Engineering and Development Issue 15, Vol.6, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.18014749 Original Article @2025 RS Publication, [email protected] 418 E. Experimental Setup 3.4.1 Deployment Environment Testing was conducted in selected corridors and blocks of a university campus representing typical indoor navigation challenges such as long hallways, multi-direction intersections, and signal obstructions. 3.4.2 Number of Nodes and Layout A prototype consisting of multiple ESP32 beacons was deployed across key checkpoints. Beacon distances varied from 8–15 meters based on environmental constraints and signal behaviour. 3.4.3 Test Scenarios Experiments were designed to evaluate: RSSI stability in open and obstructed areas Localization accuracy near intersections AR rendering consistency during user movement Route recalculation responsiveness Repeated trials were performed to ensure consistency under diverse conditions, including varying user speeds and device orientations. IV. PROPOSED SYSTEM DESIGN Fig. 2 illustrates the overall architecture of the proposed guided navigation system. The design integrates ESP32-based BLE beacons, an Android mobile application, and an AR navigation module to provide realtime indoor guidance without reliance on GPS. Fig 2 The proposed guided navigation system integrates embedded BLE beacons, a mobile-based localization engine, and an AR navigation interface to deliver real-time directional guidance within university campuses. The design focuses on reducing deployment cost, improving indoor accuracy, and providing intuitive visual cues to end users. This section details the system components and their interactions. 4.1. Beacon Deployment Model The beacon deployment model is structured around strategic placement of ESP32 nodes to maximize signal coverage while minimizing interference. Each beacon is configured to broadcast a unique identifier corresponding to a predefined coordinate in the campus map database. Deployment follows three principles: Coverage Optimization: Beacons are installed at decision points—such as junctions, staircases, and building entrances—where route guidance is essential. Distances between beacons are maintained in the range of 8–15 meters to ensure consistent BLE visibility. Structural Adaptation: Placement considers walls, metallic surfaces, and reflective materials. Line-of-sight International Journal of Emerging Trends in Engineering and Development Issue 15, Vol.6, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.18014749 Original Article @2025 RS Publication, [email protected] 419 paths are prioritized whenever possible to reduce RSSI distortion. Redundancy Minimization: Overlapping beacon zones are minimized to simplify localization logic and prevent ambiguous signal readings. This model ensures reliable navigation without requiring dense or costly infrastructure. 4.2. Localization Technique The localization mechanism employs a hybrid RSSI-based proximity estimation approach: RSSI Smoothing: Raw RSSI data is filtered using a moving-average algorithm to mitigate fluctuations caused by user movement and multipath effects. Zone-Based Mapping: Instead of precise trilateration—which is highly sensitive indoors—the system categorizes the user into a logical zone based on the strongest beacon and signal thresholds. Node Association: Each zone is mapped to a navigation node in the campus graph. Transitions between zones trigger recalculation of the user’s relative position. This simplified technique reduces computational complexity while maintaining reliable accuracy for path guidance. 4.3. Route Computation The navigation graph is constructed from campus blueprint data, where nodes represent spatial checkpoints and edges represent navigable paths. For any selected destination: Pathfinding Algorithm: The Dijkstra algorithm computes the shortest path based on node distances. Deviation Handling: The system periodically re-checks the user’s beacon zone. If the user deviates from the expected node sequence, the route is recalculated instantly to provide updated guidance. Context-Aware Routing: Special priority is given to accessible pathways such as ramps or wide corridors, enabling potential support for accessibility-based routing in future enhancements. 4.4. AR Interface The AR navigation interface is built using Google ARCore, allowing real-time rendering of digital cues over the physical environment. Directional Arrow Rendering: A 3D arrow is anchored in front of the user’s device, dynamically oriented according to the next step in the route. Motion Tracking: ARCore’s visual-inertial odometry (VIO) tracks device motion to maintain arrow stability even when users rotate or tilt their phones. Environmental Awareness: The AR system identifies planes and surfaces to ensure arrows appear naturally aligned with the walkway, improving comprehension and reducing visual clutter. This AR overlay closely resembles navigation cues in simulation games, enhancing usability and engagement. 4.5. Communication Protocol Communication between beacons and the mobile application follows a lightweight BLE protocol: BLE Advertising: ESP32 beacons periodically broadcast packets containing UUID, major/minor values, and calibrated transmission power. Mobile Device Scanning: The application scans BLE channels 37–39 to detect nearby beacons, capturing RSSI and timestamp data at fixed intervals. Wi-Fi Fallback Capability (Optional): Although BLE is the primary communication method, the system design allows future integration of Wi-Fi Received Signal Strength or Wi-Fi Fine Time Measurement (FTM) to extend range in open areas. The chosen protocol minimizes power consumption and reduces the risk of packet congestion in dense deployments. International Journal of Emerging Trends in Engineering and Development Issue 15, Vol.6, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.18014749 Original Article @2025 RS Publication, [email protected] 420 V. Results and Discussion The proposed guided navigation system was evaluated through a series of controlled experiments conducted within a university academic block. Performance metrics included localization accuracy, latency, beacon coverage, AR rendering stability, and user experience feedback. This section presents the observed outcomes and analyzes system behaviour under realistic campus conditions. 5.1. Localization Accuracy Localization accuracy was measured by comparing the system’s detected user zone with the actual position across multiple test runs. Average zone-detection accuracy : 87–93% in unobstructed hallways Accuracy near intersections : Approximately 82%, due to overlapping beacon regions Accuracy in obstructed areas (walls, pillars ): 75–80% depending on materials The hybrid RSSI-based method proved sufficient for micro-navigation, where precise centimeter-level accuracy is unnecessary. Accuracy improved significantly after applying RSSI smoothing, reducing noise from sudden signal drops. 5.2. Latency and Response Time The system demonstrated low processing delays: Evaluation Me tric Average Latency Beacon detection interval 200 – 350 ms Position update cycle < 500 ms Route recomputation 20 – 80 ms AR arrow update delay < 50 ms These results show that the navigation system operates in near real-time, ensuring smooth transitions during movement. The on-device processing model eliminates server dependency, contributing to minimal latency. 5.3. Range Performance (BLE vs Wi-Fi) BLE was tested as the primary communication channel: Effective beacon range indoors: 8–14 meters High signal consistency when mounted at standardized heights Moderate interference observed near metal doors and crowded areas Preliminary tests using Wi-Fi RSSI indicated extended range but reduced stability due to router placement inconsistencies. Hence, BLE remains optimal for controlled, node-based campus navigation. 5.4. User Study and Feedback A small group of students and volunteers participated in a guided navigation trial: Ease of use: 92% rated the AR interface as intuitive Route clarity: Users found AR arrows easier to follow than static maps Perceived accuracy: Most users reported timely updates and clear decision cues at junctions Participants emphasized that the AR overlay felt similar to navigation used in virtual simulation games, improving engagement and reducing cognitive effort. 5.5. Comparison with Existing Systems Compared to traditional indoor navigation approaches: GPS-based systems : Fail indoors; proposed system performs reliably in enclosed areas Wi-Fi fingerprinting: Requires extensive calibration; proposed system eliminates this overhead Vision-based SLAM: Offers high accuracy but demands significant processing power; the proposed AR solution only uses vision for rendering, not localization International Journal of Emerging Trends in Engineering and Development Issue 15, Vol.6, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.18014749 Original Article @2025 RS Publication, [email protected] 421 Commercial beacon systems: Often costly or proprietary; ESP32 beacons provide a low-cost alternative with comparable performance for campus-scale use Overall, the proposed design achieves a practical balance between performance, affordability, and scalability. 5.6. Discussion The experimental results validate the feasibility of an embedded BLE–AR navigation system for university campuses. Although RSSI fluctuations introduce localization uncertainty, the zone-based mapping strategy mitigates their impact on route guidance. AR-based navigation significantly improves user interaction and reduces misinterpretation at intersections. The modular design allows easy scaling across larger campus areas with minimal additional configuration VI. LIMITATION Despite demonstrating promising results, the proposed navigation system exhibits several inherent limitations associated with BLE-based localization and AR rendering in dynamic indoor environments. RSSI Instability: BLE signal strength is sensitive to multipath reflections, human movement, and environmental obstructions. This leads to fluctuations that occasionally reduce localization accuracy, especially at intersections or crowded pathways. Beacon Density Requirements: Although the system requires fewer beacons than trilateration-based approaches, a minimum number of nodes must still be deployed to ensure adequate coverage. Large campuses may require meticulous planning during installation. Limited Vertical Navigation Support : The current prototype focuses on single-floor navigation. Multifloor buildings require separate beacon mapping and more complex logic for stairway or elevator detection. AR Sensitivity to Lighting and Motion: The AR module relies on visual-inertial odometry (VIO), which can be affected by poor lighting, glossy surfaces, or abrupt phone movements. This occasionally leads to drifting or misaligned arrow placement. Device Compatibility Constraints: ARCore support varies across smartphone models. Older devices with limited processing power may experience reduced frame rates or delayed rendering. These limitations do not detract from the system’s overall feasibility but highlight areas for optimization and future research. VII. FUTURE ENHANCEMENT Several improvements can be introduced to extend the capabilities, accuracy, and scalability of the system: Multi-Floor Navigation : Integrating barometric sensors, QR markers, or staircase/elevator beacons would enable seamless vertical navigation and reduce ambiguities between floors. Wi-Fi and BLE Hybrid Localization: Combining BLE with Wi-Fi RSSI or Wi-Fi Fine Time Measurement (FTM) can enhance stability in open areas and reduce dependence on dense BLE placement. Machine Learning–Based Signal Correction: Implementing neural networks or regression models to predict distance from fluctuating RSSI values can significantly improve localization accuracy, especially in complex indoor environments. Map Auto-Generation Tools: Tools for semi-automated graph creation from floor plans can simplify campus-wide deployment and reduce manual configuration.