Indoor Positioning for Critical Applications
Overview
This project aims to develop a mobile-based system that tracks and navigates people in real time and indoors within buildings using Wi-Fi Received Signal Strength Indicator (RSSI) measurements. The system determines users’ locations with 10-meter accuracy and guides them to emergency exits, improving safety during emergencies. Additionally, the system could track user mobility patterns to optimize Wi-Fi access and identify congested areas. A simulated radio map, integrated with machine learning, eliminates the need for manual on-site calibration, making it efficient and scalable.
Project Team:
Principal Investigator (PI):
Co-PIs:
Researcher:
- Dr. Oussama Kerdjidj
Industry Collaborators:
- Dr. Saeed Al Mansoori, MBRSC, Dubai, UAE
External Advisors and Collaborators:
- Prof. Fethi Rabhi, UNSW, Sydney, Australia
Publications:
- Kerdjidj et al., “Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning,” in IEEE Access, vol. 12, pp. 73980-74010, 2024, https://doi.org/10.1109/ACCESS.2024.3402997
- Kerdjidj et al., “Exploiting 2-D Representations for Enhanced Indoor Localization: A Transfer Learning Approach,” in IEEE Sensors Journal, vol. 24, no. 12, pp. 19745-19755, 15 June15, 2024, https://doi.org/10.1109/JSEN.2024.3394237
- Kerdjidj et al., “Exploring 2D Representation and Transfer Learning Techniques for People Identification in Indoor Localization,” 2023 6th International Conference on Signal Processing and Information Security (ICSPIS), Dubai, United Arab Emirates, 2023, pp. 173-177, https://doi.org/10.1109/ICSPIS60075.2023.10343825
