• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Wu Haifeng and Zeng Yu. ADFA Protocol for RFID Tag Collision Arbitration[J]. Journal of Computer Research and Development, 2011, 48(5): 802-810.
Citation: Wu Haifeng and Zeng Yu. ADFA Protocol for RFID Tag Collision Arbitration[J]. Journal of Computer Research and Development, 2011, 48(5): 802-810.

ADFA Protocol for RFID Tag Collision Arbitration

More Information
  • Published Date: May 14, 2011
  • When a radio frequency identification (RFID) system identifies multiple tags, tag collisions will happen. The RFID system generally applies a tag anti-collision protocol to resolve the multi-tag collisions. To reduce identified time, this paper proposes a new adaptive dynamic framed Aloha (ADFA) for RFID tag collision arbitration. Based on dynamic framed Aloha protocol, ADFA adaptively allocates each identified tag a slot number. During the next reading round, the tags will be identified according to the slot number, which can reduce collision and idle slots when a reader repeatedly tags. In many RFID applications where a reader may repeatedly identify tags, such as supply chain operation, object tracking and locating, the proposed protocols can reduce time of re-identifying tags. Furthermore, to reduce more identified time, we improve ADFA protocol and propose a tag quantity estimate with low computational complexity and an optimal frame length. The tag estimate is based on Vogt method, and can reduce computational complexity by narrowing the search range of the tag quantity. And the optimal frame length scheme can achieve maximum throughput under the condition that the slot durations are different. The theoretical computation and simulation results both show that ADFA can reduce identified time when repeatedly reading tags, and the tag estimate in the improved ADFA can lower computational complexity. In addition, the optimal frame length in the improved ADFA can also advance system throughput.
  • Related Articles

    [1]Ma Zhaojia, Shao En, Di Zhanyuan, Ma Lixian. Porting and Parallel Optimization of Common Operators Based on Heterogeneous Programming Models[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202330869
    [2]Zhou Ze, Sun Yinghui, Sun Quansen, Shen Xiaobo, Zheng Yuhui. An Adversarial Detection Method Based on Tracking Performance Difference of Frequency Bands[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440428
    [3]Li Maowen, Qu Guoyuan, Wei Dazhou, Jia Haipeng. Performance Optimization of Neural Network Convolution Based on GPU Platform[J]. Journal of Computer Research and Development, 2022, 59(6): 1181-1191. DOI: 10.7544/issn1000-1239.20200985
    [4]Xie Zhen, Tan Guangming, Sun Ninghui. Research on Optimal Performance of Sparse Matrix-Vector Multiplication and Convoulution Using the Probability-Process-Ram Model[J]. Journal of Computer Research and Development, 2021, 58(3): 445-457. DOI: 10.7544/issn1000-1239.2021.20180601
    [5]Zhang Jun, Xie Jingcheng, Shen Fanfan, Tan Hai, Wang Lümeng, He Yanxiang. Performance Optimization of Cache Subsystem in General Purpose Graphics Processing Units: A Survey[J]. Journal of Computer Research and Development, 2020, 57(6): 1191-1207. DOI: 10.7544/issn1000-1239.2020.20200113
    [6]Gu Rong, Yan Jinshuang, Yang Xiaoliang, Yuan Chunfeng, and Huang Yihua. Performance Optimization for Short Job Execution in Hadoop MapReduce[J]. Journal of Computer Research and Development, 2014, 51(6): 1270-1280.
    [7]Zhang Fengjun, Zhao Ling, An Guocheng, Wang Hongan, Dai Guozhong. Mean Shift Tracking Algorithm with Scale Adaptation[J]. Journal of Computer Research and Development, 2014, 51(1): 215-224.
    [8]Lü Na and Feng Zuren. Adaptive Multi-Resolutional Image Tracking Algorithm[J]. Journal of Computer Research and Development, 2012, 49(8): 1708-1714.
    [9]Li Shanqing, Tang Liang, Liu Keyan, Wang Lei. A Fast and Adaptive Object Tracking Method[J]. Journal of Computer Research and Development, 2012, 49(2): 383-391.
    [10]Zheng Ruijuan, Wu Qingtao, Zhang Mingchuan, Li Guanfeng, Pu Jiexin, Wang Huiqiang. A Self-Optimization Mechanism of System Service Performance Based on Autonomic Computing[J]. Journal of Computer Research and Development, 2011, 48(9): 1676-1684.

Catalog

    Article views (1043) PDF downloads (567) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return