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Automatic Update for Wi-Fi Fingerprinting Indoor Localization via Multi-Target Domain Adaptation

Published: 12 June 2023 Publication History

Abstract

Wi-Fi fingerprinting system in the long term suffers from gradually deteriorative localization accuracy, leading to poor user experiences. To keep high accuracy yet at a low cost, we first study long-term variation of access points (APs) and characteristics of their Wi-Fi signals through over-one-year experiments. Motivated by the experimental findings, we then design MTLoc, a Multi-Target domain adaptation network-based Wi-Fi fingerprinting Localization system. As the core, MTDAN (Multi-Target Domain Adaptation Network) model adopts the framework of generative adversarial network to learn time-invariant, time-specific, and location-aware features from the source and target domains. To enhance the alignment among the source and targets, two-level cycle consistency constraints are proposed. Hence, MTDAN is able to transfer location knowledge from the source domain to multiple targets. In addition, domain selection and outlier detection are designed to avoid explosive growth of storage for targets and to limit the impact of random variations of Wi-Fi signals. Extensive experiments are carried out on five datasets collected over two years in various real-world indoor environments with a total area of 8, 350 m2. Experimental results demonstrate that MTLoc retains high localization accuracy with limited storage and training cost in the long term, which significantly outperforms its counterparts. We share our dataset to the community for other researchers to validate our results and conduct further research.

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References

[1]
Roshan Ayyalasomayajula, Aditya Arun, Chenfeng Wu, Sanatan Sharma, Abhishek Rajkumar Sethi, Deepak Vasisht, and Dinesh Bharadia. 2020. Deep Learning Based Wireless Localization for Indoor Navigation. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking (London, United Kingdom) (MobiCom '20). ACM, New York, USA, Article 17, 14 pages. https://doi.org/10.1145/3372224.3380894
[2]
Changhao Chen, Yishu Miao, Chris Xiaoxuan Lu, Linhai Xie, Phil Blunsom, Andrew Markham, and Niki Trigoni. 2019. MotionTransformer: Transferring Neural Inertial Tracking between Domains. In AAAI '19. 8009--8016. https://doi.org/10.1609/aaai.v33i01.33018009
[3]
Xi Chen, Hang Li, Chenyi Zhou, Xue Liu, Di Wu, and Gregory Dudek. 2020. FiDo: Ubiquitous Fine-Grained WiFi-Based Localization for Unlabelled Users via Domain Adaptation. In The World Wide Web Conference (Taipei, Taiwan) (WWW '20). ACM, New York, USA, 23--33. https://doi.org/10.1145/3366423.3380091
[4]
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016. Domain-Adversarial Training of Neural Networks. Journal of Machine Learning Research 17, 1 (Jan. 2016), 2096--2030.
[5]
Chao Gao and Robert Harle. 2018. Semi-Automated Signal Surveying Using Smartphones and Floorplans. IEEE Transactions on Mobile Computing 17, 8 (2018), 1952--1965. https://doi.org/10.1109/TMC.2017.2776128
[6]
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In NIPS'14. MIT Press, 2672--2680.
[7]
Baoshen Guo, Weijian Zuo, Shuai Wang, Wenjun Lyu, Zhiqing Hong, Yi Ding, Tian He, and Desheng Zhang. 2022. WePos: Weak-Supervised Indoor Positioning with Unlabeled WiFi for On-Demand Delivery. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 2, Article 54 (jul 2022), 25 pages. https://doi.org/10.1145/3534574
[8]
Daniel Halperin, Wenjun Hu, Anmol Sheth, and David Wetherall. 2011. Tool Release: Gathering 802.11n Traces with Channel State Information. SIGCOMM Comput. Commun. Rev. 41, 1 (jan 2011), 53. https://doi.org/10.1145/1925861.1925870
[9]
Suining He and S-H Gary Chan. 2016. Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons. IEEE Communications Surveys Tutorials 18, 1 (Firstquarter 2016), 466--490. https://doi.org/10.1109/COMST.2015.2464084
[10]
Suining He, Wenbin Lin, and S. H. Gary Chan. 2017. Indoor Localization and Automatic Fingerprint Update with Altered AP Signals. IEEE Transactions on Mobile Computing 16, 7 (2017), 1897--1910.
[11]
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell. 2018. CyCADA: Cycle-Consistent Adversarial Domain Adaptation. In International Conference on Machine Learning. PMLR, 1989--1998.
[12]
Baoqi Huang, Zhendong Xu, Bing Jia, and Guoqiang Mao. 2019. An Online Radio Map Update Scheme for WiFi Fingerprint-Based Localization. IEEE Internet of Things Journal 6, 4 (2019), 6909--6918. https://doi.org/10.1109/JIOT.2019.2912808
[13]
Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In ICML'15. JMLR.org, 448--456.
[14]
Jin-Woo Jang and Song-Nam Hong. 2018. Indoor Localization with WiFi Fingerprinting Using Convolutional Neural Network. In 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, USA, 753--758.
[15]
Diederik Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR.
[16]
Manikanta Kotaru, Kiran Joshi, Dinesh Bharadia, and Sachin Katti. 2015. Spotfi: Decimeter level localization using WiFi. In ACM SIGCOMM. ACM, 269--282.
[17]
Swarun Kumar, Stephanie Gil, Dina Katabi, and Daniela Rus. 2014. Accurate Indoor Localization with Zero Start-up Cost. In Proceedings of the 20th Annual International Conference on Mobile Computing and Networking (Maui, Hawaii, USA) (MobiCom '14). ACM, New York, USA, 483--494. https://doi.org/10.1145/2639108.2639142
[18]
Danyang Li, Jingao Xu, Zheng Yang, Yumeng Lu, Qian Zhang, and Xinglin Zhang. 2021. Train once, locate anytime for anyone: Adversarial learning based wireless localization. In IEEE Conference on Computer Communications (INFOCOM 2021). IEEE, USA, 1--10.
[19]
Hang Li, Xi Chen, Ju Wang, Di Wu, and Xue Liu. 2022. DAFI: WiFi-Based Device-Free Indoor Localization via Domain Adaptation. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 4, Article 167 (dec 2022), 21 pages. https://doi.org/10.1145/3494954
[20]
Liqun Li, Guobin Shen, Chunshui Zhao, Thomas Moscibroda, Jyh-Han Lin, and Feng Zhao. 2014. Experiencing and Handling the Diversity in Data Density and Environmental Locality in an Indoor Positioning Service. In Proceedings of the 20th Annual International Conference on Mobile Computing and Networking (Maui, Hawaii, USA) (MobiCom '14). ACM, New York, USA, 459--470. https://doi.org/10.1145/2639108.2639118
[21]
Zhizhong Li and Derek Hoiem. 2018. Learning without Forgetting. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 12 (2018), 2935--2947. https://doi.org/10.1109/TPAMI.2017.2773081
[22]
Germán Martín Mendoza-Silva, Philipp Richter, Joaquín Torres-Sospedra, Elena Simona Lohan, and Joaquín Huerta. 2018. Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning. Data 3, 1 (2018). https://doi.org/10.3390/data3010003
[23]
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. 2018. Spectral Normalization for Generative Adversarial Networks. In ICLR.
[24]
Jiazhi Ni, Fusang Zhang, Jie Xiong, Qiang Huang, Zhaoxin Chang, Junqi Ma, BinBin Xie, Pengsen Wang, Guangyu Bian, Xin Li, and Chang Liu. 2022. Experience: Pushing Indoor Localization from Laboratory to the Wild. In Proceedings of the 28th Annual International Conference on Mobile Computing And Networking (Sydney, NSW, Australia) (MobiCom '22). Association for Computing Machinery, New York, NY, USA, 147--157. https://doi.org/10.1145/3495243.3560546
[25]
Qun Niu, Tao He, Ning Liu, Suining He, Xiaonan Luo, and Fan Zhou. 2020. MAIL: Multi-Scale Attention-Guided Indoor Localization Using Geomagnetic Sequences. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4, 2, Article 54 (Jun 2020), 23 pages. https://doi.org/10.1145/3397335
[26]
Augustus Odena, Christopher Olah, and Jonathon Shlens. 2017. Conditional image synthesis with auxiliary classifier GANs. In International conference on machine learning. PMLR, 2642--2651.
[27]
Sinno Jialin Pan, James T. Kwok, Qiang Yang, and Jeffrey Junfeng Pan. 2007. Adaptive Localization in a Dynamic WiFi Environment through Multi-View Learning. In Proceedings of the 22nd National Conference on Artificial Intelligence (Vancouver, British Columbia, Canada) (AAAI '07). AAA Press, 1108--1113.
[28]
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017. Automatic Differentiation in PyTorch. In NIPS 2017 Workshop on Autodiff.
[29]
Anshul Rai, Krishna Kant Chintalapudi, Venkata N Padmanabhan, and Rijurekha Sen. 2012. Zee: zero-effort crowdsourcing for indoor localization. In MobiCom 12. 293--304.
[30]
Souvik Sen, Božidar Radunovic, Romit Roy Choudhury, and Tom Minka. 2012. You Are Facing the Mona Lisa: Spot Localization Using PHY Layer Information. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (Low Wood Bay, Lake District, UK) (MobiSys '12). ACM, New York, USA, 183--196. https://doi.org/10.1145/2307636.2307654
[31]
Kihyuk Sohn, Honglak Lee, and Xinchen Yan. 2015. Learning structured output representation using deep conditional generative models. In NIPS. 3483--3491.
[32]
Yu Tian, Jiankun Wang, and Zenghua Zhao. 2021. Wi-Fi Fingerprint Update for Indoor Localization via Domain Adaptation. In 2021 IEEE ICPADS. 835--842.
[33]
Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing Data using t-SNE. Journal of Machine Learning Research 9 (2008), 2579--2605.
[34]
Deepak Vasisht, Swarun Kumar, and Dina Katabi. 2016. Decimeter-level Localization with a Single WiFi Access Point. In Proceedings of the 13th Usenix Conference on Networked Systems Design and Implementation (Santa Clara, CA) (NSDI '16). USENIX Association, Berkeley, CA, USA, 165--178. http://dl.acm.org/citation.cfm?id=2930611.2930623
[35]
Bang Wang, Qiuyun Chen, Laurence T Yang, and Han-Chieh Chao. 2016. Indoor smartphone localization via fingerprint crowdsourcing: challenges and approaches. IEEE Wireless Communications 23, 3 (June 2016), 82--89. https://doi.org/10.1109/MWC.2016.7498078
[36]
He Wang, Souvik Sen, Ahmed Elgohary, Moustafa Farid, Moustafa Youssef, and Romit Roy Choudhury. 2012. No Need to War-Drive: Unsupervised Indoor Localization. In MobiSys '12. ACM, 197--210. https://doi.org/10.1145/2307636.2307655
[37]
Jiankun Wang, Zenghua Zhao, Jiayang Cui, Yu Wang, YiYao Shi, and Bin Wu. 2021. Low-Cost Wi-Fi Fingerprinting Indoor Localization via Generative Deep Learning. In WASA. Springer International Publishing, 53--64.
[38]
Xuyu Wang, Xiangyu Wang, and Shiwen Mao. 2017. CiFi: Deep convolutional neural networks for indoor localization with 5 GHz Wi-Fi. In 2017 IEEE International Conference on Communications (ICC). 1--6. https://doi.org/10.1109/ICC.2017.7997235
[39]
Xiangyu Wang, Xuyu Wang, Shiwen Mao, Jian Zhang, Senthilkumar CG Periaswamy, and Justin Patton. 2020. Indoor Radio Map Construction and Localization With Deep Gaussian Processes. IEEE Internet of Things Journal 7, 11 (2020), 11238--11249. https://doi.org/10.1109/JIOT.2020.2996564
[40]
Garrett Wilson and Diane J. Cook. 2020. A Survey of Unsupervised Deep Domain Adaptation. ACM Trans. Intell. Syst. Technol. 11, 5, Article 51 (July 2020), 46 pages. https://doi.org/10.1145/3400066
[41]
Chenshu Wu, Zheng Yang, and Yunhao Liu. 2015. Smartphones Based Crowdsourcing for Indoor Localization. IEEE Transactions on Mobile Computing 14, 2 (2015), 444--457. https://doi.org/10.1109/TMC.2014.2320254
[42]
Chenshu Wu, Zheng Yang, Chaowei Xiao, Chaofan Yang, Yunhao Liu, and Mingyan Liu. 2015. Static power of mobile devices: Self-updating radio maps for wireless indoor localization. In 2015 IEEE Conference on Computer Communications (INFOCOM). IEEE, USA, 2497--2505. https://doi.org/10.1109/INFOCOM.2015.7218639
[43]
Yaxiong Xie, Zhenjiang Li, and Mo Li. 2015. Precise Power Delay Profiling with Commodity WiFi. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking (Paris, France) (MobiCom '15). ACM, New York, USA, 53--64. https://doi.org/10.1145/2789168.2790124
[44]
Yaxiong Xie, Jie Xiong, Mo Li, and Kyle Jamieson. 2019. MD-Track: Leveraging Multi-Dimensionality for Passive Indoor Wi-Fi Tracking. In The 25th Annual International Conference on Mobile Computing and Networking (MobiCom '19). ACM, New York, USA, Article 8, 16 pages. https://doi.org/10.1145/3300061.3300133
[45]
Jie Xiong and Kyle Jamieson. 2013. ArrayTrack: a fine-grained indoor location system. In Proc. of USENIX NSDI. 71--84.
[46]
Bing Xu, Naiyan Wang, Tianqi Chen, and Mu Li. 2015. Empirical Evaluation of Rectified Activations in Convolutional Network. arXiv:1505.00853 [cs.LG]
[47]
Huatao Xu, Pengfei Zhou, Rui Tan, Mo Li, and Guobin Shen. 2021. LIMU-BERT: Unleashing the Potential of Unlabeled Data for IMU Sensing Applications. In SenSys '21. ACM, 220--233. https://doi.org/10.1145/3485730.3485937
[48]
Zheng Yang, Chenshu Wu, Zimu Zhou, Xinglin Zhang, Xu Wang, and Yunhao Liu. 2015. Mobility Increases Localizability: A Survey on Wireless Indoor Localization Using Inertial Sensors. ACM Comput. Surv. 47, 3, Article 54 (April 2015), 34 pages. https://doi.org/10.1145/2676430
[49]
Zili Yi, Hao Zhang, Ping Tan, and Minglun Gong. 2017. DualGAN: Unsupervised Dual Learning for Image-to-Image Translation. In 2017 IEEE International Conference on Computer Vision (ICCV) (Venice, Italy). 2868--2876. https://doi.org/10.1109/ICCV.2017.310
[50]
Daqing Zhang, Dan Wu, Kai Niu, Xuanzhi Wang, Fusang Zhang, Jian Yao, Dajie Jiang, and Fei Qin. 2022. Practical Issues and Challenges in CSI-based Integrated Sensing and Communication. In 2022 IEEE International Conference on Communications Workshops (ICC Workshops). 836--841. https://doi.org/10.1109/ICCWorkshops53468.2022.9814523
[51]
Vincent Wenchen Zheng, Evan Wei Xiang, Qiang Yang, and Dou Shen. 2008. Transferring Localization Models over Time. In Proceedings of the 23rd National Conference on Artificial Intelligence (Chicago, Illinois, USA) (AAAI '08). AAAI Press, 1421--1426.

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      cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
      Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 7, Issue 2
      June 2023
      969 pages
      EISSN:2474-9567
      DOI:10.1145/3604631
      Issue’s Table of Contents
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      Published: 12 June 2023
      Published in IMWUT Volume 7, Issue 2

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      Author Tags

      1. Wi-Fi fingerprinting
      2. deep learning
      3. fingerprint update
      4. indoor localization
      5. unsupervised multi-target domain adaptation

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