Adaptive and dynamic RFID tag anti-collision based on secant iteration

Area of Science:

• Computer Science
• Electrical Engineering
• Information Technology

Background:

• Radio Frequency Identification (RFID) technology has seen significant advancements and widespread adoption in various sectors.
• Tag collision is a critical challenge in large-scale passive RFID systems, hindering identification efficiency.
• Existing anti-collision methods require improvement for optimal performance.

Purpose of the Study:

• To propose a novel tag anti-collision method for RFID systems based on Aloha technology.
• To enhance RFID identification efficiency by accurately estimating tag populations and dynamically adjusting frame lengths.
• To address the limitations of current anti-collision techniques in large-scale passive RFID applications.

Main Methods:

• A novel anti-collision method utilizing Aloha technology for RFID systems.
• Estimation of remaining tags through the secant iteration method.
• Adaptive and dynamic adjustment of subsequent frame lengths based on estimated tag population.
• An elimination method using two probing frames to resolve pseudo-solutions in tag population estimation.

Main Results:

• The proposed method achieves an estimation precision of over 97% for tag population.
• Global throughput is significantly superior to the current international standard (Q algorithm).
• Performance closely approaches the ideal system, outperforming existing schemes.

Conclusions:

• The developed RFID anti-collision method effectively addresses tag collision issues.
• High estimation accuracy and superior throughput demonstrate the method's practical viability.
• This approach offers a significant improvement for large-scale passive RFID systems.