TY - GEN
T1 - TomoID
T2 - 42nd IEEE International Conference on Computer Communications, INFOCOM 2023
AU - Su, Yang Hsi
AU - Ren, Jingliang
AU - Qian, Zi
AU - Fouhey, David
AU - Sample, Alanson
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Device-free localization methods allow users to benefit from location-aware services without the need to carry a transponder. However, conventional radio sensing approaches using active wireless devices require wired power or continual battery maintenance, limiting deployability. We present TomoID, a real-time multi-user UHF RFID tomographic localization system that uses low-level communication channel parameters such as RSSI, RF Phase, and Read Rate, to create probability heatmaps of users' locations. The heatmaps are passed to our custom-designed signal processing and machine learning pipeline to robustly predict users' locations. Results show that TomoID is highly accurate, with an average mean error of 17.1 cm for a stationary user and 18.9 cm when users are walking. With multiuser tracking, results showing an average mean error of <72 cm for five individuals in constant motion. Importantly, TomoID is specifically designed to work in real-world multipath-rich indoor environments. Our signal processing and machine learning pipeline allows a pre-trained localization model to be applied to new environments of different shapes and sizes, while maintaining good accuracy sufficient for indoor user localization and tracking. Ultimately, TomoID enables a scalable, easily deployable, and minimally intrusive method for locating uninstrumented users in indoor environments.
AB - Device-free localization methods allow users to benefit from location-aware services without the need to carry a transponder. However, conventional radio sensing approaches using active wireless devices require wired power or continual battery maintenance, limiting deployability. We present TomoID, a real-time multi-user UHF RFID tomographic localization system that uses low-level communication channel parameters such as RSSI, RF Phase, and Read Rate, to create probability heatmaps of users' locations. The heatmaps are passed to our custom-designed signal processing and machine learning pipeline to robustly predict users' locations. Results show that TomoID is highly accurate, with an average mean error of 17.1 cm for a stationary user and 18.9 cm when users are walking. With multiuser tracking, results showing an average mean error of <72 cm for five individuals in constant motion. Importantly, TomoID is specifically designed to work in real-world multipath-rich indoor environments. Our signal processing and machine learning pipeline allows a pre-trained localization model to be applied to new environments of different shapes and sizes, while maintaining good accuracy sufficient for indoor user localization and tracking. Ultimately, TomoID enables a scalable, easily deployable, and minimally intrusive method for locating uninstrumented users in indoor environments.
KW - Indoor Localization
KW - Multi-User
KW - RFID
KW - Radio Tomography
KW - Real-Time
UR - http://www.scopus.com/inward/record.url?scp=85171612707&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85171612707&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM53939.2023.10228938
DO - 10.1109/INFOCOM53939.2023.10228938
M3 - Conference contribution
AN - SCOPUS:85171612707
T3 - Proceedings - IEEE INFOCOM
BT - INFOCOM 2023 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 May 2023 through 20 May 2023
ER -