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Time-Efficient RFID-Based Stocktaking with a Coarse-Grained Inventory List

机译:省时的基于RFID的库存盘点清单

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RFID-based stocktaking uses RFID technology to verify the presence of objects in a region e.g., a warehouse or a library. The existing approaches for this purpose assume that an inventory list of objects in the interrogation region of an RFID reader is known. This is not true in some cases. For example, for a handheld RFID reader, only the objects in a larger region (e.g., the warehouse) rather than in its interrogation region can be known. The additional objects significantly increase the time required for stocktaking. In this paper, we propose a time-efficient stocktaking algorithm called CLS (Coarse-grained inventory list based stocktaking) to solve this problem. We transform the problem to a missing tag identification problem with a large missing rate. CLS enables multiple missing objects to hash to a single time slot and thus verifies them together. CLS also improves the existing approaches by utilizing more kinds of RFID collisions and reducing approximately one-fourth of the amount of data sent by the reader. Extensive simulations are performed and the results show CLS outperforms the best existing algorithm.
机译:基于RFID的盘点使用RFID技术来验证对象在某个区域(例如仓库或图书馆)中的存在。为此目的的现有方法假定在RFID读取器的询问区域中的对象的清单清单是已知的。在某些情况下,这是不正确的。例如,对于手持RFID读取器,只有在较大区域(例如,仓库)而不是在其询问区域中的物体才是已知的。附加对象显着增加了清点所需的时间。在本文中,我们提出了一种节省时间的清点算法,称为CLS(基于粗粒度清单的清点方法)来解决此问题。我们将问题转换为丢失率高的标签识别问题。 CLS使多个丢失的对象可以散列到单个时隙,从而一起验证它们。 CLS还利用更多种类的RFID冲突并减少了读取器发送的数据量的约四分之一,从而改善了现有方法。进行了广泛的仿真,结果表明CLS优于现有的最佳算法。

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