Active RFID Attached Object Clustering Method with New Evaluation Criterion for Finding Lost Objects

Abstract

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.