A Lightweight RSSI-Based Device-Free Localization System

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Communication Engineering > Electronic Thesis & Dissertation > Item 987654321/83862

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/83862

Title:	A Lightweight RSSI-Based Device-Free Localization System
Authors:	陳彥宇;Chen, Yen-Yu
Contributors:	通訊工程學系
Keywords:	免攜式定位;機器學習;時間序列分類;Device-Free Localization;Machine Learning;Time Series Classification
Date:	2020-08-20
Issue Date:	2020-09-02 17:16:56 (UTC+8)
Publisher:	國立中央大學
Abstract:	免攜帶式定位為一種新興的定位技術，此技術可在目標沒有攜帶任何裝置下透過無線感知網絡中的電磁訊號進行定位。此項技術的關鍵點在於當目標在監視區域中移動時，如何從無線信號中提取特徵並進行定位。為解決此問題，我們通過將免攜帶式定位問題轉化為多變數時間序列分類問題，並以新興的深度學習方法來處理。此外，我們提出的深度學習模型，稱作LSTM-MSCNN，用以萃取原始無線信號並完成端到端定位服務。另一個重要的問題是如何有效地決定無線設備的放置位置並組建無線感知網路，我們提出了一種兼顧成本效益和定位準確性的設備放置策略來解決此問題。從實驗數據中可以觀察到，本論文所提出的模型在眾多的比較的方法中在不同數量感測器配置上皆取得最高的定位精度。此外，本論文所提出的硬體擺放策略較正常擺放的方法有著百分之二十五的準確率提升，這些結果凸顯了提出的硬體擺放策略的價值，並能以一個有規劃性的方式創建適用於免攜帶式定位所需的環境。 ;Device-free localization (DFL) is an emerging technology that locates targets without any additional devices via wireless sensor networks. A fundamental problem of DFL is how to extract features from wireless signals while a target is moving in the monitoring area. We address this problem by formulating the DFL as a time series classification problem. Moreover, we propose a deep learning (DL) model taking advantage of long short-term memory (LSTM) and multi-scale convolutional network (MSCNN), called LSTM-MSCNN, for received signal strength (RSS) based localization. The network consists of multiple CNN branches to extract patterns in various time scales; LSTM cells learn the temporal dependencies in the RSS sequences. For device placement planning, we propose a strategy considering cost-effectiveness and localization accuracy. The experimental results show that the proposed model achieves the highest localization accuracy among the compared methods in different sensor implementation stages on real-world data. Additionally, the sensor implement based on the proposed strategy outperforms the uniform implement more than $25\%$ in localization accuracy.
Appears in Collections:	[Graduate Institute of Communication Engineering] Electronic Thesis & Dissertation

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