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Active RFID attached object clustering method based on RSSI series for finding lost objects

机译:基于RSSI系列的活动RFID附加对象聚类方法查找丢失对象

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An active radio frequency identifier (RFID) tag that can communicate with smartphones using Bluetooth Low Energy technology has recently received widespread attention. Indeed, many products have been released that aim to find lost objects using the received signal strength indication (RSSI). However, these products do not provide sufficiently accurate location information to find objects. In this paper, we propose an active RFID attached object clustering method based on RSSI series for finding lost objects. This approach to find lost objects does not execute existing localization methods. We hypothesize that users can deduce the location of a lost object from information about surrounding objects in an environment where RFID tags are attached to all personal belongings. To help find lost objects from the proximity between RFID tags, the system calculates the proximity between a pair of RFID tags from the RSSI series, and estimates the groups of objects in the neighborhood. We present a method for calculating the proximity of the lost object to those around it using a distance function between RSSI series and estimating the group by hierarchical clustering. We confirm the validity of the proposed method, and determine the most appropriate pairs of four distance functions and four clustering algorithms. From the experimental results, it is apparent that our method provides a clear advantage in finding lost objects at low financial and installation cost, and can estimate groups accurately even if the smartphone or RSSI sensor is moving quickly.
机译:最近,可以使用蓝牙低能量技术与智能手机通信的活动射频标识符(RFID)标签已获得广泛的关注。实际上,许多产品已被释放,旨在使用所接收的信号强度指示(RSSI)找到丢失的物体。但是,这些产品不提供足够准确的位置信息来查找对象。在本文中,我们提出了一种基于RSSI系列的Active RFID附加的对象聚类方法,用于查找丢失对象。找到丢失对象的方法不执行现有的本地化方法。我们假设用户可以在RFID标签附加到所有个人物品的环境中,用户可以从关于周围对象的信息中推断出丢失对象的位置。为了帮助从RFID标签之间的邻近找到丢失的对象,系统计算来自RSSI系列的一对RFID标签之间的接近度,并估计邻域中的对象组。我们介绍了一种方法,用于计算丢失对象与其周围的那些在其周围的字段之间的附近,并通过分层群集估计该组。我们确认所提出的方法的有效性,并确定最合适的四个距离功能对和四个聚类算法。从实验结果来看,显而易见的是,我们的方法在以低财务和安装成本寻找丢失的物体方面提供了明显的优势,并且即使智能手机或RSSI传感器快速移动,也可以准确估计群组。

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