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

机译:基于时间效率的RFID储存,粗粒库存列表

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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的储存使用RFID技术来验证区域中的物体的存在,例如仓库或库。现有方法以此目的假设RFID读取器的询问区域中的对象的库存列表是已知的。在某些情况下,这并非如此。例如,对于手持RFID读取器,只能知道较大区域中的对象(例如,仓库)而不是在其询问区域中。额外的物体显着增加了储存所需的时间。在本文中,我们提出了一个叫做CLS(基于粗粒度库存列表的储存)的时间效率的储存算法来解决这个问题。我们以大量丢失率丢失标签识别问题的问题。 CLS使多个丢失的对象能够哈希到单个时隙,从而将它们验证在一起。 CLS还通过利用更多种RFID冲突来提高现有方法,并减少读者发送的数据量的大约四分之一。进行广泛的模拟,结果显示CLS优于最佳现有算法。

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