摘要
近年来,随着标签价格的下降和供应链物流需求的增长,射频识别技术已经走向全面部署阶段.全面部署意指在所有商品上部署标签,以提升供应链追踪的精度.这种趋势会导致标签数据规模呈指数倍增长,给阅读器的信息读取带来很大的压力.在这个背景下,标签数量估计协议,正因为其时间效率高的特点得到越来越多的关注.现有的方法主要根据时隙状态统计特征进行数量估计.然而,各类型数量估计统计量的精度受到系统参数、标签规模、以及随机噪声的影响,其结果很不稳定.任意单一特征都很难给出精准、稳定的数量估计结果.为此,本文设计了一种基于多层感知机的数量估计方法,通过融合多样化的数量估计特征来提高估计的精度.针对真实时隙状态难以获取,模型难以训练的问题,本文研究了如何通过仿真器生成大量具有不同时隙状态的随机帧来训练我们的网络.针对数量估计网络工作范围较窄的问题,我们设计了一种两阶段基于采样思想的数量估计协议,通过快速的标签数量粗略估计设定合理的采样概率,以确保标签数目位于数量估计网络的工作区间.我们的仿真实验结果表明,本文提出的基于多层感知机的数量估计协议比现有方法能提升至少21%的精度.
In recent years,with the price decline of RFID tags and the growth of supply chain demand,the RFID technology has moved towards the next full deployment stage,which requires the deployment of tags on individual commodities to improve the accuracy of supply chain tracking.This trend will lead to an exponential increase in the number of RFID tags,increasing the pressure on reader for inventory counting.Usually,the reader needs to identify 96-bit ID of a tag for checking if they exist.However,transmitting ID via low-rate RFID channel takes too much time.Considering that many existing RFID applications do not require explicit ID information but quantity information for production and logistics decision-making,the tag cardinality estimation problems have therefore attracted the most attention in recent years.Most of the existing tag cardinality estimation schemes only need tags to respond a binary or short messages and let the read-er observe the statical features of the time frame for performing quantity estimation.The researchers have proposed various estimators,which reveals the explicit relationship between the statical feature of the time frame and the number of tags,including the number of empty/nonempty slots, the index of first empty /non-empty slot, the average length of runs of empty/nonempty slots, the difference between empty/nonempty slots. Although all these estimators are unbiased estimators of tag quantity, it is still challenging to derive an accurate and stable estimation result due to the large estimation variance caused by random noise. Moreover, all these estimators have limited applicable range, and their performance degrade quickly once the actual tag quantality out of the estimation range. To this end, this paper designs an AI-driven tag estimation method based on multi-layer perceptrons (MLP), a simple fully-connected neural network, which prompts the accuracy and stability of estimator by fusing diversified statical features of time frame. We also propose a time frame generator to simulate the slot status of time frame with given tag quantality and frame length. By generating numerous simulated time frames of the same length with respect to each tag quantality and feeding them into the training process of the neural networks, we can obtain a MLP estimator for the certain length of time frame. Additionally, to overcome the limited range of the MLP estimator, we designed a two-sage MLP estimation protocol based on the idea of sampling theory. Specifically, the first stage following the binary tree search idea to gradually increases the length of the masks for decreases the number of tags activated by the matched mask. The reader can obtain a roughly quality estimation results based on the index of first empty slots and sets a reasonable sampling probability for the second stage accordingly. Such a sampling process limits the number of tags replying within the working range of the MLP estimator and promise the estimator can provide an accurate estimation result under various tag populations. The extensive simulation results under various scenario demonstrate that the proposed MLP estimation protocol can improve the estimation accuracy by at least 21% compared with existing state-of-the-art methods.
作者
谢鑫
刘秀龙
王军晓
郭嵩
李克秋
XIE Xin;LIU Xiu-Long;WANG Jun-Xiao;GUO Song;LI Ke-Qiu(Department of Computing,The Hong Kong Polytechnic University,Hong Kong 999077;College of Intelligence and Computing,Tianjin University,Tianjin 300072)
出处
《计算机学报》
EI
CAS
CSCD
北大核心
2023年第3期499-511,共13页
Chinese Journal of Computers
基金
国家自然科学基金(No.6200225,62032017)资助.
关键词
无线射频识别
标签数量估计
多层感知机
标签采样
时间效率
radio frequency identification
tag cardinality estimation
multilayer perceptron
tag sampling
time-efficiency
