Estimation of RFID Tag Population Size by Gaussian Estimator

Hasan, Mahmudul; Wei, Shuangqing; Vaidyanathan, Ramachandran

doi:10.1109/icc.2018.8422212

Cited by 4 publications

(4 citation statements)

References 19 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reader counts the number of empty slots n e as well as the number of busy slots n b and uses its difference n b − n e to estimate the number of tags. Specifically, the expected number of empty slots and busy slots can be represented as follows [37]:…”

Section: B Tag Estimation Phasementioning

confidence: 99%

Fast Counterfeit RFID Tags Detection With Slot Constraints

Zhang

Gao

2023

IEEE Access

View full text Add to dashboard Cite

RFID tags are prone to counterfeit attacks in supply chain scenarios. The attacker often uses counterfeit tags to replace stolen tags to avoid being detected by the inventory process.To defend against such attacks, existing hash-based schemes have to know the hash functions embedded in tags, which are usually unavailable in large scale systems. This letter proposes a novel group-based slot constraint (GSC) scheme for lightweight counterfeit tag detection in RFIDenabled supply-chain systems. GSC can be integrated into the identification process by leveraging elaborately designed group hash functions to authenticate tags based on their slot correlation. That is, the tags in the same group can always map to slots with a fixed offset, which offers evidence for identifying counterfeit tags with an abnormal slot index. We also provide a theoretical analysis of the time slots required to achieve the desired accuracy. The simulation results show that GSC provides reliable accuracy without knowing exact hash functions.

show abstract

Section: B Tag Estimation Phasementioning

confidence: 99%

Fast Counterfeit RFID Tags Detection With Slot Constraints

Zhang

Gao

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Now to perform our estimation of the AG population size while maintaining the accuracy requirements given in (18), we need the following two things, 1) g f (t) has to be an invertible function.…”

Section: Gaussian Approximation Of the Estimatormentioning

confidence: 99%

“…For a given frame size f , the required number of rounds required for the estimation of the tag population follows from the accuracy requirements specified in (18). For all the frame sizes in the permissible range [f min , f max ], g f (t) is a monotonic function and Z f ∼ N (µ f , σ 2 f ) with an approximation error .…”

Section: B Number Of Rounds Nmentioning

confidence: 99%

See 1 more Smart Citation

Efficient, Effective and Well Justified Estimation of Active Nodes within a Cluster

Hasan¹,

Wei²,

Vaidyanathan³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Reliable and efficient estimation of the size of a dynamically changing cluster in an IoT network is critical in its nominal operation. Most previous estimation schemes worked with relatively smaller frame size and large number of rounds.Here we propose a new estimator named "Gaussian Estimator of Active Nodes,"(GEAN), that works with large enough frame size under which testing statistics is well approximated as a Gaussian variable, thereby requiring less number of frames, and thus less total number of channel slots to attain a desired accuracy in estimation. More specifically, the selection of the frame size is done according to Triangular Array Central Limit Theorem which also enables us to quantify the approximation error. Larger frame size helps the statistical average to converge faster to the ensemble mean of the estimator and the quantification of the approximation error helps to determine the number of rounds to keep up with the accuracy requirements. We present the analysis of our scheme under two different channel models i.e. {0, 1} and {0, 1, e}, whereas all previous schemes worked only under {0, 1} channel model. The overall performance of GEAN is better than the previously proposed schemes considering the number of slots required for estimation to achieve a given level of estimation accuracy.

show abstract