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.

[1] J. Wilson and N. Patwari, “Radio tomographic imaging with wireless networks,”
IEEE Transactions on Mobile Computing, vol. 9, no. 5, pp. 621–632, 2010.
[2] X. Wang, L. Gao, S. Mao, and S. Pandey, “Csi-based fingerprinting for indoor localization:
A deep learning approach,” IEEE Transactions on Vehicular Technology,
vol. 66, no. 1, pp. 763–776, 2017.
[3] D. Konings, F. Alam, F. Noble, and E. M. Lai, “Springloc: A device-free localization
technique for indoor positioning and tracking using adaptive rssi spring relaxation,”
IEEE Access, vol. 7, pp. 56 960–56 973, 2019.
[4] D. Halperin, W. Hu, A. Sheth, and D. Wetherall, “Tool release: Gathering 802.11n
traces with channel state information,” ACM SIGCOMM CCR, vol. 41, no. 1, p. 53,
Jan. 2011.
[5] X. Wang, X. Wang, and S. Mao, “Deep convolutional neural networks for indoor
localization with csi images,” IEEE Transactions on Network Science and Engineering,
vol. 7, no. 1, pp. 316–327, 2020.
[6] J.Wang, X. Zhang, Q. Gao, H. Yue, and H.Wang, “Device-free wireless localization
and activity recognition: A deep learning approach,” IEEE Transactions on Vehicular
Technology, vol. 66, no. 7, pp. 6258–6267, 2017.
[7] F. Adib and D. Katabi, “See through walls with wifi!” in Proceedings of the ACM
SIGCOMM 2013 Conference on SIGCOMM, ser. SIGCOMM ’13. New York, NY,
USA: Association for Computing Machinery, 2013, p. 75–86. [Online]. Available:
https://doi.org/10.1145/2486001.2486039
[8] J. Wang, Q. Gao, H. Wang, Y. Yu, and M. Jin, “Time-of-flight-based radio tomography
for device free localization,” IEEE Transactions on Wireless Communications,
vol. 12, no. 5, pp. 2355–2365, 2013.
[9] M. Bocca, O. Kaltiokallio, N. Patwari, and S. Venkatasubramanian, “Multiple target
tracking with rf sensor networks,” IEEE Transactions on Mobile Computing, vol. 13,
no. 8, pp. 1787–1800, 2014.
[10] S. Yiu, M. Dashti, H. Claussen, and F. Perez-Cruz, “Wireless rssi fingerprinting
localization,” Signal Processing, vol. 131, 07 2016.
[11] B. Mager, P. Lundrigan, and N. Patwari, “Fingerprint-based device-free localization
performance in changing environments,” IEEE Journal on Selected Areas in Communications,
vol. 33, no. 11, pp. 2429–2438, 2015.
[12] X.Wang, L. Gao, and S. Mao, “Csi phase fingerprinting for indoor localization with
a deep learning approach,” IEEE Internet of Things Journal, vol. 3, no. 6, pp. 1113–
1123, 2016.
[13] W. Zhang, K. Liu, W. Zhang, Y. Zhang, and J. Gu, “Deep neural networks for wireless
localization in indoor and outdoor environments,” Neurocomputing, vol. 194, 03
2016.
[14] D. S.Wang, X. S. Guo, and Y. X. Zou, “Accurate and robust device-free localization
approach via sparse representation in presence of noise and outliers,” in 2016 IEEE
International Conference on Digital Signal Processing (DSP), 2016, pp. 199–203.
[15] H. Huang, H. Zhao, X. Li, S. Ding, L. Zhao, and Z. Li, “An accurate and efficient
device-free localization approach based on sparse coding in subspace,” IEEE Access,
vol. 6, pp. 61 782–61 799, 2018.
[16] J. W. Taylor, P. E. McSharry, and R. Buizza, “Wind power density forecasting using
ensemble predictions and time series models,” IEEE Transactions on Energy
Conversion, vol. 24, no. 3, pp. 775–782, 2009.
[17] R. S. Tsay, Analysis of financial time series. Wiley USA, 2005.
[18] L. C. Alwan and H. V. Roberts, “Time-series modeling for statistical process control,”
Journal of Business & Economic Statistics, vol. 6, no. 1, pp. 87–95, 1988.
[19] E. Keogh and C. Ratanamahatana, “Exact indexing of dynamic time warping,”
Knowledge and Information Systems, vol. 7, pp. 358–386, 01 2005.
[20] Z. Cui, W. Chen, and Y. Chen, “Multi-scale convolutional neural networks for time
series classification,” arXiv preprint arXiv:1603.06995, 2016.
[21] Z. Wang, W. Yan, and T. Oates, “Time series classification from scratch with deep
neural networks: A strong baseline,” in 2017 International Joint Conference on Neural
Networks (IJCNN), 2017, pp. 1578–1585.
[22] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke,
A. Rabinovich et al., “Going deeper with convolutions. arxiv 2014,” arXiv
preprint arXiv:1409.4842, vol. 1409, 2014.
[23] S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training
by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167, 2015.
[24] A. Graves, Supervised Sequence Labelling with Recurrent Neural Networks, 01
2012, vol. 385.
[25] S. Ruder, “An overview of gradient descent optimization algorithms,” arXiv preprint
arXiv:1609.04747, 2016.
[26] A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison,
L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017.
[27] F. Karim, S. Majumdar, H. Darabi, and S. Chen, “Lstm fully convolutional networks
for time series classification,” IEEE access, vol. 6, pp. 1662–1669, 2017.
[28] L. Zhao, H. Huang, X. Li, S. Ding, H. Zhao, and Z. Han, “An accurate and robust
approach of device-free localization with convolutional autoencoder,” IEEE Internet
of Things Journal, vol. 6, no. 3, pp. 5825–5840, 2019.