Abstract
Unsupervised indoor localization has received increasing attention in recent years. It enables automatically learning and recognizing the significant locations from Wi-Fi measurements continuously collected from mobile devices in a user’s daily life, without requiring data annotation from professional staff or users. However, such systems suffer from continuous Wi-Fi collection, which results in a high power consumption of mobile devices. These problems can be addressed through activating Wi-Fi collection when it is necessary and deactivating Wi-Fi collection when “enough” data is collected. By using the acceleration readings from the embedded accelerometer sensor, a motion detection algorithm is implemented for an unsupervised localization system DCCLA (Density-based Clustering Combined Localization Algorithm). The information of motion states (i.e. a mobile device in motion or not in motion) is then used to automatically activate and deactivate the process of Wi-Fi collection, and thus save power. Tests carried out by different users in real-world scenarios show an improved performance of unsupervised indoor localization, in terms of location accuracy and power consumption.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Xu, Y., Lau, S.L., Kusber, R., David, K.: DCCLA: autonomous indoor localization using unsupervised Wi-Fi fingerprinting. In: CONTEXT 2013, Annecy, France (2013)
Mobile, G.: Google Maps for Android. https://www.google.com/intl/en/mobile/maps/. (Accessed 21 May 2014)
“Skyhook,” Skyhook Wireless, Inc. (2013) http://www.skyhookwireless.com/. (Accessed 06 Dec 2013)
Marmasse, N., Schmandt, C.: A user-centered location model. Pers. Ubiquit. Comput. 6(5–6), 318–321 (2002)
Ashbrook, D., Starner, T.: Learning signicant locations and predicting user movement with GPS. In: The 6th International Symposium on Wearable Computers (2002)
Hightower, J., Consolvo, S., LaMarca, A., Smith, I., Hughes, J.: Learning and recognizing the places we go. In: 7th International Conference on Ubiquitous Computing, Venice, Italy (2005)
Kim, D., Kim, Y., Estrin, D., Srivastava, M.: SensLoc: sensing everyday places and paths using less energy. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems (2010)
Ester, E., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA (1996)
Jiang, Y., Pan, X., Li, K., Lv, Q., Dick, R.P., Hannigan, M., Shang, L.: ARIEL: automatic Wi-Fi based room fingerprinting for indoor localization. In: 14th International Conference on Ubiquitous Computing, Pittsburgh, PA, USA (2012)
Dousse, O., Eberle, J., Mertens, M.: Place learning via direct WiFi fingerprint clustering. In: IEEE 13th International Conference on Mobile Data Management, Bengaluru, India (2012)
Woodman, O., Harle, R.: Pedestrian localisation for indoor environments. In: UbiComp 2008 Proceedings of the 10th international conference on Ubiquitous computing (2008)
Jimenez, A., Seco, F., Prieto, C., Guevara, J.: A comparison of pedestrian dead-reckoning algorithms using a low-cost MEMS IMU. In: IEEE International Symposium on Intelligent Signal Processing, 2009, Budapest (2009)
Wu, C., Yang, Z., Liu, Y., Xi, W.: WILL: wireless indoor localization without site survey without site survey. In: INFOCOM, 2012 Proceedings IEEE, Orlando, FL (2012)
Shafer, I., Chang, M.L.: Movement detection for power-efficient smartphone WLAN localization. In: Proceedings of the 13th ACM International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems (2010)
Xu, Y., Lau, S.L., Kusber, R., David, K.: An experimental investigation of indoor localization by unsupervised Wi-Fi signal clustering. In: Future Network and Mobile Summit, Berlin, Germany (2012)
Xu, Y., Kusber, R., David, K.: An enhanced density-based clustering algorithm for the autonomous indoor localization. In: MOBILe Wireless MiddleWARE, Operating Systems and Applications (Mobilware), Bologna, Italy (2013)
Lau, S.L., Xu, Y., David, K.: Novel indoor localisation using an unsupervised Wi-Fi signal clustering method. In: Future Network and Mobile Summit, Warsaw, Poland (2011)
Acknowledgments
This work has been [co-]funded by the Social Link Project within the Loewe Program of Excellence in Research, Hessen, Germany.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xu, Y., Meng, L., David, K. (2015). Unsupervised Indoor Localization with Motion Detection. In: Christiansen, H., Stojanovic, I., Papadopoulos, G. (eds) Modeling and Using Context. CONTEXT 2015. Lecture Notes in Computer Science(), vol 9405. Springer, Cham. https://doi.org/10.1007/978-3-319-25591-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-25591-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25590-3
Online ISBN: 978-3-319-25591-0
eBook Packages: Computer ScienceComputer Science (R0)