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Dr YANG Lei and his team won Best Paper Award at IEEE SECON 2023

31 Oct 2023

Ms Qingrui PAN received the best paper award from the general co-chairs and TPC chairs of IEEE SECON 2023.

(a) Imperfection caused by the measurement of the width of symbol TRcal. The width is measured by counting the number of edges of the tag’s internal clock. (b) The BLF histograms across three tags indicate the uniqueness of frequency shifts. (c) Illustration of the experimental setup, including an acquisition system and RFID tags.

Dr YANG Lei, our Associate Professor, along with Dr Zhenlin AN, postdoctoral fellow and Ms Qingrui PAN and Mr Xiaopeng ZHAO, our PhD students,  received Best Paper Award at IEEE SECON 2023.


Their research paper titled "Revisiting Backscatter Frequency Drifts for Fingerprinting RFIDs: A Perspective of Frequency Resolution" was honoured with the Best Paper Award at the 20th Annual IEEE International Conference on Sensing, Communication, and Networking (IEEE SECON 2023).


Researchers have been aware of the potential of utilising the backscatter frequency drift (BFD) as a unique fingerprint for Radio Frequency Identification (RFID) tags for over a decade. By comparing the discrepancy between the actual backscatter frequency and the requested backscatter link frequency (BLF), it is possible to identify the RFID tags at the physical layer. However, the efficacy of this method has been limited due to a low success rate.


Addressing this issue, this paper suggests a new perspective on the low success rate – a low resolution in frequency measurement. In other words, the measurement lacks sufficient ability to effectively distinguish the slight difference in frequency in BFD, resulting in inaccurate results. To overcome this limitation, this paper introduces an innovative solution by significantly augmenting the frequency resolution to enhance the distinctiveness of BFD, thereby the accuracy of identification can be improved, especially when handling a diverse range of RFID tags.


Read the paper:


IEEE SECON is a distinguished avenue that offers a collaborative space for scholars, industry experts, standards creators, and policymakers to discuss, exchange, and promote ideas and advancements in the realms of sensor, ad hoc, and mesh networks and systems.

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