Enhancing counterfeit RFID tag classification through distance based cognitive risk control

In the realm of radio frequency identification (RFID) systems, combatting counterfeit tags through anti-counterfeiting technologies has garnered significant attention, particularly physical layer identification methods, lauded for their cost-effectiveness and simplicity in deployment. Nonetheless, the performance of physical layer recognition method is significantly impacted by the conditions of the tag detection setting, especially in scenarios characterized by low signal-to-noise ratio (SNR), where classification accuracy tends to suffer. To tackle this challenge head-on, this study proposes the implementation of a cognitive risk control strategy, which fine-tunes tag distance within the tag classification process to bolster the SNR and enhance recognition precision. Beyond the enactment of cognitive risk control, this paper extends its focus to encompass enriched time domain and frequency domain feature extraction, totaling 104 features, aimed at further enhancing classification efficacy. Leveraging software-defined radio devices, classification experiments encompassing seven popular tag types from three distinct manufacturers were conducted. Results from these experiments reveal that upon integrating the cognitive risk control strategy, the average accuracy of tag classification experiences an approximate 11% increase. Concurrently, in comparison to traditional twenty-eight and seven features, the adoption of one-hundred-and-four features translates to an enhancement in classification accuracy by roughly 4.3% and 5.3%, respectively. These findings not only underscore the efficacy of cognitive risk control in elevating label classification accuracy within low SNR environments but also underscore the potential for augmenting classification performance through an increased feature set.