research-article

Fast and scalable counterfeits estimation for large-scale RFID systems

Authors:

Ivan Stojmenovic,

Haoxiang LiuAuthors Info & Claims

IEEE/ACM Transactions on Networking (TON), Volume 24, Issue 2

Pages 1052 - 1064

https://doi.org/10.1109/TNET.2015.2406669

Published: 01 April 2016 Publication History

Abstract

Many algorithms have been introduced to deterministically authenticate Radio Frequency Identification (RFID) tags, while little work has been done to address scalability issue in batch authentications. Deterministic approaches verify tags one by one, and the communication overhead and time cost grow linearly with increasing size of tags. We design a fast and scalable counterfeits estimation scheme, INformative Counting (INC), which achieves sublinear authentication time and communication cost in batch verifications. The key novelty of INC builds on an FM-Sketch variant authentication synopsis that can capture key counting information using only sublinear space. With the help of this well-designed data structure, INC is able to provide authentication results with accurate estimates of the number of counterfeiting tags and genuine tags, while previous batch authentication methods merely provide 0/1 results indicating the existence of counterfeits. We conduct detailed theoretical analysis and extensive experiments to examine this design and the results show that INC significantly outperforms previous work in terms of effectiveness and efficiency.

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Published In

cover image IEEE/ACM Transactions on Networking

IEEE/ACM Transactions on Networking Volume 24, Issue 2

April 2016

646 pages

ISSN:1063-6692

Editor:
Chee Wei Tan
IEEE Transactions on Communications

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IEEE Press

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Published: 01 April 2016

Published in TON Volume 24, Issue 2

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Cited By

Zhou ZWang FGong W(2024)i-Sample: Augment Domain Adversarial Adaptation Models for WiFi-based HARACM Transactions on Sensor Networks10.1145/361649420:2(1-20)Online publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1145/3616494
Jiang MGong W(2024)Bidirectional Bluetooth Backscatter With EdgesIEEE Transactions on Mobile Computing10.1109/TMC.2023.324120223:2(1601-1612)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TMC.2023.3241202
Feng YChen SXi WWang SZhao JGong W(2023)Heartbeating With LTE Networks for Ambient BackscatterIEEE Transactions on Mobile Computing10.1109/TMC.2023.329029823:5(4246-4258)Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1109/TMC.2023.3290298
Yang YYuan LZhao JGong WBulusu NAryafar EBalasubramanian ASong J(2022)Content-agnostic backscatter from thin airProceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services10.1145/3498361.3538930(343-356)Online publication date: 27-Jun-2022
https://dl.acm.org/doi/10.1145/3498361.3538930
Gong WLiu JYang Z(2017)Efficient Unknown Tag Detection in Large-Scale RFID Systems With Unreliable ChannelsIEEE/ACM Transactions on Networking (TON)10.1109/TNET.2017.269968325:4(2528-2539)Online publication date: 1-Aug-2017
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Gong WLiu JLiu KLiu Y(2017)Toward More Rigorous and Practical Cardinality Estimation for Large-Scale RFID SystemsIEEE/ACM Transactions on Networking (TON)10.1109/TNET.2016.263455125:3(1347-1358)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1109/TNET.2016.2634551
Gong WLiu HChen LLiu KLiu Y(2016)Fast Composite Counting in RFID SystemsIEEE/ACM Transactions on Networking (TON)10.1109/TNET.2015.248368124:5(2756-2767)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1109/TNET.2015.2483681

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