Tagoram: real-time tracking of mobile RFID tags to high precision using COTS devices

By Lei Yang, Yekui Chen, Xiangyang Li, Chaowei Xiao, Mo Li, Yunhao Liu. ACM/IEEE International Conference on Mobile Computing and Networking, 2014

754 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/d25f8103e30d158e4993bbe4f120c49fb0b7f9a5

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In Aerospace Engineering

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Citations in the corpus

Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, background): “number of layers in that network [64], [65].”
Precise Power Delay Profiling with Commodity Wi-Fi (2015, background): “Prior works also notice that the CSI traces reported by WiFi NICs contain phase errors introduced by hardware [40], [41].”
Dynamic-MUSIC: accurate device-free indoor localization (2016, methodology): “Different technologies have been employed for indoor localization, such as camera [3, 4], RF [2, 33, 36, 37, 50], infrared [8, 13], Ultra-wideband (UWB) [9], sound [14, 41], and recently visible light [15, 20], etc.”
Understanding and Modeling of WiFi Signal Based Human Activity Recognition (2015, background): “Theoretically, it is possible to precisely measure the phase of the path in systems where sender and receiver are perfectly synchronized, e.g. , as in RFID systems [31].”
Caraoke: An E-Toll Transponder Network for Smart Cities (2015, result): “Our work is also related to past work on RFID localization and RF-based positioning [60, 29, 59, 63, 26].”
Deep Learning for RFID-Based Activity Recognition (2016, methodology): “Examples include the Convolutional Neural Network (CNN), widely used for image classification [13] and recently for speech recognition [24], the Deep Neural Network (DNN), used for speech recognition and audio sensing [25], and a multimodal structure used for audio-visual speech recognition [26].”
ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization (2015, background): “Recently, indoor wireless localization systems have broken the meter accuracy barrier both for Wi-Fi devices [18, 58, 59] and RFID tags [52, 53, 62], but to achieve these results, require some combination of many access points (APs) and antennas, very long antenna arrays, and/or an RF environment wi”
A Survey on Wireless Indoor Localization from the Device Perspective (2016, methodology): “…technique and achieved a mean accuracy of 12.8cm. MIMO techniques further pushed RFID-based localization to centimeter-level accuracy using Synthetic Aperture Radar (SAR) [Wang and Katabi 2013], Inverse Synthetic Aperture Radar (ISAR) [Scherhauﬂ et al. 2014], and hologram [Yang et al. 2014].”