RSS (dbm) cm 1cm 2cm 3cm 4cm 5cm Time (sec) rss [dbm] 6 7 BLE beacon Random Forest!!! time [msec] Receiver 2 RSS F
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1 マルチメディア, 分散, 協調とモバイル (DICOMO218) シンポジウム 平成 3 年 7 月 Bluetooth Low Energy 1,a) Bluetooth Low Energy BLE BLE Received Signal Strength RSS RSS 2 BLE RSS RSS 2.85 cm Proposal of Water-Level Estimation Method using Bluetooth Low Energy Ryo Orihara 1,a) Shigemi Ishida 1 Masahiko Miyazaki 1 Shigeaki Tagashira 2 Akira Fukuda 1 1. [1] [2] ISEE, Kyushu University, Fukuoka , Japan 2 Faculty of Informatics, Kansai University, Osaka, , Japan a) orihara@f.ait.kyushu-u.ac.jp 4986 [3] Bluetooth Low Energy BLE BLE RSS Received Signal Strength RSS 2 BLE RSS RSS 218 Information Processing Society of Japan 812
2 RSS (dbm) cm 1cm 2cm 3cm 4cm 5cm Time (sec) rss [dbm] 6 7 BLE beacon Random Forest!!! time [msec] Receiver 2 RSS Fig. 2 Variation of RSS 1 Fig. 1 Overview of Water-Level Estimation Method BLE RSS 6 RSS % 2.85 cm 2. BLE 3. BLE BLE 1 BLE BLE BLE BLE BLE Advertising BLE Advertising RSS BLE 2.4 GHz Advertising RSS BLE 2 1) RSS BLE RSS RSS 2 BLE Advertising RSS cm 5 cm 1 cm 2 RSS RSS RSS 2) RSS RSS RSS RSS ) RSS RSS 2 BLE Advertising RSS RSS 2 BLE Advertising RSS RSS 2) RSS Advertising 218 Information Processing Society of Japan 813
3 Reference BLE Observation BLE Fig. 3 Reference BLE Observation BLE 3 RSS Data Leaning Process Analysis Block Analysis Block Estimate Process Overview of water-level estimation system RSS Data RSS Data Analysis Block Moving Average Moving Average Random Forest Random Forest Output Output RSS Advertising RSS 2 BLE Advertising 2 BLE RSS RSS BLE RSS RSS RSS 4 Fig. 4 Overview of analysis block RSS Advertising RSS RSS RSS BLE BLE Advertising [4] Advertising Advertising BLE BLE BLE Reference BLE BLE Observation BLE 2 BLE BLE BLE Advertising RSS RSS BLE RSS Advertising Random Forest 3.4 Random Forest Random Forest Random Forest 2 2 BLE RSS RSS Random Forest Random Forest RSS RSS 4. BLE 218 Information Processing Society of Japan 814
4 4 Linear interpolation Original data RSS [dbm] 5 Amplitude Time [sec] Frequency [Hz] Fig. 5 5 Result of linear interpolation 7 Fig. 7 FFT Result of FFT Signal [dbm] Cumulative Distribution Time [sec].2 6 Fig. 6 Result of using window function 4.1 RSS Fast Fourier Transform FFT FFT FFT BLE 1 RSS 512 FFT 5 6 FFT Hz.5 Hz 99.4 % 2 1 RSS Frequency [Hz] 12cm Fig. 8 11cm 8 FFT Cumulative distribution of FFT 55cm Bathtub 9 Fig. 9 16cm Size of bathroom Bathroom 1 BLE BLE 2 BLE RSS cm 5 cm 1 cm BLE RSS 218 Information Processing Society of Japan 815
5 表 1 実装環境 Table 1 Implementation environment PC MacBook Pro 13-inch, 216 OS macos Sierra プロセッサ 2GHz Intel Core i5 メモリ 8GB 14 図 1 12 Reference BLE beacon error quantity Observation BLE beacon 実験環境 Fig. 1 Experiment setup degree of error [cm] 4 5 図 12 誤差の分布 Fig. 12 Distribution of error 1..8 使用機器 proportion of error 図 11 Fig. 11 Experiment equipments し 得られた RSS データと受信間隔を用いて機械学習で 水位を推定した Random Forest では決定木数は 1 とし た そして 1 分割交差検証を用いて どの程度正確に水 位を推定できたか また どの程度の誤差が生じたか評価 した. 図 11 に評価に使用した機器を示す BLE ビーコンは MyBeacon Pro 汎用型 MB4 Ac-DR を使用した Mac degree of error [cm] 図 13 PC は表 1 に示すように MacBook Pro を使用し プログ 4 5 誤差の累積分布 Fig. 13 Cumulative distribution of error ラムは Python バージョン によって実装した node.js [5] の bleacon を用いて BLE ビーコンから送信 例えば 1 のところにある棒グラフは 1 cm だけ間違え された信号の受信を実装した ユーザが指定した時間信号 て推定しているのがこれだけあるということを意味してお を受信し 信号の RSS と受信時刻を記録する Python [6] り 2 のところにある棒グラフは 2 cm だけ間違えて推 を用いて 取得したデータの受信時刻から信号の受信間 定しているのがこれだけあるということを意味している 隔を算出する 以下 Python を用いて実装した RSS また 図 13 には推定誤差の累積分布を示す と信号の受信間隔の変化を特徴量として sklearn [7] の RandomForestClassifier を用いて機械学習を実装した 本システムを用いた水位推定の結果 正答率は 84.7 % 平均誤差は 2.85 cm となった これは 単一の BLE ビー コンを用いた際の 正答率 64.8 % 平均誤差 4.79 cm とい 4.3 評価結果 う結果を大きく上回るものであり 高い精度での推定が可 誤差の結果を図 12 に示す 図 12 は水位を推定する際 能であると確認した に 誤差としてどれだけ間違えて推定したかを表している c 218 Information Processing Society of Japan 816
6 5. RSS 5.1 CCTV Closed Circuit Television: [8] CCTV Dr.i-sensor CCTV [9] cm 8.3 cm Channel State Information CSI Wi-Wheat [1] CSI WiFi [11 16] Wi-Wheat support vector machine SVM PCA CSI SVM CSI 2 CSI Wi-Wheat line-of-sight LOS non-lineof-sight NLOS 5.2 WiFi WiGest WiFi-based hand gesture recognition system [17] WiGest WiFi RSS 1 Discrete Wavelet Transform: DWT Information Processing Society of Japan 817
7 Stein unbiased risk estimate SURE [18] SURE WiGest AP 87.5 % 3 6. BLE RSS 2 BLE RSS % 2.85 cm JP15H578 JP17H1741 Digital Content Technology and its Applications, Vol. 5, No. 3, pp (211). [12] Liu, Z., Wu, Z., Zhang, Z., Wu, W. and Li, H.: Research on online moisture detector in grain drying process based on V/F conversion, Mathematical Problems in Engineering, Vol. 215 (215). [13] Nelson, S. O., Kraszewski, A. W., Trabelsi, S. and Lawrence, K. C.: Using cereal grain permittivity for sensing moisture content, IEEE transactions on instrumentation and measurement, Vol. 49, No. 3, pp (2). [14] Kim, K., Kim, J., Lee, C., Noh, S. and Kim, M.: Simple instrument for moisture measurement in grain by free-space microwave transmission, Transactions of the ASABE, Vol. 49, No. 4, pp (26). [15] Yang, Y., Wang, J., Wang, C. et al.: Study on online measurement of grain moisture content by neutron gauge., Transactions of the Chinese Society of Agricultural Engineering, Vol. 16, No. 5, pp (2). [16] Nath K, D. and Ramanathan, P.: Non-destructive methods for the measurement of moisture contents a review, Sensor Review, Vol. 37, No. 1, pp (217). [17] Abdelnasser, H., Youssef, M. and Harras, K. A.: WiGest: A ubiquitous WiFi-based gesture recognition system, 215 IEEE Conference on Computer Communications (INFOCOM), pp (online), DOI: 1.119/IN- FOCOM (215). [18] Sardy, S., Tseng, P. and Bruce, A.: Robust wavelet denoising, IEEE Transactions on Signal Processing, Vol. 49, No. 6, pp (online), DOI: 1.119/ (21). [1]. kawabou/reference/indexall_ip.html. [2].com (217). -/3757. [3] 7 (217) /k/m/4/172c. [4] Bluetooth Special Interest Group: Bluetooth Specification Version 4.2 (214). [5] Node.js Foundation: Node.js. en/. [6] Python Software Foundation: Python (218). https: // [7] scikit learn: scikit-learn Machine Learning in Python. [8] CCTV i-net, Vol. 44, pp. 8 9 (216). [9] CCTV Dr.i-sensor i-net, Vol. 39, pp. 4 5 (215). [1] Yang, W., Wang, X., Song, A. and Mao, S.: Wi-Wheat: Contact-free Wheat Moisture Detection with Commodity WiFi. [11] Wang, W. and Dai, Y.: A grain moisture detecting system based on capacitive sensor, International Journal of 218 Information Processing Society of Japan 818
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