Zero-Calibration Device-Free Localization for the IoT Based on Participatory Sensing

Device-free localization (DFL) is an emerging technology for estimating the position of a human or object that is not equipped with any electronic tag, nor participate actively in the localization process. Similar to device-based localization, the initial phase in DFL is to build the fingerprint database which is usually done manually using site surveying. This process is tedious, time-consuming, and vulnerable to environmental dynamics. Motivated by the recent advances in the Internet of Things (IoT), this paper introduces RadioGrapher; a system that automates the process of device-free fingerprint calibration in IoT environments. RadioGrapher leverages the device-based locations of entities in the area of interest in a crowd-sensing manner, aided with Fresnel zones of the wirelessly connected IoT devices to automatically construct a device-free fingerprint. Experimental evaluation of RadioGrapher in an IoT testbed using multiple entities shows that it can construct DFL fingerprints with high accuracy. Moreover, its median localization accuracy is comparable to that of manual fingerprinting. This comes with no calibration overhead, highlighting the promise of RadioGrapher as a crowdsourcing device-free fingerprint constructor in IoT environments.

[1]  William Henry Besant.  Conic sections, treated geometrically , 2008 .

[2]  Moustafa Youssef,et al.  Multivariate analysis for probabilistic WLAN location determination systems , 2005, The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services.

[3]  J. Clegg,et al.  Radio frequency electromagnetic fields in large conducting enclosures: effects of apertures and human bodies on propagation and field-statistics , 2006, IEEE Transactions on Electromagnetic Compatibility.

[4]  Moustafa Youssef,et al.  The Tale of Two Localization Technologies: Enabling Accurate Low-Overhead WiFi-based Localization for Low-end Phones , 2017, SIGSPATIAL/GIS.

[5]  Athanasios V. Vasilakos,et al.  ACE: An Accurate and Efficient Multi-Entity Device-Free WLAN Localization System , 2012, IEEE Transactions on Mobile Computing.

[6]  Mark A Beach,et al.  An experimental investigation of the impact of human shadowing on temporal variation of broadband indoor radio channel characteristics and system performance , 2000, Vehicular Technology Conference Fall 2000. IEEE VTS Fall VTC2000. 52nd Vehicular Technology Conference (Cat. No.00CH37152).

[7]  Wei Wang,et al.  RSS Distribution-Based Passive Localization and Its Application in Sensor Networks , 2016, IEEE Transactions on Wireless Communications.

[8]  Moustafa Youssef,et al.  Dejavu: an accurate energy-efficient outdoor localization system , 2013, SIGSPATIAL/GIS.

[9]  Moustafa Youssef,et al.  CrowdInside: automatic construction of indoor floorplans , 2012, SIGSPATIAL/GIS.

[10]  Moustafa Youssef,et al.  Analysis of a Device-Free Passive Tracking System in Typical Wireless Environments , 2009, 2009 3rd International Conference on New Technologies, Mobility and Security.

[11]  Moustafa Youssef,et al.  MonoPHY: Mono-stream-based device-free WLAN localization via physical layer information , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[12]  Dan Wu,et al.  Human respiration detection with commodity wifi devices: do user location and body orientation matter? , 2016, UbiComp.

[13]  Moustafa Youssef,et al.  Smart cevices for smart environments: Device-free passive detection in real environments , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

[14]  Ashok K. Agrawala,et al.  LOCATION-CLUSTERING TECHNIQUES FOR WLAN LOCATION DETERMINATION SYSTEMS , 2006 .

[15]  Neal Patwari,et al.  Radio Tomographic Imaging with Wireless Networks , 2010, IEEE Transactions on Mobile Computing.

[16]  Moustafa Youssef,et al.  A Robust Zero-Calibration RF-Based Localization System for Realistic Environments , 2016, 2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[17]  Moustafa Youssef,et al.  Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments , 2009, IEEE Transactions on Mobile Computing.

[18]  Jun Li,et al.  Improving RF-based device-free passive localization in cluttered indoor environments through probabilistic classification methods , 2012, 2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN).

[19]  Jiro Katto,et al.  A Study on Passive Crowd Density Estimation using Wireless Sensors , 2008 .

[20]  Hristo D. Hristov,et al.  Fresnal Zones in Wireless Links, Zone Plate Lenses and Antennas , 2000 .

[21]  Xiao Zhang,et al.  Device-Free Wireless Localization and Activity Recognition: A Deep Learning Approach , 2017, IEEE Transactions on Vehicular Technology.

[22]  Hirozumi Yamaguchi,et al.  TransitLabel: A Crowd-Sensing System for Automatic Labeling of Transit Stations Semantics , 2016, MobiSys.

[23]  Moustafa Youssef,et al.  SemSense: Automatic construction of semantic indoor floorplans , 2015, 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[24]  Ning An,et al.  SCPL: indoor device-free multi-subject counting and localization using radio signal strength , 2013, IPSN.

[25]  Moustafa Youssef,et al.  AROMA: automatic generation of radio maps for localization systems , 2010, WiNTECH '11.

[26]  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).