Scalable Representation of RSSIs for Multi-Building and Multi-Floor Indoor Localization Based on Deep Neural Networks

Since the SURF project titled "Indoor localization based on Wi-Fi fingerprinting with deep learning and fuzzy sets" in 2017, we have been investigating large-scale multi-building and multi-floor indoor localization based on a single dataset for received signal strength indicators (RSSIs) and deep neural network (DNN) models for the integrated estimation of building, floor, and location with focus on the scalability of a DNN model and its outputs. In this project, we focus on inputs to a DNN model and study the scalable representation of RSSIs for DNN-based large-scale multi-building and multi-floor indoor localization.

• How can we represent in a scalable way large-dimensional RSSIs (e.g., 520-dimensional vectors in the UJIIndoorLoc database [1]) as inputs to a DNN model for multi-building and multi-floor indoor localization?
• What are the best DNN architectures for scalable representation of RSSIs (e.g., time series representation)?

• Xi'an Jiaotong-Liverpool University Summer Undergraduate Research Fellowships (SURF) programme (under grant SURF-2022076).

• Jun./2022–Aug./2022 (10 weeks)

• 06/27/2022: Kickoff meeting, 2-3 PM, Skype
  • Kyeong Soo (Joseph) Kim, Project overview

1. Alejandro Pasos Ruiz, Michael Flynn, James Large, Matthew Middlehurst, and Anthony Bagnall, "The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances," Data Mining and Knowledge Discussions, vol. 35, no. 2, pp. 401-449, Mar. 2021.
2. Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller, "Deep learning for time series classification: A review," Data Mining and Knowledge Discovery, vol. 33, no. 4, pp. 917-963, Mar. 2019.
3. Zhe Tang, Sihao Li, Kyeong Soo Kim, and Jeremy Smith, "Multi-output Gaussian process-based data augmentation for multi-building and multi-floor indoor localization," accepted for presentation at IEEE Fourth International Workshop on Data Driven Intelligence for Networks and Systems (DDINS) (organized in conjunction with IEEE ICC 2022), Mar. 7, 2022.
4. Abdalla Elesawi and Kyeong Soo Kim, "Hierarchical multi-building and multi-floor indoor localization based on recurrent neural networks," Proc. 2021 Ninth International Symposium on Computing and Networking Workshops (CANDARW 2021), Matsue, Japan, pp. 193-196, Nov. 23-26, 2021.
5. Jaehoon Cha, Kyeong Soo Kim, and Sanghyuk Lee, "Hierarchical auxiliary learning," Machine Learning: Science and Technology, vol. 1, no. 4, pp. 1-11, Sep. 11, 2020.
6. Zhenghang Zhong, Zhe Tang, Xiangxing Li, Tiancheng Yuan, Yang Yang, Wei Meng, Yuanyuan Zhang, Renzhi Sheng, Naomi Grant, Chongfeng Ling, Xintao Huan, Kyeong Soo Kim and Sanghyuk Lee, "XJTLUIndoorLoc: A new fingerprinting database for indoor localization and trajectory estimation based on Wi-Fi RSS and geomagnetic field," Proc. 2018 CANDAR, Takayama, Japan, Nov. 27-30, 2018.
7. Kyeong Soo Kim, "Hybrid building/floor classification and location coordinates regression using a single-input and multi-output deep neural network for large-scale indoor localization based on Wi-Fi fingerprinting," Proc. 2018 CANDAR, Takayama, Japan, Nov. 27-30, 2018.
8. Jaehoon Cha, Sanghyuk Lee, and Kyeong Soo Kim, "Automatic building and floor classification using two consecutive multi-layer perceptron," Proc. ICCAS 2018, Pyeongchang, Korea, Oct. 2018.
9. Kyeong Soo Kim, Sanghyuk Lee, and Kaizhu Huang "A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting," Big Data Analytics, vol. 3, no. 4, pp. 1–17, Apr. 2018.
10. Kyeong Soo Kim, Ruihao Wang, Zhenghang Zhong, Zikun Tan, Haowei Song, Jaehoon Cha, and Sanghyuk Lee, "Large-scale location-aware services in access: Hierarchical building/floor classification and location estimation using Wi-Fi fingerprinting based on deep neural networks," (Extended version of the FOAN 2017 paper) Fiber and Integrated Optics, vol. 37, no. 5, pp. 277–289, Apr. 10, 2018.
11. Kyeong Soo Kim, Ruihao Wang, Zhenghang Zhong, Zikun Tan, Haowei Song, Jaehoon Cha, and Sanghyuk Lee, "Large-scale location-aware services in access: Hierarchical building/floor classification and location estimation using Wi-Fi fingerprinting based on deep neural networks," Proc. FOAN 2017, Munich, Germany, Nov. 7, 2017.
12. J. Torres-Sospedra et al., "UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems," Proc. International Conference on Indoor Positioning and Indoor Navigation (IPIN), Busan, Korea, Oct. 2014, pp. 261–270.
13. P. Bahl and V. N. Padmanabhan, "RADAR: An in-building RF-based user location and tracking system," Proc. 2000 IEEE INFOCOM, vol. 2, 2000, pp. 775–784.

• XJTLU PGRS1912001: Scalable and Energy-Efficient Multi-Building and Multi-Floor Indoor Localisation/Navigation based on Deep Neural Networks with a Multivariate Database
• XJTLU KSF-E-25: Feasibility Study of XJTLU Campus-Wide Indoor Localization System Based on Deep Neural Networks
• XJTLU SURF-201913: Analysis of XJTLUIndoorLoc Multivariate Dataset for DNN-Based Indoor Localization
• XJTLU SURF-201830: Trajectory Estimation of Mobile Users/Devices based on Wi-Fi Fingerprinting and Deep Neural Networks
• XJTLU SURF-201739: Indoor Localisation Based on Wi-Fi Fingerprinting with Fuzzy Sets
• XJTLU RISGC-2017-4: Feasibility Assessment and Roadmap for XJTLU Campus Information and Visitor Service System as A Test Bed for Large-Scale Location-Aware Services in SIP