Geographical Correlation-Based Data Collection for Sensor-Augmented RFID Systems

Geographical Correlation-Based Data Collection for Sensor-Augmented RFID Systems
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传感器增强 RFID 系统基于地理相关性的数据收集

DOI:
10.1109/tmc.2019.2923413
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发表时间:
2020-10
影响因子:
7.9
通讯作者:
Jie Wu
Jie Wu
中科院分区:
计算机科学2区
文献类型:
--
作者:
Xin Xie;Xiulong Liu;Heng Qi;Bin Xiao;Keqiu Li;Jie Wu

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本文研究了传感器增强的RFID系统的数据采集的实际重要问题。然而,现有的RFID数据收集协议遭受两个共同的限制:执行时间自然地与标签的数量成比例,因此它们不能满足时间严格的应用场景;它们都不符合C1 G2标准,因此它们不能使用商用现成(COTS)RFID标签来实现。为了克服这两个局限性,本文提出了基于地理相关性<underline>的</underline>射频数据<underline>收集</underline>协议(GRC)。<underline></underline>GRC是快速的,因为它能够通过仅从一小组采样标签实际收集数据来近似捕获所有标签的感测数据。这是基于从真实世界数据集的观察,即感测数据具有很强的地理相关性,即,从附近的RFID标签收集的数据具有类似的值。在GRC中,我们使用一种贪婪的方法来找到最小的采样标签集,以覆盖整个监测区域,使每个未采样的标签至少有一个附近的采样标签。然后,RFID阅读器运行C1 G2标准中规定的帧时隙Aloha(FSA)协议,从采样的标签中收集传感数据。对于每个未采样的标签,我们通过计算从其附近的采样标签收集的数据的加权平均值来近似其感测数据,其中远处的采样标签应该被赋予小的权重,反之亦然。与现有的RFID数据采集方案相比,GRC具有两方面的优势:(1)大量的仿真结果表明,我们的GRC方案的时间开销仅为现有数据采集方案的<inline-formula><tex-math notation="LaTeX">1/28{\sim }1/3 $1/28{\sim }1/3</tex-math><alternatives><mml:math><mml:mrow><mml:mn>$1</mml:mn><mml:mo>/</mml:mo><mml:mn>28 {</mml:mn><mml:mo>\sim</mml:mo><mml:mn>} 1</mml:mn><mml:mo>/</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq1-2923413.gif"/></alternatives></inline-formula>;(2)GRC完全符合C1 G2标准,因此可以很容易地部署在COTS RFID标签上。
This paper studies the practically important problem of data collection for sensor-augmented RFID systems. However, existing RFID data collection protocols suffer from two common limitations: execution time is naturally in proportion to the number of tags, thus they cannot satisfy time-stringent application scenarios; none of them is complaint with the C1G2 standard, thus they cannot be implemented using Commercial-Off-The-Shelf (COTS) RFID tags. To overcome these two limitations, this paper proposes the <underline>G</underline>eographical correlation-based <underline>R</underline>F-data <underline>C</underline>ollection (GRC) protocol. GRC is fast because it is able to approximately capture the sensing data of all tags by only actually gathering data from a small set of sampled tags. This is based on the observation from the real-world data set that sensing data has a strong geographical correlation, i.e., data gathered from nearby RFID tags has similar values. In GRC, we use a greedy approach to find the minimum sampling tag set to cover the whole monitoring region such that each un-sampled tag has at least one sampled tag nearby. Then, RFID reader runs the Framed Slotted Aloha (FSA) protocol specified in C1G2 standard to collect sensing data from the sampled tags. For each un-sampled tag, we approximate its sensing data by calculating weight-average of the data collected from its nearby sampled tags, where a faraway sampled tag should be given a small weight, and vice versa. Compared with existing RFID data collection schemes, the advantages of GRC are two-fold: (1) Extensive simulation results demonstrate that the time cost of our GRC scheme is only <inline-formula><tex-math notation="LaTeX">$1/28{\sim }1/3$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>28</mml:mn><mml:mo>∼</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq1-2923413.gif"/></alternatives></inline-formula> of the state-of-the-art data collection scheme; (2) GRC is totally complaint with C1G2 standard, thus it can be easily deployed on the COTS RFID tags.
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