A Cloud-IoT Platform for Passive Radio Sensing: Challenges and Application Case Studies

We propose a platform for the integration of passive radio sensing and vision technologies into a cloud-IoT framework that performs real-time channel quality information (CQI) time series processing and analytics. Radio sensing and vision technologies allow to passively detect and track objects or persons by using radio waves as probe signals that encode a 2-D/3-D view of the environment they propagate through. View reconstruction from the received radio signals, or CQI, is based on real-time data processing tools, that combine multiple radio measurements from possibly heterogeneous IoT networks. The proposed platform is designed to efficiently store and analyze CQI time series of different types and provides formal semantics for CQI data manipulation-ontology models (OMs). Post-processed data can be then accessible to third parties via JSON-REST calls. Finally, the proposed system supports the reconfiguration of CQI data collection based on the respective application. The performance of the proposed tools are evaluated through two experimental case studies that focus on assisted living applications in a smart-space environment and on driver behavior recognition for in-car control services. Both studies adopt and compare different CQI manipulation models and radio devices as supported by current and future (5G) standards.

[1]  Wei Wang,et al.  Composable IO: A Novel Resource Sharing Platform in Personal Clouds , 2009, CloudCom.

[2]  Stephan Sigg,et al.  Smart City Environmental Perception from Ambient Cellular Signals , 2017, ICA3PP.

[3]  Mohammed Ismail,et al.  Ultra-Low Power, Secure IoT Platform for Predicting Cardiovascular Diseases , 2017, IEEE Transactions on Circuits and Systems I: Regular Papers.

[4]  Ossi Kaltiokallio,et al.  ARTI: An Adaptive Radio Tomographic Imaging System , 2017, IEEE Transactions on Vehicular Technology.

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

[6]  M. Hemalatha,et al.  Link Quality Estimation for Adaptive Data Streaming in WSN , 2017, Wirel. Pers. Commun..

[7]  Kaishun Wu,et al.  We Can Hear You with Wi-Fi! , 2014, IEEE Transactions on Mobile Computing.

[8]  Kevin Curran,et al.  Detection of multi-occupancy using device-free passive localisation , 2014, IET Wirel. Sens. Syst..

[9]  Kazuaki Maeda,et al.  Performance evaluation of object serialization libraries in XML, JSON and binary formats , 2012, 2012 Second International Conference on Digital Information and Communication Technology and it's Applications (DICTAP).

[10]  J. Burgoon,et al.  Nonverbal Communication , 2018, Encyclopedia of Evolutionary Psychological Science.

[11]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[12]  Arslan Munir,et al.  IFCIoT: Integrated Fog Cloud IoT: A novel architectural paradigm for the future Internet of Things. , 2017, IEEE Consumer Electronics Magazine.

[13]  Niall Twomey,et al.  SPHERE: A sensor platform for healthcare in a residential environment , 2017 .

[14]  Xiaohui Xie,et al.  Robust and Passive Motion Detection with COTS WiFi Devices , 2017 .

[15]  F. Richard Yu,et al.  Enhancing QoE-Aware Wireless Edge Caching With Software-Defined Wireless Networks , 2017, IEEE Transactions on Wireless Communications.

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

[17]  Xiang Li,et al.  Device-Free WiFi Human Sensing: From Pattern-Based to Model-Based Approaches , 2017, IEEE Communications Magazine.

[18]  Frédo Durand,et al.  Capturing the human figure through a wall , 2015, ACM Trans. Graph..

[19]  Deepak Puthal,et al.  Big-Sensing-Data Curation for the Cloud is Coming: A Promise of Scalable Cloud-Data-Center Mitigation for Next-Generation IoT and Wireless Sensor Networks , 2017, IEEE Consumer Electronics Magazine.

[20]  Umberto Spagnolini,et al.  Is someone moving around my cell-phone? Tracing cellular signals for passive motion detection , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[21]  Sozo Inoue,et al.  Towards pervasive geospatial affect perception , 2017, GeoInformatica.

[22]  M. Shamim Hossain,et al.  A Survey on Sensor-Cloud: Architecture, Applications, and Approaches , 2013, Int. J. Distributed Sens. Networks.

[23]  Mauro De Sanctis,et al.  Trained-once device-free crowd counting and occupancy estimation using WiFi: A Doppler spectrum based approach , 2016, 2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[24]  Vittorio Rampa,et al.  Device-Free RF Human Body Fall Detection and Localization in Industrial Workplaces , 2017, IEEE Internet of Things Journal.

[25]  Joerg Swetina,et al.  Toward a standardized common M2M service layer platform: Introduction to oneM2M , 2014, IEEE Wireless Communications.

[26]  Umberto Spagnolini,et al.  Device-Free Radio Vision for Assisted Living: Leveraging wireless channel quality information for human sensing , 2016, IEEE Signal Processing Magazine.

[27]  Moustafa Youssef,et al.  Ichnaea: A Low-Overhead Robust WLAN Device-Free Passive Localization System , 2014, IEEE Journal of Selected Topics in Signal Processing.

[28]  Vlad Trifa,et al.  Towards the Web of Things: Web Mashups for Embedded Devices , 2009 .

[29]  Luca Mainetti,et al.  A Software Architecture Enabling the Web of Things , 2015, IEEE Internet of Things Journal.

[30]  Ivan Poupyrev,et al.  Soli , 2016, ACM Trans. Graph..

[31]  Yang Xu,et al.  WiFinger: talk to your smart devices with finger-grained gesture , 2016, UbiComp.

[32]  Hans-Peter Kriegel,et al.  Subspace clustering , 2012, WIREs Data Mining Knowl. Discov..

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

[34]  Ana Juan Ferrer,et al.  Multi-cloud Platform-as-a-service Model, Functionalities and Approaches , 2016, Cloud Forward.

[35]  Mianxiong Dong,et al.  Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing , 2018, IEEE Network.

[36]  Neal Patwari,et al.  See-Through Walls: Motion Tracking Using Variance-Based Radio Tomography Networks , 2011, IEEE Transactions on Mobile Computing.

[37]  Mahdi Ben Alaya,et al.  Toward semantic interoperability in oneM2M architecture , 2015, IEEE Communications Magazine.

[38]  Ernestina Cianca,et al.  Radios as Sensors , 2017, IEEE Internet of Things Journal.

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

[40]  Yusheng Ji,et al.  SenseWaves: Radiowaves for context recognition , 2011 .

[41]  Umberto Spagnolini,et al.  Wireless Cloud Networks for the Factory of Things: Connectivity Modeling and Layout Design , 2014, IEEE Internet of Things Journal.

[42]  Fadel Adib,et al.  Emotion recognition using wireless signals , 2016, MobiCom.

[43]  Yonghe Liu,et al.  MAIS: Multiple Activity Identification System Using Channel State Information of WiFi Signals , 2017, WASA.

[44]  Friedemann Reinhard,et al.  Holography of Wi-fi Radiation. , 2017, Physical review letters.

[45]  Vittorio Rampa,et al.  Tracking of frequency selectivity for device-free detection of multiple targets , 2017, 2017 IEEE International Conference on Communications Workshops (ICC Workshops).

[46]  Kaishun Wu,et al.  GRfid: A Device-Free RFID-Based Gesture Recognition System , 2017, IEEE Transactions on Mobile Computing.

[47]  Wei Wang,et al.  Gait recognition using wifi signals , 2016, UbiComp.

[48]  Moe Z. Win,et al.  Device-Free Counting via Wideband Signals , 2017, IEEE Journal on Selected Areas in Communications.

[49]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.

[50]  Michèle Basseville,et al.  Detection of abrupt changes , 1993 .

[51]  Monica Nicoli,et al.  Pre-deployment performance assessment of device-free radio localization systems , 2016, 2016 IEEE International Conference on Communications Workshops (ICC).

[52]  Rajkumar Buyya,et al.  A survey on vehicular cloud computing , 2014, J. Netw. Comput. Appl..

[53]  Shahrokh Valaee,et al.  A Survey on Behavior Recognition Using WiFi Channel State Information , 2017, IEEE Communications Magazine.

[54]  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.

[55]  Shih-Hau Fang,et al.  Channel State Reconstruction Using Multilevel Discrete Wavelet Transform for Improved Fingerprinting-Based Indoor Localization , 2016, IEEE Sensors Journal.

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

[57]  Mario Gerla,et al.  Vehicular Cloud Computing , 2012, 2012 The 11th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net).

[58]  Yusheng Ji,et al.  Activity recognition from radio frequency data: Multi-stage recognition and features , 2012, 2012 IEEE Vehicular Technology Conference (VTC Fall).

[59]  Yusheng Ji,et al.  Accurate Location Tracking From CSI-Based Passive Device-Free Probabilistic Fingerprinting , 2018, IEEE Transactions on Vehicular Technology.

[60]  Ju Wang,et al.  FitLoc: Fine-Grained and Low-Cost Device-Free Localization for Multiple Targets Over Various Areas , 2017, IEEE/ACM Transactions on Networking.