Abstract
The increasing availability of wireless networks inside buildings has opened up numerous opportunities for new innovative smart systems. For a lot of these systems, acquisition of context-sensitive information about attendant people has evolved to a key challenge. Especially the position and distribution of attendants significantly influence the system’s service quality. To meet this challenge, several types of sensor systems have been presented over the last two decades. Most of these systems rely on an active mobile device that has to be carried by the tracked entity. Contrary to the so-called device-based active systems, device-free passive sensing systems are grounded on the idea of detecting, tracking, and identifying attendant people without carrying any active device or to actively taking part in a localization process. In order to obtain information about the position or the distribution of present people, these systems quantify the impact of the physical attendants on radio-frequency signals. Most of device-free systems rely on the existing WiFi infrastructure and device-based active concepts, but here we want to focus on a different approach. In line with our previous research on presence detection with Bluetooth Low Energy beacons, in this paper, we introduce a strategy of using those beacons for a device-free passive human counting system.
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
Notes
- 1.
In this paper, we followed the line of a batch scenario. When using this approach in the context of streaming, it should be considered that the beacons used here transmitted only once a second. In order to reduce the observed time window while receiving a sufficient amount of RSS values, the transmission frequency of the beacons has to be adjusted.
References
Bahl, P., Padmanabhan, V.N.: Radar: an in-building rf-based user location and tracking system. In: Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 775–784 (2000)
Bertuletti, S., Cereatti, A., Della, U., Caldara, M., Galizzi, M.: Indoor distance estimated from bluetooth low energy signal strength: comparison of regression models. In: 2016 IEEE Sensors Applications Symposium (SAS), pp. 1–5 (2016)
Bifet, A., Gavaldà, R., Holmes, G., Pfahringer, B.: Machine Learning for Data Streams with Practical Examples in MOA. MIT Press, Cambridge (2018)
Deak, G., Curran, K., Condell, J.: A survey of active and passive indoor localisation systems. Comput. Commun. 35(16), 1939–1954 (2012)
Faragher, R., Harle, R.: Location fingerprinting with bluetooth low energy beacons. IEEE J. Sel. Areas Commun. 33(11), 2418–2428 (2015)
Faraway, J.: Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. CRC Press, Boca Raton (2006)
Harrell, F.: Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Springer Series in Statistics. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19425-7
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd edn. Springer, New York (2009)
Mainetti, L., Patrono, L., Sergi, I.: A survey on indoor positioning systems. In: 2014 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 111–120 (2014)
Mautz, R.: Indoor positioning technologies. Ph.D. thesis, ETH Zurich (2012)
Mistry, H.P., Mistry, N.H.: Rssi based localization scheme in wireless sensor networks: A survey. In: 2015 Fifth International Conference on Advanced Computing Communication Technologies, pp. 647–652 (2015)
Münch, M., Huffstadt, K., Schleif, F.: Towards a device-free passive presence detection system with bluetooth low energy beacons. In: 27th European Symposium on Artificial Neural Networks (ESANN) (2019)
Oosterlinck, D., Benoit, D.F., Baecke, P., de Weghe, N.V.: Bluetooth tracking of humans in an indoor environment: an application to shopping mall visits. Appl. Geogr. 78, 55–65 (2017)
Pirzada, N., Nayan, M.Y., Hassan, F.S.M.F., Khan, M.A.: Device-free localization technique for indoor detection and tracking of human body: a survey. Procedia Soc. Behav. Sci. 129, 422–429 (2014). 2nd International Conference on Innovation, Management and Technology Research
Priyantha, N.B., Chakraborty, A., Balakrishnan, H.: The cricket location-support system. In: Proceedings of the 6th Annual Intern. Conference on Mobile Computing and Networking. MobiCom 2000, pp. 32–43. ACM, New York (2000)
Sabek, I., Youssef, M.: Multi-entity device-free wlan localization. In: 2012 IEEE Global Communications Conference (GLOBECOM), pp. 2018–2023 (2012)
Schleif, F., Tiño, P.: Indefinite proximity learning: a review. Neural Comput. 27(10), 2039–2096 (2015)
Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)
Stulp, F., Sigaud, O.: Many regression algorithms, one unified model: a review. Neural Networks 69, 60–79 (2015)
Subhan, F., Hasbullah, H., Rozyyev, A., Bakhsh, S.T.: Indoor positioning in bluetooth networks using fingerprinting and lateration approach. In: 2011 International Conference on Information Science and Applications, pp. 1–9 (2011)
Sugino, K., Katayama, S., Niwa, Y., Shiramatsu, S., Ozono, T., Shintani, T.: A bluetooth-based device-free motion detector for a remote elder care support system. In: 2015 IIAI 4th International Congress on Advanced Application Informatics, pp. 91–96 (2015)
Teixeira, T., Dublon, G.: A survey of human-sensing: methods for detecting presence, count, location, track, and identity. ACM Comput. Surv. 5, 59–69 (2010)
Turgut, Z., Aydin, G.Z.G., Sertbas, A.: Indoor localization techniques for smart building environment. Procedia CS 83, 1176–1181 (2016)
Want, R., Hopper, A., Falcão, V., Gibbons, J.: The active badge location system. ACM Trans. Inf. Syst. (TOIS) 10(1), 91–102 (1992)
Woyach, K., Puccinelli, D., Haenggi, M.: Sensorless sensing in wireless networks: implementation and measurements. In: 2006 4th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, pp. 1–8 (2006)
Wu, S., et al.: Survey on prediction algorithms in smart homes. IEEE Internet Things J. 4(3), 636–644 (2017)
Xiao, J., Zhou, Z., Yi, Y., Ni, L.: A survey on wireless indoor localization from the device perspective. ACM Comput. Surv. 49(2), 1–31 (2016)
Youssef, M., Mah, M., Agrawala, A.: Challenges: device-free passive localization for wireless environments. In: Proceedings of the 13th Annual ACM International Conference on Mobile Computing and Networking. MobiCom 2007, pp. 222–229. ACM, New York (2007)
Zanella, A., Bui, N., Castellani, A., Vangelista, L., Zorzi, M.: Internet of things for smart cities. IEEE Internet Things J. 1(1), 22–32 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Münch, M., Schleif, FM. (2019). Device-Free Passive Human Counting with Bluetooth Low Energy Beacons. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_66
Download citation
DOI: https://doi.org/10.1007/978-3-030-20518-8_66
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20517-1
Online ISBN: 978-3-030-20518-8
eBook Packages: Computer ScienceComputer Science (R0)