Device-Free Passive Human Counting with Bluetooth Low Energy Beacons

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.

[1]  Moustafa Youssef,et al.  Multi-entity device-free WLAN localization , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[2]  Robert Harle,et al.  Location Fingerprinting With Bluetooth Low Energy Beacons , 2015, IEEE Journal on Selected Areas in Communications.

[3]  Andrea Zanella,et al.  Internet of Things for Smart Cities , 2014, IEEE Internet of Things Journal.

[4]  Frank-Michael Schleif,et al.  Towards a device-free passive presence detection system with Bluetooth Low Energy beacons , 2019, ESANN.

[5]  Moustafa Youssef,et al.  CoSDEO 2016 Keynote: A decade later — Challenges: Device-free passive localization for wireless environments , 2007, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[6]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[7]  Olivier Sigaud,et al.  Many regression algorithms, one unified model: A review , 2015, Neural Networks.

[8]  Nital H. Mistry,et al.  RSSI Based Localization Scheme in Wireless Sensor Networks: A Survey , 2015, 2015 Fifth International Conference on Advanced Computing & Communication Technologies.

[9]  Albert Bifet,et al.  MACHINE LEARNING FOR DATA STREAMS , 2018 .

[10]  Peter Tiño,et al.  Indefinite Proximity Learning: A Review , 2015, Neural Computation.

[11]  Michele Caldara,et al.  Indoor distance estimated from Bluetooth Low Energy signal strength: Comparison of regression models , 2016, 2016 IEEE Sensors Applications Symposium (SAS).

[12]  Sheikh Tahir Bakhsh,et al.  Indoor positioning in Bluetooth networks using fingerprinting and lateration approach , 2011, 2011 International Conference on Information Science and Applications.

[13]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[14]  Kevin Curran,et al.  A survey of active and passive indoor localisation systems , 2012, Comput. Commun..

[15]  Luca Mainetti,et al.  A survey on indoor positioning systems , 2014, 2014 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM).

[16]  Zeynep Turgut,et al.  Indoor Localization Techniques for Smart Building Environment , 2016, ANT/SEIT.

[17]  Toramatsu Shintani,et al.  A Bluetooth-Based Device-Free Motion Detector for a Remote Elder Care Support System , 2015, 2015 IIAI 4th International Congress on Advanced Applied Informatics.

[18]  Nico Van de Weghe,et al.  Bluetooth tracking of humans in an indoor environment: An application to shopping mall visits , 2017 .

[19]  Frank E. Harrell,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2001 .

[20]  Albert Y. Zomaya,et al.  A Survey of Mobile Device Virtualization , 2016, ACM Comput. Surv..

[21]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[22]  J. Faraway Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models , 2005 .

[23]  R. Mautz Indoor Positioning Technologies , 2012 .

[24]  Lionel M. Ni,et al.  A Survey on Wireless Indoor Localization from the Device Perspective , 2016, ACM Comput. Surv..

[25]  Muhammad Amir Khan,et al.  Device-free Localization Technique for Indoor Detection and Tracking of Human Body: A Survey , 2014 .

[26]  K. Woyach,et al.  Sensorless Sensing in Wireless Networks: Implementation and Measurements , 2006, 2006 4th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks.

[27]  Junhong Xu,et al.  Survey on Prediction Algorithms in Smart Homes , 2017, IEEE Internet of Things Journal.

[28]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[29]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[30]  Andy Hopper,et al.  The active badge location system , 1992, TOIS.