Abstract
Indoor location has become the core part in the large-scale location-aware services, especially in the extendable/scalable applications. Fingerprint location by using the signal strength indicator (RSSI) of the received WiFi signal has the advantages of full coverage and strong expansibility. It also has the disadvantages of requiring data calibration and lacking samples under the dynamic environment. This paper describes a deep neural network method used for indoor positioning (DNNIP) based on stacked auto-encoder and data stratification. The experimental results show that this DNNIP has better classification accuracy than the machine learning algorithms that are based on UJIIndoorLoc dataset.
Keywords
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
Gu, Y., Lo, A., Niemegeers, I.: A survey of indoor positioning systems for wireless personal networks. IEEE Commun. Surv. Tutor. 11(1), 13–32 (2009)
Machaj, J., Brida, P., Majer, N.: Challenges introduced by heterogeneous devices for Wi-Fi-based indoor localization. Concurr. Comput.: Pract. Exp. 32, 1–10 (2019)
Miao, H., Wang, Z., Wang, J., Zhang, L., Zhengfeng, L.: A novel access point selection strategy for indoor location with Wi-Fi. In: China Control and Decision Conference (CCDC), pp. 5260–5265. IEEE, Changsha (2014)
Wang, B., Zhou, S., Yang, L.T., Mo, Y.: Indoor positioning via subarea fingerprinting and surface fitting with received signal strength. Pervasive Mob. Comput. 23, 43–58 (2015)
Lin, T., Fang, S., Tseng, W., Lee, C., Hsieh, J.: A group-discrimination-based access point selection for WLAN fingerprinting localization. IEEE Trans. Veh. Technol. 63(8), 3967–3976 (2014)
Liu, W., Fu, X., Deng, Z., Xu, L., Jiao, J.: Smallest enclosing circle-based fingerprint clustering and modified-WKNN matching algorithm for indoor positioning. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–6. IEEE, Alcala de Henares (2016)
Gharghan, S.K., Nordin, R., Ismail, M., Ali, J.A.: Accurate wireless sensor localization technique based on hybrid PSO-ANN algorithm for indoor and outdoor track cycling. IEEE Sens. J. 16(2), 529–541 (2016)
Turgut, Z., Ustebay, S., Aydin, M.A., Aydin, Z.G., Sertbaş, A.: Performance analysis of machine learning and deep learning classification methods for indoor localization in Internet of things environment. Trans. Emerg. Telecommun. Technol. 30(9), 1–18 (2019)
Ma, Y.-W., Chen, J.-L., Chang, F.-S., Tang, C.-L.: Novel fingerprinting mechanisms for indoor positioning. Int. J. Commun. Syst. 29(3), 638–656 (2016)
Félix, G., Siller, M., Álvarez, E.N.: A fingerprinting indoor localization algorithm based deep learning. In: Eighth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 1006–1011. IEEE, Vienna (2016)
Nowicki, M., Wietrzykowski, J.: Low-effort place recognition with WiFi fingerprints using deep learning. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds.) ICA 2017. AISC, vol. 550, pp. 575–584. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54042-9_57
Zhang, W., Liu, K., Zhang, W.D., Zhang, Y., Gu, J.: Deep neural networks for wireless localization in indoor and outdoor environments. Neurocomputing 194, 279–287 (2016)
Moreira, A., Nicolau, M.J., Meneses, F., Costa, A.: Wi-Fi fingerprinting in the real world- RTLS@UM at the EvAAL competition. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–10. IEEE, Banff (2015)
Torres-Sospedra, J., Montoliu, R., Martinez-Uso, A., et al.: UJIIndoorLoc: a new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 261–270, IEEE, Busan (2014)
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
Zhang, J., Su, Y. (2021). A Deep Neural Network Based on Stacked Auto-encoder and Dataset Stratification in Indoor Location. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12743. Springer, Cham. https://doi.org/10.1007/978-3-030-77964-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-77964-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77963-4
Online ISBN: 978-3-030-77964-1
eBook Packages: Computer ScienceComputer Science (R0)