Active RFID Attached Object Clustering Method with New Evaluation Criterion for Finding Lost Objects
Active RFID Attached Object Clustering Method with New Evaluation Criterion for Finding Lost Objects
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DOI：
10.1155/2017/3637814
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发表时间：
2017-02
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通讯作者：
Masaya Tanbo;Ryoma Nojiri;Yuusuke Kawakita;H. Ichikawa
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作者：
Masaya Tanbo;Ryoma Nojiri;Yuusuke Kawakita;H. Ichikawa
可以使用蓝牙低功耗技术与智能手机通信的有源射频识别（RFID）标签最近受到了广泛关注。我们已经研究了一种新的方法来寻找丢失的对象，使用有源RFID。我们假设，用户可以推断出一个丢失的对象的位置，从周围的物体在RFID标签附着到所有的个人物品的环境中的信息。为了帮助从RFID标签之间的接近度找到丢失的物体，系统从RSSI系列计算RFID标签对之间的接近度，并估计附近的物体组。我们开发了一种方法，用于计算丢失的对象，它周围使用RSSI系列之间的距离函数和估计组的层次聚类的接近度。没有方法直接评估组合是否适合应用目的。目前，距离函数和聚类算法的不同组合产生不同的聚类结果。因此，我们提出了最近邻候选数（NNNC）作为衡量聚类结果的标准。仿真结果表明，NNNC是一个合适的评价标准，我们的系统，因为它能够详尽地评估距离函数和聚类算法的组合。
An active radio frequency identification (RFID) tag that can communicate with smartphones using Bluetooth low energy technology has recently received widespread attention. We have studied a novel approach to finding lost objects using active RFID. 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 pairs of RFID tags from the RSSI series and estimates the groups of objects in the neighborhood. We developed 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. There is no method to evaluate whether a combination is suitable for application purposes directly. Presently, different combinations of distance functions and clustering algorithms yield different clustering results. Thus, we propose the number of nearest neighbor candidates (NNNC) as the criterion to evaluate the clustering results. The simulation results show that the NNNC is an appropriate evaluation criterion for our system because it is able to exhaustively evaluate the combination of distance functions and clustering algorithms.