Advances in RFID-Based Indoor Positioning Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".
Deadline for manuscript submissions: 25 November 2026 | Viewed by 3615
Special Issue Editors
2. “Instituto de Telecomunicações”, Centre for Research in Informatics and Communications—CIIC, Leiria, Portugal
Interests: sensors; positioning; RFID
Special Issues, Collections and Topics in MDPI journals
2. Computer Science Engineering Department at the School of Technology and Management of the Polytechnic University of Leiria, 2411-901 Leiria, Portugal
Interests: image processing and computer vision; machine learning; assistive technology for disabled people
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid evolution of radio frequency identification (RFID) technology has significantly transformed indoor positioning systems (IPSs), enabling precise and efficient tracking in diverse environments such as smart buildings, healthcare facilities, warehouses, and industrial automation. However, despite recent advancements, challenges remain in enhancing accuracy, scalability, energy efficiency, and real-time processing.
This Special Issue aims to explore cutting-edge research and innovative solutions in RFID-based indoor localization, covering topics such as the following:
- Machine learning- and AI-driven RFID positioning;
- Hybrid positioning methods combining RFID with other technologies (UWB, Wi-Fi, BLE, etc.);
- RF propagation modeling and signal processing techniques;
- Low-power and energy-efficient RFID-based localization;
- Security and privacy challenges in RFID tracking;
- Applications in Industry 4.0, healthcare, retail, and logistics.
We invite researchers, engineers, and industry professionals to contribute original research articles, reviews, and case studies addressing the latest breakthroughs in RFID-based IPS. Join us in shaping the future of indoor positioning!
Prof. Dr. Joao da Silva Pereira
Prof. Dr. Paulo Manuel Almeida Costa
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning- and AI-driven RFID positioning
- hybrid positioning methods combining RFID with other technologies (UWB, Wi-Fi, BLE, etc.)
- RF propagation modeling and signal processing techniques
- low-power and energy-efficient RFID-based localization
- security and privacy challenges in RFID tracking
- applications in Industry 4.0, healthcare, retail, and logistics
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