Capture-Aware Dense Tag Identification Using RFID Systems in Vehicular Networks

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-07, Vol.23 (15), p.6792
• Main Authors: Xu, Weijian, Song, Zhongzhe, Sun, Yanglong, Wang, Yang, Lai, Lianyou
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Binomial distribution; capture effect; Efficiency; Fading channels; Identification; Monte Carlo method; Probability; Radio frequency; Radio frequency identification (RFID); RFID; tag identification; vehicular networks; Wireless communications
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23156792

Passive radio-frequency identification (RFID) systems have been widely applied in different fields, including vehicle access control, industrial production, and logistics tracking, due to their ability to improve work quality and management efficiency at a low cost. However, in an intersection situation where tags are densely distributed with vehicle gathering, the wireless channel becomes extremely complex, and the readers on the roadside may only decode the information from the strongest tag due to the capture effect, resulting in tag misses and considerably reducing the performance of tag identification. Therefore, it is crucial to design an efficient and reliable tag-identification algorithm in order to obtain information from vehicle and cargo tags under adverse traffic conditions, ensuring the successful application of RFID technology. In this paper, we first establish a Nakagami- distributed channel capture model for RFID systems and provide an expression for the capture probability, where each channel is modeled as any relevant Nakagami- distribution. Secondly, an advanced capture-aware tag-estimation scheme is proposed. Finally, extensive Monte Carlo simulations show that the proposed algorithm has strong adaptability to circumstances for capturing under-fading channels and outperforms the existing algorithms in terms of complexity and reliability of tag identification.