RSSI系列類似性比較による発信源の近接関係推定における離散フーリエ変換を用いた距離関数の適用効果

ユークリッド距離やDTWなどの距離関数は時系列データ分析で一般的に使われているが，ノイズに弱いという欠点がある．これに対し，ノイズ耐性のある離散フーリエ変換を用いた距離関数が提案されている．本論文は伝搬環境の影響を受けやすいRSSI時系列データから発信源の近接関係を推定する問題に対し，離散フーリエ変換を用いた距離関数の適用効果を定量的に評価した．We use distance function for proximity estimation of transmission source in order to estimate objects location without distance measurement. The distance function needs tolerance to fluctuation of RSSI to estimate proximity accurately. This letter reports result of comparing noise tolerance of distance function using discrete Fourier transform with general distance function

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

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