ABSTRACT
Indoor localization is defined as the process of locating a user or device in an indoor environment. It plays a crucial role for first responders in disaster and emergency situations. In situations where the environment does not change much after an incident happens, fingerprint based indoor localization can be used for effective localization and positioning. Due to the growth in smartphone users in the last few years, indoor localization using Wi-Fi fingerprints has been studied by researchers. The measured Wi-Fi signal strength can be used as an indication of the distribution of users in various indoor locations. In disaster and emergency situations, localization services should be highly accurate and fast. We can model localization as a classification problem and address using machine learning (ML) approaches. However, these two requirements are conflicting since an accurate fingerprint-based indoor localization system needs to process a large amount of data, and this leads to slow response. This problem becomes even worse when both the number of floors and the number of reference points increase. To address this challenge, we use a two-level localization in order to improve both the accuracy and the response time. First, the fingerprint database is used to train ML models. The localization phase has two steps: (i) floor prediction, and (ii) reference point prediction on the predicted floor. For floor prediction, we use K-Nearest Neighbors (KNN) classification algorithm. Then we use various ML models such as Random Forest, Decision Tree, and Support Vector Machine. We use a dataset having two files with different floor numbers. Experiment results showed that random forest gives the best accuracy among other ML models. So two-level localization method is more suitable than single level localization in terms of localization accuracy and speed, and thus can be utilized in many applications.
- Abebe Belay Adege, Yirga Yayeh, Getaneh Berie, Hsin-piao Lin, Lei Yen, and Yun Ruei Li. 2018. Indoor localization using K-nearest neighbor and artificial neural network back propagation algorithms. In 2018 27th Wireless and Optical Communication Conference (WOCC). IEEE, 1–2.Google ScholarCross Ref
- Mahnoor Anjum, Muhammad Abdullah Khan, Syed Ali Hassan, Aamir Mahmood, Hassaan Khaliq Qureshi, and Mikael Gidlund. 2020. RSSI fingerprinting-based localization using machine learning in LoRa networks. IEEE Internet of Things Magazine 3, 4 (2020), 53–59.Google ScholarCross Ref
- Junhang Bai, Yongliang Sun, Weixiao Meng, and Cheng Li. 2021. Wi-Fi fingerprint-based indoor mobile user localization using deep learning. Wireless Communications and Mobile Computing 2021 (2021).Google Scholar
- Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Gaël Varoquaux. 2013. API design for machine learning software: experiences from the scikit-learn project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning. 108–122.Google Scholar
- Xi Chen, Hang Li, Chenyi Zhou, Xue Liu, Di Wu, and Gregory Dudek. 2022. Fidora: Robust WiFi-based Indoor Localization via Unsupervised Domain Adaptation. IEEE Internet of Things Journal(2022).Google Scholar
- Amira Chriki, Haifa Touati, and Hichem Snoussi. 2017. SVM-based indoor localization in wireless sensor networks. In 2017 13th international wireless communications and mobile computing conference (IWCMC). IEEE, 1144–1149.Google Scholar
- Alessandro Depari, Alessandra Flammini, Daniela Fogli, and Paola Magrino. 2018. Indoor localization for evacuation management in emergency scenarios. In 2018 Workshop on Metrology for Industry 4.0 and IoT. IEEE, 146–150.Google ScholarCross Ref
- J. Talvitie E.S. Lohan. 2015. "WLAN RSS indoor measurement data", Tampere University of Technology.Google Scholar
- Xu Feng, Khuong An Nguyen, and Zhiyuan Luo. 2022. A survey of deep learning approaches for WiFi-based indoor positioning. Journal of Information and Telecommunication 6, 2(2022), 163–216.Google ScholarCross Ref
- Weipeng Guan, Xin Chen, Mouxiao Huang, Zixuan Liu, Yuxiang Wu, and Yingcong Chen. 2018. High-speed robust dynamic positioning and tracking method based on visual visible light communication using optical flow detection and Bayesian forecast. IEEE Photonics Journal 10, 3 (2018), 1–22.Google ScholarCross Ref
- Amir Haider, Yiqiao Wei, Shuzhi Liu, and Seung-Hoon Hwang. 2019. Pre-and post-processing algorithms with deep learning classifier for Wi-Fi fingerprint-based indoor positioning. Electronics 8, 2 (2019), 195.Google ScholarCross Ref
- Chaur-Heh Hsieh, Jen-Yang Chen, and Bo-Hong Nien. 2019. Deep learning-based indoor localization using received signal strength and channel state information. IEEE access 7(2019), 33256–33267.Google ScholarCross Ref
- Wei Kui, Shiling Mao, Xiaolun Hei, and Fan Li. 2018. Towards accurate indoor localization using channel state information. In 2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). IEEE, 1–2.Google ScholarCross Ref
- Hyo Won Lee, Wha Sook Jeon, and Dong Geun Jeong. 2017. A practical indoor localization scheme for disaster relief. In 2017 IEEE 85th Vehicular Technology Conference (VTC Spring). IEEE, 1–7.Google ScholarCross Ref
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825–2830.Google ScholarDigital Library
- Jose Luis Salazar González, Luis Miguel Soria Morillo, Juan Antonio Álvarez García, Fernando Enríquez, and Antonio Ramon Jimenez Ruiz. 2019. Energy-efficient indoor localization WiFi-fingerprint dataset. https://doi.org/10.21227/49yg-5d21Google ScholarCross Ref
- Hiroaki Santo, Takuya Maekawa, and Yasuyuki Matsushita. 2017. Device-free and privacy preserving indoor positioning using infrared retro-reflection imaging. In 2017 IEEE international conference on pervasive computing and communications (PerCom). IEEE, 141–152.Google ScholarCross Ref
- Navneet Singh, Sangho Choe, and Rajiv Punmiya. 2021. Machine learning based indoor localization using Wi-Fi RSSI fingerprints: an overview. IEEE Access (2021).Google ScholarCross Ref
- Min Yu, Shuyin Yao, Xuan Wu, and Liang Chen. 2022. Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning. Applied Sciences 12, 14 (2022), 7151.Google ScholarCross Ref
- Faheem Zafari, Athanasios Gkelias, and Kin K Leung. 2019. A survey of indoor localization systems and technologies. IEEE Communications Surveys & Tutorials 21, 3 (2019), 2568–2599.Google ScholarCross Ref
Index Terms
- Two Level Wi-Fi Fingerprinting based Indoor Localization using Machine Learning
Recommendations
MIMO CSI-based Super-resolution AoA Estimation for Wi-Fi Indoor Localization
ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and ComputingIndoor localization technology has always been a research hotspot in industry and academia. Indoor localization research using channel state information (CSI) of Wi-Fi signals has also received more and more attention. The existing Angle of Arrival (AoA)...
Indoor human localization with orientation using WiFi fingerprinting
ICUIMC '14: Proceedings of the 8th International Conference on Ubiquitous Information Management and CommunicationLocalization in indoor environment poses a fundamental challenge in ubiquitous computing compared to its well-established GPS-based outdoor environment counterpart. This study investigated the feasibility of a WiFi-based indoor positioning system to ...
Survey on the Indoor Localization Technique of Wi-Fi Access Points
This article describes how indoor localization of Wi-Fi AP (access point) plays an important role in the discovery of illegal indoor Wi-Fi and for the safety inspection of confidential places. There have been many related research results in recent ...
Comments