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9 October 2023 Optimization of dynamic frame slot ALOHA algorithm based on back propagation neural network
Yulu Wu, Shaopeng Li, Xiangpeng Kong, Yang Li, Yunfeng Sun, Fuxiang Zhang
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Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127910B (2023) https://doi.org/10.1117/12.3004669
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China
Abstract
In RFID systems, the dynamic frame slot ALOHA algorithm is an efficient and low-cost method to solve the tag anticollision problem. To solve the problem of excessive energy loss of the reader caused by the Q algorithm frequently transmitting frame length adjustment commands, a dynamic frame slot ALOHA algorithm based on BP neural network (BP-DFSA) is proposed, which can estimate the remaining number of unidentified tags based on the quantity of the three slot states of the previous frame, and adjust the frame length of the next frame only at the end of the current frame. The MATLAB simulation results show that the algorithm can make the RFID system send the frame length adjustment command within 13 times in the whole identification process, and the system throughput rate is stable at about 0.345. It is calculated that this algorithm can significantly reduce the number of frame length adjustment commands sent, saving 43% of reader energy consumption compared to the Q anti-collision algorithm.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yulu Wu, Shaopeng Li, Xiangpeng Kong, Yang Li, Yunfeng Sun, and Fuxiang Zhang "Optimization of dynamic frame slot ALOHA algorithm based on back propagation neural network", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127910B (9 October 2023); https://doi.org/10.1117/12.3004669
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KEYWORDS
Neural networks
Computer simulations
Algorithm development
Neurons
MATLAB
System identification
Algorithms
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