Radios as Sensors

Radio receivers, besides acting as wireless network nodes participating to the Internet of Things (IoT) communication task, may act as opportunistic sensors participating to the IoT sensing task. In particular, a radio receiver is intrinsically an electronic sensor which may be used for device-free human activity recognition. In this paper, we analyze recent results on how the identification of the human body presence and movement can be carried out analyzing the RF signals transmitted by sources of opportunity. The impact of channel bandwidth, transmission mode, carrier frequency, and signal descriptors on the recognition performance is discussed. Moreover, we present a novel crowd counting system and assess the performance considering two different types of signal descriptors. Results prove the effectiveness of the presented crowd counting system and allow to get more insights into the relation among the specific sensed environment, chosen signal descriptors, and classification accuracy.

[1]  Daqing Zhang,et al.  Anti-fall: A Non-intrusive and Real-Time Fall Detector Leveraging CSI from Commodity WiFi Devices , 2015, ICOST.

[2]  Gerhard Tröster,et al.  The telepathic phone: Frictionless activity recognition from WiFi-RSSI , 2014, 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[3]  Lu Wang,et al.  FIMD: Fine-grained Device-free Motion Detection , 2012, 2012 IEEE 18th International Conference on Parallel and Distributed Systems.

[4]  Xu Chen,et al.  Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi , 2015, MobiHoc.

[5]  Moustafa Youssef,et al.  Robust WLAN Device-free Passive motion detection , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[6]  Richard P. Martin,et al.  Tracking human queues using single-point signal monitoring , 2014, MobiSys.

[7]  Li Sun,et al.  WiDraw: Enabling Hands-free Drawing in the Air on Commodity WiFi Devices , 2015, MobiCom.

[8]  Shwetak N. Patel,et al.  Whole-home gesture recognition using wireless signals , 2013, MobiCom.

[9]  Wei Wang,et al.  Understanding and Modeling of WiFi Signal Based Human Activity Recognition , 2015, MobiCom.

[10]  Fadel Adib,et al.  Multi-Person Localization via RF Body Reflections , 2015, NSDI.

[11]  Theodore S. Rappaport,et al.  Millimeter Wave Mobile Communications for 5G Cellular: It Will Work! , 2013, IEEE Access.

[12]  Khaled A. Harras,et al.  WiGest demo: A ubiquitous WiFi-based gesture recognition system , 2015, 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[13]  Moustafa Youssef,et al.  RASID: A robust WLAN device-free passive motion detection system , 2011, 2012 IEEE International Conference on Pervasive Computing and Communications.

[14]  Parth H. Pathak,et al.  Analyzing Shopper's Behavior through WiFi Signals , 2015, WPA@MobiSys.

[15]  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.

[16]  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).

[17]  Moustafa Youssef,et al.  Device-Free Radio-based Low Overhead Identification of Subject Classes , 2015, WPA@MobiSys.

[18]  Shyamnath Gollakota,et al.  Bringing Gesture Recognition to All Devices , 2014, NSDI.

[19]  Kaishun Wu,et al.  WiG: WiFi-Based Gesture Recognition System , 2015, 2015 24th International Conference on Computer Communication and Networks (ICCCN).

[20]  Kaishun Wu,et al.  WiFall: Device-free fall detection by wireless networks , 2017, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[21]  Marina Ruggieri,et al.  WIBECAM: Device Free Human Activity Recognition Through WiFi Beacon-Enabled Camera , 2015, WPA@MobiSys.

[22]  Rob Miller,et al.  3D Tracking via Body Radio Reflections , 2014, NSDI.

[23]  Ramjee Prasad,et al.  Wideband indoor channel measurements and BER analysis of frequency selective multipath channels at 2.4, 4.75, and 11.5 GHz , 1996, IEEE Trans. Commun..

[24]  Wen Hu,et al.  Radio-based device-free activity recognition with radio frequency interference , 2015, IPSN.

[25]  Yusheng Ji,et al.  RF-Sensing of Activities from Non-Cooperative Subjects in Device-Free Recognition Systems Using Ambient and Local Signals , 2014, IEEE Transactions on Mobile Computing.

[26]  Jie Yang,et al.  E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures , 2014, MobiCom.

[27]  Xiao Zhang,et al.  FM-based Device-Free Localization and Activity Recognition via Sparse Representation , 2015, CSAR@SenSys.

[28]  Saandeep Depatla,et al.  Occupancy Estimation Using Only WiFi Power Measurements , 2015, IEEE Journal on Selected Areas in Communications.

[29]  Wei Wang,et al.  Keystroke Recognition Using WiFi Signals , 2015, MobiCom.

[30]  Sneha Kumar Kasera,et al.  Monitoring Breathing via Signal Strength in Wireless Networks , 2011, IEEE Transactions on Mobile Computing.

[31]  Shaojie Tang,et al.  Electronic frog eye: Counting crowd using WiFi , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.