Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Efficient IoT Devices Localization Through Wi-Fi CSI Feature Fusion and Anomaly Detection (2407.02919v1)

Published 3 Jul 2024 in cs.IT and math.IT

Abstract: Internet of Things (IoT) device localization is fundamental to smart home functionalities, including indoor navigation and tracking of individuals. Traditional localization relies on relative methods utilizing the positions of anchors within a home environment, yet struggles with precision due to inherent inaccuracies in these anchor positions. In response, we introduce a cutting-edge smartphone-based localization system for IoT devices, leveraging the precise positioning capabilities of smartphones equipped with motion sensors. Our system employs AI to merge channel state information from proximal trajectory points of a single smartphone, significantly enhancing line of sight (LoS) angle of arrival (AoA) estimation accuracy, particularly under severe multipath conditions. Additionally, we have developed an AI-based anomaly detection algorithm to further increase the reliability of LoSAoA estimation. This algorithm improves measurement reliability by analyzing the correlation between the accuracy of reversed feature reconstruction and the LoS-AoA estimation. Utilizing a straightforward least squares algorithm in conjunction with accurate LoS-AoA estimation and smartphone positional data, our system efficiently identifies IoT device locations. Validated through extensive simulations and experimental tests with a receiving antenna array comprising just two patch antenna elements in the horizontal direction, our methodology has been shown to attain decimeter-level localization accuracy in nearly 90% of cases, demonstrating robust performance even in challenging real-world scenarios. Additionally, our proposed anomaly detection algorithm trained on Wi-Fi data can be directly applied to ultra-wideband, also outperforming the most advanced techniques.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (52)
  1. R. Jin, J. Zhou, J. Hu, P. Guo, Y. Wu et al., “Towards Practical Lightweight Passive Human Tracking Using WiFi Sensing,” IEEE Internet Things J., vol. 10, no. 15, pp. 13 769–13 783, 2023.
  2. L. Jiang, L. N. Hoe, and L. L. Loon, “Integrated UWB and GPS location sensing system in hospital environment,” in Proc. IEEE Conf. Ind. Electron. Appl., ICIEA, 2010, pp. 286–289.
  3. L. Terças, H. Alves, C. H. M. de Lima, and M. Juntti, “Bayesian-Based Indoor Factory Positioning Using AOA, TDOA and Hybrid Measurements,” IEEE Internet Things J, pp. 1–1, 2024.
  4. H. Li, J. K. Ng, V. C. Cheng, and W. K. Cheung, “Fast indoor localization for exhibition venues with calibrating heterogeneous mobile devices,” Internet Things, vol. 3, pp. 175–186, 2018.
  5. M. Zhao, “Technology of internet of things technology in the construction of smart mine,” in Proc. 3rd Int. Conf. Artif. Intell. Big Data (ICAIBD), Chengdu, China, 2020, pp. 289–292.
  6. R. Faragher and R. Harle, “Location fingerprinting with bluetooth low energy beacons,” IEEE J. Select. Areas Commun., vol. 33, no. 11, pp. 2418–2428, 2015.
  7. M. I. ul Haq and D. Kim, “A novel localization algorithm for internet of things in 3D,” in Proc. Int. Conf. Inf. Commun. Technol. Convergence (ICTC), 2015, pp. 237–241.
  8. D. Feng, C. Wang, C. He, Y. Zhuang, and X.-G. Xia, “Kalman-filter-based integration of imu and uwb for high-accuracy indoor positioning and navigation,” IEEE Internet Things J, vol. 7, no. 4, pp. 3133–3146, 2020.
  9. J. Ding, Y. Wang, H. Si, S. Gao, and J. Xing, “Three-dimensional indoor localization and tracking for mobile target based on wifi sensing,” IEEE Internet Things J, vol. 9, no. 21, pp. 21 687–21 701, 2022.
  10. S. Alletto, R. Cucchiara, G. Del Fiore, L. Mainetti, V. Mighali, L. Patrono, and G. Serra, “An indoor location-aware system for an iot-based smart museum,” IEEE Internet Things J, vol. 3, no. 2, pp. 244–253, 2016.
  11. S. Ma, Q. Liu, and P. C.-Y. Sheu, “Foglight: Visible light-enabled indoor localization system for low-power iot devices,” IEEE Internet Things J, vol. 5, no. 1, pp. 175–185, 2018.
  12. Y. Shu, C. Bo, G. Shen, C. Zhao, L. Li, and F. Zhao, “Magicol: Indoor localization using pervasive magnetic field and opportunistic WiFi sensing,” IEEE J. Sel. Areas Commun., vol. 33, no. 7, pp. 1443–1457, 2015.
  13. D. Chiasson, Y. Lin, M. Kok, and P. B. Shull, “Asynchronous hyperbolic uwb source-localization and self-localization for indoor tracking and navigation,” IEEE Internet Things J, vol. 10, no. 13, pp. 11 655–11 668, 2023.
  14. X. Geng, R. Peng, M. Li, W. Liu, G. Jiang, H. Jiang, and J. Luo, “A lightweight approach for passive human localization using an infrared thermal camera,” IEEE Internet Things J, vol. 9, no. 24, pp. 24 800–24 811, 2022.
  15. Y. Lin, K. Yu, L. Hao, J. Wang, and J. Bu, “An indoor Wi-Fi localization algorithm using ranging model constructed with transformed rssi and BP neural network,” IEEE Trans. Commun., vol. 70, no. 3, pp. 2163–2177, 2022.
  16. X. Tong, H. Wang, X. Liu, and W. Qu, “MapFi: Autonomous mapping of Wi-Fi infrastructure for indoor localization,” IEEE Trans. Mobile Comput., vol. 22, no. 3, pp. 1566–1580, 2021.
  17. L. Gong, C. Wang, L. Zhu, J. Zhang, W. Yang, Y. Zhao, and C. Xiang, “LAMP: Lightweight and accurate malicious access points localization via channel phase information,” in Proc. Int. Conf. Wireless Algorithms Syst. Appl., 2018, pp. 748–753.
  18. H. Zou, B. Huang, X. Lu, H. Jiang, and L. Xie, “A robust indoor positioning system based on the procrustes analysis and weighted extreme learning machine,” IEEE Trans. Wireless Commun., vol. 15, no. 2, pp. 1252–1266, 2015.
  19. Y. Zhuang, Z. Syed, J. Georgy, and N. El-Sheimy, “Autonomous smartphone-based WiFi positioning system by using access points localization and crowdsourcing,” Pervasive Mob. Comput., vol. 18, pp. 118–136, 2015.
  20. H. Zhao, B. Huang, and B. Jia, “Applying kriging interpolation for WiFi fingerprinting based indoor positioning systems,” in IEEE Wireless Commun. Networking Conf. WCNC, 2016, pp. 1–6.
  21. A. Yassin, Y. Nasser, M. Awad, A. Al-Dubai, R. Liu, C. Yuen, R. Raulefs, and E. Aboutanios, “Recent advances in indoor localization: A survey on theoretical approaches and applications,” IEEE Commun. Surv. Tutorials, vol. 19, no. 2, pp. 1327–1346, 2016.
  22. B. Jia, B. Huang, H. Gao, W. Li, and L. Hao, “Selecting critical WiFi APs for indoor localization based on a theoretical error analysis,” IEEE Access, vol. 7, pp. 36 312–36 321, 2019.
  23. S. He and S.-H. G. Chan, “Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons,” IEEE Commun. Surveys Tuts., vol. 18, no. 1, pp. 466–490, 2015.
  24. H.-H. Hsu, W.-J. Peng, T. K. Shih, T.-W. Pai, and K. L. Man, “Smartphone indoor localization with accelerometer and gyroscope,” in Proc. Int. Conf. Netw.-Based Inf. Syst., NBiS, 2014, pp. 465–469.
  25. J. Jeong, S. Yeon, T. Kim, H. Lee, S. M. Kim, and S.-C. Kim, “SALA: Smartphone-assisted localization algorithm for positioning indoor IoT devices,” Wireless Netw., vol. 24, pp. 27–47, 2018.
  26. W.-T. Shih, C.-K. Wen, S.-H. Tsai, R. Liu, and C. Yuen, “EasyAPPos: Positioning Wi-Fi access points by using a mobile phone,” IEEE Internet Things J., vol. 10, no. 15, pp. 13 385–13 400, 2023.
  27. T. Hao, R. Zhou, G. Xing, M. W. Mutka, and J. Chen, “Wizsync: Exploiting Wi-Fi infrastructure for clock synchronization in wireless sensor networks,” IEEE Trans. Mobile Comput., vol. 13, no. 6, pp. 1379–1392, 2013.
  28. R. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Trans. Antennas Propag., vol. 34, no. 3, pp. 276–280, 1986.
  29. B. Mamandipoor, D. Ramasamy, and U. Madhow, “Newtonized orthogonal matching pursuit: Frequency estimation over the continuum,” IEEE Trans. Signal Process, vol. 64, no. 19, pp. 5066–5081, 2016.
  30. X. Tong, H. Li, X. Tian, and X. Wang, “Wi-Fi localization enabling self-calibration,” IEEE/ACM Trans. Netw., vol. 29, no. 2, pp. 904–917, 2021.
  31. M. Asim, M. O. Iqbal, W. Aman, M. M. U. Rahman, and Q. H. Abbasi, “Pathloss-based non-Line-of-Sight identification in an indoor environment: An experimental study,” arXiv preprint arXiv:2307.15995, 2023.
  32. X. Li, X. Cai, Y. Hei, and R. Yuan, “NLOS identification and mitigation based on channel state information for indoor Wi-Fi localization,” IET Commun., vol. 11, no. 4, pp. 531–537, 2017.
  33. W. Xue, W. Qiu, X. Hua, and K. Yu, “Improved Wi-Fi RSSI measurement for indoor localization,” IEEE Sens. J., vol. 17, no. 7, pp. 2224–2230, 2017.
  34. P. Chen and S. Zhang, “DeepMetricFi: Improving Wi-Fi Fingerprinting Localization by Deep Metric Learning,” IEEE Internet Things J., vol. 11, no. 4, pp. 6961–6971, 2023.
  35. S.-M. Chun, S.-M. Lee, J.-W. Nah, J.-H. Choi, and J.-T. Park, “Localization of Wi-Fi access point using smartphone’s GPS information,” in Int. Conf. Sel. Top. Mob. Wirel. Networking, iCOST, 2011, pp. 121–126.
  36. M. Ji, J. Kim, Y. Cho, Y. Lee, and S. Park, “A novel Wi-Fi AP localization method using monte carlo path-loss model fitting simulation,” in Proc. IEEE 24th Annu. Int. Symp. Pers. Indoor Mobile Radio Commun. (PIMRC), 2013, pp. 3487–3491.
  37. Y. Zhuang, Y. Li, H. Lan, Z. Syed, and N. El-Sheimy, “Wireless access point localization using nonlinear least squares and multi-level quality control,” IEEE Wireless Commun. Lett., vol. 4, no. 6, pp. 693–696, 2015.
  38. J. Koo and H. Cha, “Localizing Wi-Fi access points using signal strength,” IEEE Commun. Lett., vol. 15, no. 2, pp. 187–189, 2010.
  39. S. Y. Nam, “Localization of access points based on signal strength measured by a mobile user node,” IEEE Commun. Lett., vol. 18, no. 8, pp. 1407–1410, 2014.
  40. F. Zhao, H. Luo, H. Geng, and Q. Sun, “An RSSI gradient-based AP localization algorithm,” China Commun., vol. 11, no. 2, pp. 100–108, 2014.
  41. F. Zafari, A. Gkelias, and K. K. Leung, “A survey of indoor localization systems and technologies,” IEEE Commun. Surv. Tutorials, vol. 21, no. 3, pp. 2568–2599, 2019.
  42. X. Tong, H. Li, X. Tian, and X. Wang, “Triangular antenna layout facilitates deployability of CSI indoor localization systems,” in Annu. IEEE Commun.Soc. Conf. Sens., Mesh Ad Hoc Commun. Netw. workshops, 2019, pp. 1–9.
  43. R. Ayyalasomayajula, A. Arun, C. Wu, A. Shaikh, S. Rajagopalan, Y. Hu, S. Ganesaraman, C. J. Rossbach, A. Seetharaman, E. Witchel et al., “LocAP: Autonomous millimeter accurate mapping of Wi-Fi infrastructure,” in Proc. USENIX Symp. Networked Syst. Des. Implement., NSDI, 2020, pp. 1115–1129.
  44. K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom, “Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding,” in Proc. 24th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min. (KDD), Jul. 2018, pp. 387–395.
  45. P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, and G. Shroff, “LSTM-based encoder-decoder for multi-sensor anomaly detection,” arXiv preprint arXiv:1607.00148, 2016.
  46. B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen, “Deep autoencoding gaussian mixture model for unsupervised anomaly detection,” in Int. Conf. Learn. Represent., ICLR - Conf. Track Proc, 2018.
  47. D. Park, Y. Hoshi, and C. C. Kemp, “A multimodal anomaly detector for robot-assisted feeding using an LSTM-Based variational autoencoder,” IEEE Rob. Autom. Lett., vol. 3, no. 3, pp. 1544–1551, 2018.
  48. B. Mamandipoor, D. Ramasamy, and U. Madhow, “Newtonized orthogonal matching pursuit: Frequency estimation over the continuum,” IEEE Trans. Signal Process., vol. 64, no. 19, pp. 5066–5081, 2016.
  49. F. Remcom, “Wireless InSite: 3D Wireless Prediction Software,” 2021, [Online]. Available: https://www.remcom.com/wireless-insite-empropagation-software.
  50. I. . W. Group et al., “Part 11: Wireless LAN medium access control (mac) and physical layer (phy) specifications,” ANSI/IEEE Std. 802.11, 1999 Edition, 1999.
  51. C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning (still) requires rethinking generalization,” Commun. ACM, vol. 64, no. 3, pp. 107–115, 2021.
  52. C. Jiang, J. Shen, S. Chen, Y. Chen, D. Liu, and Y. Bo, “UWB NLOS/LOS classification using deep learning method,” IEEE Commun. Lett., vol. 24, no. 10, pp. 2226–2230, 2020.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (6)
  1. Yan Li (505 papers)
  2. Jie Yang (516 papers)
  3. Shang-Ling Shih (4 papers)
  4. Wan-Ting Shih (6 papers)
  5. Chao-Kai Wen (145 papers)
  6. Shi Jin (487 papers)

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now