Smart Campus Navigation System: Enhancing Wayfinding Using AI and IoT Integration

Abstract

University campuses are evolving into miniature smart cities, yet navigation within them remains a persistent challenge. New students, faculty, and visitors often face confusion in locating classrooms, hostels, administrative offices, or event venues. Traditional signboards and static maps fail to address the dynamic nature of campuses with frequent schedule changes, construction zones, and accessibility requirements. This paper proposes a Smart Campus Navigation System (SCNS) integrating AI, IoT, and mobile technologies to deliver real-time, personalized wayfinding. The system employs GPS and IoT sensors for hybrid indoor-outdoor positioning, machine learning algorithms for optimized routing, and accessibility-aware features for differently-abled users. By bridging social and technical gaps, SCNS transforms wayfinding from a logistical problem into an inclusive, smart campus service aligned with smart city goals.

Introduction

The text presents the development of a Smart Campus Navigation System (SCNS) to address challenges faced in navigating modern university campuses, which have expanded into complex, multi-building environments. Traditional wayfinding methods—signboards, printed maps, QR codes, and general-purpose navigation apps—are limited, often outdated, and fail to support indoor guidance, real-time updates, or accessibility needs. These limitations impact productivity, inclusivity, and visitor experience, highlighting the societal importance of efficient, human-centered campus navigation.

The SCNS framework integrates AI, IoT, and mobile technologies to deliver a seamless, adaptive, and inclusive navigation experience. Its key innovations include:

1. Hybrid Indoor–Outdoor Positioning: Combines GPS for outdoor navigation with Wi-Fi and BLE beacons indoors for uninterrupted guidance.

2. AI-Powered Route Optimization: Machine learning algorithms suggest routes considering distance, crowd density, time constraints, and accessibility requirements.

3. Accessibility-Aware Features: Supports wheelchair-friendly paths, voice-based navigation for visually impaired users, and multilingual interfaces.

4. Dynamic, Event-Aware Navigation: Adapts in real time to temporary blockages, events, or maintenance disruptions.

Theoretical foundations cover:

• Wayfinding in complex campus environments, emphasizing contextual cues, landmarks, and personalized routing.

• Indoor and outdoor positioning technologies (GPS, Wi-Fi fingerprinting, BLE beacons, RFID, QR codes), with hybrid models providing seamless coverage.

• AI-driven route optimization using algorithms like Dijkstra and A* enhanced with predictive analytics and reinforcement learning for congestion-aware, personalized navigation.

• Accessibility and human-centered design for inclusivity, supporting differently-abled users and diverse populations.

System architecture includes five core modules:

1. User Application – Mobile interface for searching destinations and receiving real-time routes.

2. IoT Positioning Layer – BLE beacons and Wi-Fi access points for indoor localization.

3. Central Server – Hosts AI algorithms, maps, and real-time decision-making.

4. Accessibility Module – Integrates ramps, elevators, and restricted areas into route planning.

5. Admin Portal – Allows campus staff to update maps, flag disruptions, and manage alerts.

Technical innovations:

• Accurate hybrid positioning with 3–5 meter localization error.

• Accessibility-aware pathfinding using modified Dijkstra–A* algorithms.

• Context-aware AI guidance for dynamic rerouting.

• Multi-modal user interface including visual maps, voice navigation, and AR overlays for intuitive guidance.

Conclusion

The Smart Campus Navigation System addresses the persistent challenges faced by students, faculty, and visitors in navigating large and complex university environments. Traditional tools such as GPS-based applications fail to provide reliable indoor positioning, accessibility support, and campus-specific contextual guidance. By integrating IoT-enabled indoor positioning, AI-driven pathfinding, and accessibility-aware routing, the proposed system moves beyond conventional navigation solutions to create an inclusive, adaptive, and human-centered framework. The experimental deployment demonstrated that the system significantly improves navigation efficiency, reduces time-to-destination, and enhances accessibility for differently-abled users. Features such as multi-modal interfaces, event-based rerouting, and real-time campus updates further establish its relevance in dynamic academic settings. Beyond navigation, this system lays the groundwork for a broader Smart Campus ecosystem, with potential extensions into campus safety, energy management, and crowd-flow optimization. Future work will focus on scaling the system with advanced technologies such as Augmented Reality (AR) navigation, machine learning-based congestion prediction, and integration with wearable devices to further personalize the user experience. In conclusion, the Smart Campus Navigation System represents a socio-technical solution that enhances efficiency, inclusivity, and user experience within higher education institutions, and has the potential to be adapted for other complex environments such as hospitals, airports, and smart cities.

References

[1] H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of Wireless Indoor Positioning Techniques and Systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 37, no. 6, pp. 1067–1080, Nov. 2007. [2] S. He and S. H. G. Chan, “Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons,” IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 466–490, 2016. [3] P. Davidson and R. Piché, “A Survey of Selected Indoor Positioning Methods for Smartphones,” IEEE Communications Surveys & Tutorials, vol. 19, no. 2, pp. 1347–1370, 2017. [4] R. Faragher and R. Harle, “Location Fingerprinting With Bluetooth Low Energy Beacons,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 11, pp. 2418–2428, Nov. 2015. [5] F. Zafari, A. Gkelias, and K. Leung, “A Survey of Indoor Localization Systems and Technologies,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2568–2599, 2019. [6] A. Baniukevic, D. Sabonis, C. S. Jensen, and H. Lu, “Improving Wi-Fi Based Indoor Positioning Using Bluetooth Add-Ons,” Proceedings of the 2013 IEEE 14th International Conference on Mobile Data Management, Milan, Italy, pp. 246–255, 2013. [7] M. Papadopoulos, G. Zacharaki, and D. Kandris, “Smart Campus Services and Applications: A Systematic Review,” Sensors, vol. 22, no. 2, p. 612, Jan. 2022. [8] A. A. Bakar, S. S. Yusof, and N. H. Hamid, “Smart Campus: A Framework for Digital Transformation of Higher Education Institutions,” International Journal of Interactive Mobile Technologies (iJIM), vol. 14, no. 14, pp. 4–18, 2020. [9] H. P. Nguyen, M. H. Pham, and V. H. Le, “Towards Smart Campus: A Review of Trends, Architectures, and Challenges,” IEEE Access, vol. 10, pp. 11123–11141, 2022. [10] L. Mainetti, V. Mighali, L. Patrono, and I. Sergi, “A Survey on Indoor Positioning Systems for Smart Campus Applications,” International Journal of Computer Applications, vol. 975, pp. 8887–8893, 2014. [11] C. Xu, L. Chen, and C. Wu, “Accessibility-Oriented Path Planning for Indoor Navigation of the Visually Impaired,” Sensors, vol. 19, no. 21, p. 4781, Nov. 2019. [12] D. D. Salim, T. A. Rahman, and A. S. Malik, “An Indoor Navigation System for Visually Impaired People Using a Smart Phone,” IEEE Access, vol. 6, pp. 56812–56822, 2018. [13] A. Alletto, R. Cucchiara, G. Del Fiore, L. Mainetti, V. Mighali, L. Patrono, and G. Serra, “An Indoor Location-Aware System for an IoT-Based Smart Museum,” IEEE Internet of Things Journal, vol. 3, no. 2, pp. 244–253, Apr. 2016. [14] P. Hart, N. Nilsson, and B. Raphael, “A Formal Basis for the Heuristic Determination of Minimum Cost Paths,” IEEE Transactions on Systems Science and Cybernetics, vol. 4, no. 2, pp. 100–107, July 1968. [15] E. W. Dijkstra, “A Note on Two Problems in Connexion With Graphs,” Numerische Mathematik, vol. 1, no. 1, pp. 269–271, 1959. [16] K. Kim, H. Shin, and S. Lee, “Mobile Augmented Reality Navigation System Using Indoor Positioning and Map Generation,” Sensors, vol. 21, no. 4, p. 1231, Feb. 2021. [17] Z. Zhou, X. Chen, Y. Li, and L. Zhang, “Smart Campus: Toward an Inclusive and Intelligent Environment,” IEEE Access, vol. 8, pp. 183407–183420, 2020. [18] M. Dardari, A. Conti, U. Ferner, A. Giorgetti, and M. Z. Win, “Ranging With Ultrawide Bandwidth Signals in Multipath Environments,” Proceedings of the IEEE, vol. 97, no. 2, pp. 404–426, Feb. 2009. [19] B. Ferris, D. Fox, and N. Lawrence, “WiFi-SLAM Using Gaussian Process Latent Variable Models,” Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2480–2485, 2007. [20] L. Bruno and P. Robertson, “Wi-Fi Positioning and Navigation in Large Buildings,” IEEE Pervasive Computing, vol. 13, no. 4, pp. 90–97, 2014. [21] Y. Gu, A. Lo, and I. Niemegeers, “A Survey of Indoor Positioning Systems for Wireless Personal Networks,” IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 13–32, 2009. [22] A. Correa, M. Fischer, and T. Krüger, “Navigation Support for Blind People Using Smartphone and Bluetooth Beacons,” Proceedings of the 13th International Conference on Computers Helping People with Special Needs (ICCHP), pp. 265–272, 2012. [23] A. Ghosh, A. Kumar, and M. Singh, “Smart Campus Framework Using IoT and Cloud Computing,” International Journal of Computer Applications, vol. 178, no. 44, pp. 6–11, 2019. [24] S. Yiu, C. Jin, and S. H. G. Chan, “Adaptive Indoor Positioning System Using Crowdsourced Fingerprints,” IEEE Transactions on Mobile Computing, vol. 17, no. 7, pp. 1585–1598, July 2018. [25] A. Montella, L. Pariota, and A. D’Apuzzo, “Mobile Augmented Reality for Indoor Navigation: A User Study,” International Journal of Human-Computer Studies, vol. 132, pp. 62–73, 2019.

Copyright

Copyright © 2025 Vishal Nayakwadi, Varada Supekar, Bhumi Takrani, Aastha Thakare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.