Abstract
There are significant research efforts on indoor positioning technologies as well as GPS-based outdoor localization and navigation. So, there is a requirement for effectively integrating the indoor and outdoor localization services and load the appropriate system depending on the context. Indoor/outdoor context detection can be done ubiquitously through exploiting the smartphone sensors that play the role of interconnecting the two services together. Thus, our contribution in this paper is to propose a ubiquitous indoor/outdoor localization framework that can not only detect the context but can also localize at a finer granularity around such transitional areas, such as the building gates. A combination of sensors is needed for precise positioning around that area. To implement the framework, a data collection application is built for smartphones. Experiments are also conducted based on data collected for several use cases in the university campus. The results indicate that a classification accuracy of 88% could be achieved for indoor–outdoor detection while the average localization error is around 1 m.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Roy, P., Chowdhury, C., Ghosh, D., Bandyopadhyay, S.: Juindoorloc: a ubiquitous framework for smartphone-based indoor localization subject to context and device heterogeneity. Wirel. Pers. Commun. 106(2), 739–762 (2019)
Chenshu, W., Yang, Z., Liu, Y., Xi, W.: Will: wireless indoor localization without site survey. IEEE Trans. Parallel Distrib. Syst. 24(4), 839–848 (2012)
Shtar, G., Shapira, B., Rokach, L.: Clustering wi-fi fingerprints for indoor-outdoor detection. Wirel.Netw. 25(3), 1341–1359 (2019)
Ali, M., ElBatt, T., Youssef, M.: Senseio: realistic ubiquitous indoor outdoor detection system using smartphones. IEEE Sens. J. 18(9), 3684–3693 (2018)
Wang, L., Sommer, L., Zhou, Y., Huang, Y., Wang, J., Riedel, T., Beigl, M.: Neuralio: indoor-outdoor detection via multimodal sensor data fusion on smartphones. Sens. Mater. 32(1), 1–12 (2020)
Saffar, I., Alberi Morel, M.L., Deep Singh, K., Viho, C.: Semi-supervised deep learning-based methods for indoor outdoor detection. In: 2019 IEEE International Conference on Communications (ICC), ICC 2019, pp. 1–7. IEEE (2019)
Canovas, O., Lopez-de Teruel, P.E., Ruiz, A.: Detecting indoor/outdoor places using wifi signals and adaboost. IEEE Sens. J. 17(5), 1443–1453 (2016)
Calderoni, L., Ferrara, M., Franco, A., Maio, D.: Indoor localization in a hospital environment using random forest classifiers. Expert Syst. Appl. 42(1), 125–134 (2015)
Xie, Y., Wang, Y., Nallanathan, A., Wang, L.: An improved k-nearest-neighbor indoor localization method based on spearman distance. IEEE Signal Process. Lett. 23(3), 351–355 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rajak, S., Panja, A.K., Chowdhury, C., Neogy, S. (2021). A Ubiquitous Indoor–Outdoor Detection and Localization Framework for Smartphone Users. In: Hassanien, A.E., Bhattacharyya, S., Chakrabati, S., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1286. Springer, Singapore. https://doi.org/10.1007/978-981-15-9927-9_67
Download citation
DOI: https://doi.org/10.1007/978-981-15-9927-9_67
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-9926-2
Online ISBN: 978-981-15-9927-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)