Occupancy Counting With Burst and Intermittent Signals in Smart Buildings

Zone-level occupancy counting is a critical technology for smart buildings and can be used for applications, such as building energy management, surveillance, and public safety. Existing occupancy counting techniques typically require installation of large number of occupancy monitoring sensors inside a building and an established wireless network. In this paper, in order to achieve occupancy counting, we consider the use of Wi-Fi probe requests that are continuously transmitted from Wi-Fi enabled smart devices for discovering nearby access points. To this end, Wi-Fi Pineapple equipment are used for passively capturing ambient probe requests from Wi-Fi devices, such as smart phones and tablets, where no connectivity to a Wi-Fi network is required. This information is then used to localize users within coarsely defined occupancy zones, and subsequently to obtain occupancy count within each zone at different time scales. An interacting multimodel (IMM) Kalman filter technique is developed to improve occupancy counting accuracy. Our numerical results using Wi-Fi data collected at a university building show that the use of Wi-Fi probe requests in conjunction with IMM-based Kalman filters can be a viable solution for zone-level occupancy monitoring in smart buildings.

[1]  Paul J. M. Havinga,et al.  RSS-based self-adaptive localization in dynamic environments , 2012, 2012 3rd IEEE International Conference on the Internet of Things.

[2]  Julien Freudiger,et al.  How talkative is your mobile device?: an experimental study of Wi-Fi probe requests , 2015, WISEC.

[3]  Erik G. Ström,et al.  RSS-Based Sensor Localization in the Presence of Unknown Channel Parameters , 2013, IEEE Transactions on Signal Processing.

[4]  Peter Jung,et al.  Scaled Unscented Kalman Filter for RSSI-based Indoor Positioning and Tracking , 2015, 2015 9th International Conference on Next Generation Mobile Applications, Services and Technologies.

[5]  Ismail Güvenç,et al.  IoT-based occupancy monitoring techniques for energy-efficient smart buildings , 2015, 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW).

[6]  Xuemei Guo,et al.  An Exponential-Rayleigh Model for RSS-Based Device-Free Localization and Tracking , 2015, IEEE Transactions on Mobile Computing.

[7]  Zhaohui Wu,et al.  Trace analysis and mining for smart cities: issues, methods, and applications , 2013, IEEE Communications Magazine.

[8]  Kaveh Pahlavan,et al.  Localization Challenges for the Emergence of the Smart World , 2015, IEEE Access.

[9]  Fernando Seco Granja,et al.  Indoor Positioning Using Efficient Map Matching, RSS Measurements, and an Improved Motion Model , 2015, IEEE Transactions on Vehicular Technology.

[10]  Shahrokh Valaee,et al.  Indoor Tracking and Navigation Using Received Signal Strength and Compressive Sensing on a Mobile Device , 2013, IEEE Transactions on Mobile Computing.

[11]  Myungchul Kim,et al.  Probe request based Load Balancing Metric with timely handoffs for WLANs , 2010, 2010 International Conference on Information and Communication Technology Convergence (ICTC).

[12]  Petar M. Djuric,et al.  Indoor Tracking: Theory, Methods, and Technologies , 2015, IEEE Transactions on Vehicular Technology.

[13]  Rajesh Gupta,et al.  Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings , 2013, SenSys '13.

[14]  Sneha A. Dalvi,et al.  Internet of Things for Smart Cities , 2017 .

[15]  Thomas Weng,et al.  Occupancy-driven energy management for smart building automation , 2010, BuildSys '10.

[16]  Arno Solin,et al.  Optimal Filtering with Kalman Filters and Smoothers , 2011 .

[17]  Ismail Guvenc,et al.  Enhancements to RSS Based Indoor Tracking Systems Using Kalman Filters , 2003 .

[18]  Alberto E. Cerpa,et al.  POEM: Power-efficient occupancy-based energy management system , 2013, 2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[19]  William A. Arbaugh,et al.  An empirical analysis of the IEEE 802.11 MAC layer handoff process , 2003, CCRV.

[20]  Chuan Heng Foh,et al.  A practical path loss model for indoor WiFi positioning enhancement , 2007, 2007 6th International Conference on Information, Communications & Signal Processing.

[21]  Pau Closas,et al.  Simultaneous tracking and RSS model calibration by robust filtering , 2014, 2014 48th Asilomar Conference on Signals, Systems and Computers.

[22]  Marimuthu Palaniswami,et al.  An Information Framework for Creating a Smart City Through Internet of Things , 2014, IEEE Internet of Things Journal.

[23]  Ismail Güvenç,et al.  Indoor occupancy tracking in smart buildings using passive sniffing of probe requests , 2016, 2016 IEEE International Conference on Communications Workshops (ICC).

[24]  Ismail Güvenç,et al.  Fundamental limits and improved algorithms for linear least-squares wireless position estimation , 2012, Wirel. Commun. Mob. Comput..

[25]  Erik G. Ström,et al.  Improved Position Estimation Using Hybrid TW-TOA and TDOA in Cooperative Networks , 2012, IEEE Transactions on Signal Processing.

[26]  A. B. M. Musa,et al.  Tracking unmodified smartphones using wi-fi monitors , 2012, SenSys '12.

[27]  Levent Demir Wi-Fi tracking : what about privacy , 2013 .

[28]  Satoshi Tadokoro Smart Building Technology [TC Spotlight] , 2014, IEEE Robotics & Automation Magazine.

[29]  Dmitry Namiot,et al.  On the analysis of statistics of mobile visitors , 2014, Automatic Control and Computer Sciences.

[30]  Hojung Cha,et al.  Smartphone-based Wi-Fi pedestrian-tracking system tolerating the RSS variance problem , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications.

[31]  Kian Meng Yap,et al.  The effect of probe interval estimation on attack detection performance of a WLAN independent intrusion detection system , 2012, ICWCA.

[32]  Roksana Boreli,et al.  I know who you will meet this evening! Linking wireless devices using Wi-Fi probe requests , 2012, 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[33]  Ismail Güvenç,et al.  A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques , 2009, IEEE Communications Surveys & Tutorials.

[34]  Klaus Moessner,et al.  Enabling smart cities through a cognitive management framework for the internet of things , 2013, IEEE Communications Magazine.

[35]  Chansik Park,et al.  Extended Kalman Filter for wireless LAN based indoor positioning , 2008, Decis. Support Syst..