An adaptive frame slotted ALOHA anti‐collision algorithm based on tag grouping

• Main Authors: Junsuo Qu, Ting Wang
• Format: Article
• Language: English
• Published: Wiley 2021-03-01
• Series: Cognitive Computation and Systems
• Online Access: https://doi.org/10.1049/ccs2.12001

Abstract Multi‐tag anti‐collision is an important problem in radio frequency identification (RFID) application. Solving the problem is of great significance to the RFID technology application and the future internet of things; therefore, an adaptive frame slotted ALOHA anti‐collision algorithm based on tag grouping (IGA) is proposed. First, a novel method for estimating the number of tags accurately is proposed. Through theoretical research and the experimental verification, a relationship is obtained between the ratio of the collision time slot in the frame and the average number of tags in each collision slot, which helps us to calculate the number of tags. Second, the method of estimating the number of tags is applied to the IGA algorithm. The reader randomly groups the tags after the number of tags are estimated, and recognises the tags by grouping. In the identification process, the idle time slot is skipped automatically, and the collided tags can be identified with an additional frame until all tags are identified. The simulation results show that the total time slot of the IGA algorithm is relatively small, and the identification efficiency is about 71%, which is 30% better than the the improved RFID anti‐collision algorithm and 90% higher than the traditional ALOHA algorithm.