Abstract
Indoor smartphone positioning is one of the key ICT techniques. Generally, the development and implementation of an indoor positioning system heavily relies on the technology of wireless sensor network (WSN) since wireless sensors can estimate the probable distance between radio source and the sensors themselves by evaluating the strengths of wireless signals, received from the radio source, such as RSSIs of Wi-Fi or Bluetooth. However, the wireless signals could be influenced by indoor and outdoor objects, and carrying mode of a user’s smartphone, like in-pocket and in-backpack. Therefore, this study proposes an indoor positioning scheme, named LEarning-based Indoor Positing System (LEIPS), which identifies the carrying mode of a user’s smartphone by using this smartphone’s inertial sensors, aiming to increase the positioning accuracy of indoor positioning. Deep learning algorithms are also deployed by the LEIPS to improve the prediction performance. The experimental results demonstrate that this system can reach 99% of positioning accuracy. In the meantime, carrying mode information is validated to show that it is able to improve the accuracy of a positioning system.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Wang, J., Ghosh, R., Das, S.K.: A survey on sensor localization. J. Control Theory Appl. 8, 2–11 (2010)
He, S., Chan, S.-H.G.: Wi-Fi fingerprint-based indoor positioning: recent advances and comparisons. IEEE Commun. Surv. Tutor. 18, 466–490 (2016)
Wang, X., Gao, L., Mao, S., Pandey, S.: CSI-based fingerprinting for indoor localization: a deep learning approach. IEEE Trans. Veh. Technol. 66, 763–776 (2017)
Faragher, R., Harle, R.: An analysis of the accuracy of bluetooth low energy for indoor positioning applications, vol. 1, pp. 201–210 (2014)
Gan, X., Yu, B., Huang, L., Li, Y.: Deep learning for weights training and indoor positioning using multi-sensor fingerprint. In: 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–7 (2017)
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A.W.M., van Ginneken, B., Sánchez, C.I.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Sahiner, B., Chan, H.-P., Petrick, N., Wei, D., Helvie, M.A., Adler, D.D., Goodsitt, M.M.: Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans. Med. Imaging 15, 598–610 (1996)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, vol. 1, pp. 1097–1105. Curran Associates Inc., Lake Tahoe, (2012)
Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8, 98–113 (1997)
Sak, H., Senior, A., Rao, K., Beaufays, F.: Fast and accurate recurrent neural network acoustic models for speech recognition. ArXiv150706947 Cs Stat. (2015)
Mautz, R., Tilch, S.: Survey of optical indoor positioning systems. In: 2011 International Conference on Indoor Positioning and Indoor Navigation, pp. 1–7 (2011)
Koyuncu, H., Yang, S.-H.: A Survey of indoor positioning and object locating systems. Presented at the (2010)
Radu, V., Marina, M.K.: HiMLoc: indoor smartphone localization via activity aware Pedestrian Dead Reckoning with selective crowdsourced WiFi fingerprinting. In: International Conference on Indoor Positioning and Indoor Navigation, pp. 1–10 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, SY., Leu, FY., Ko, CY., Shih, MC., Tang, WJ. (2021). A Hybrid Information-Based Smartphone Indoor-Position Approach. In: Barolli, L., Poniszewska-Maranda, A., Park, H. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2020. Advances in Intelligent Systems and Computing, vol 1195. Springer, Cham. https://doi.org/10.1007/978-3-030-50399-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-50399-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50398-7
Online ISBN: 978-3-030-50399-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)