摘要
动态帧时隙Aloha算法是一种常用的被动式射频识别(radio frequency identification,RFID)标签防冲突算法.在该算法中,帧长需要动态设置以保证较高的识别效率.通常,帧长的设置与标签数和捕获效应的发生概率相关.传统的估计算法虽然可以估计出标签数和捕获效应的发生概率,但是在稠密RFID标签环境下,标签数可能远大于初始帧长,其估计误差会显著增加.为了解决传统算法无法应用于稠密RFID标签环境的问题,提出了捕获感知贝叶斯标签估计,并且给出了非等长时隙下最优帧长的设置方法.从实验结果来看,提出算法的估计误差在稠密RFID标签环境下显著低于传统算法,而且根据估计结果设置帧长所得到的识别效率也高于传统算法.
Dynamic framed slotted Aloha algorithm is one kind of commonly used passive radio frequency identification（RFID）tag anti-collision algorithms.In the algorithm,the frame length requires dynamical set to ensure high identification efficiency.Generally,the settings of the frame length are associated with the number of tags and the probability of capture effect.Traditional estimation algorithms can estimate the number of tags and the probability of capture effect,but the number of tags is greater than an initial frame length when it is in dense RFID tags environment,and the estimation errors will increase significantly.In order to solve the problem that the conventional algorithms can not be applied to dense RFID tags environment,capture-aware Bayesian tag estimation is proposed in the paper,and the settings of optimal frame length with non-isometric slots are given.From the experimental results,the proposed algorithms have significantly lower estimation errors than traditional algorithms in dense RFID tags environment.And the identification efficiency got by setting the frame length according to the estimation results is also higher than that of traditional algorithms.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2016年第6期1325-1331,共7页
Journal of Computer Research and Development
基金
国家自然科学基金项目(61262091)
云南省第17批中青年学术和技术带头人资助项目(2014HB019)
云南省教育厅科学基金重大项目(ZD2011009)
云南省教育厅科学基金重点项目(2014Z093)~~
关键词
ALOHA算法
射频识别
捕获感知
稠密
贝叶斯估计
Aloha algorithm
radio frequency identification（RFID）
capture-aware
dense
Bayesian Estimation
