Transformative techniques to capture and process wireless stray radiation originated from different radio sources are gaining increasing attention. They can be applied to human sensing, behavior recognition, localization and mapping. The omnipresent radio-frequency (RF) stray radiation of wireless devices (WiFi, Cellular or any Personal/Body Area Network) encodes a 3D view of all objects traversed by its propagation. A trained machine learning model is then applied to features extracted in real-time from radio signals to isolate body-induced footprints or environmental alterations. The technology can augment and transform existing radio-devices into ubiquitously distributed sensors that simultaneously act as wireless transmitters and receivers (e.g. fast time-multiplexed). Thereby, 5G-empowered tiny device networks transform into a dense web of RF-imaging links that extract a view of an environment, for instance, to monitor manufacturing processes in next generation industrial set-ups (Industry 4.0, I4.0). This article highlights emerging transformative computing tools for radio sensing, promotes key technology enablers in 5G communication and reports deployment experiences.

  author = {Savazzi, Stefano and Sigg, Stephan and Vicentini, Federico and Kianoush, Sanaz and Findling, Rainhard},
  journal = {IEEE Computer, Special Issue on Transformative Computing
  		  and Communication},
  title = {On the use of stray wireless signals for sensing: a look
  		  beyond 5G for the next generation industry},
  year = {2019},
  month = jul,
  number = {7},
  pages = {25-36},
  volume = {52},
  keywords = {Sensors, Antenna arrays, Radio frequency, Convolution, 5G
  		  mobile communication, Feature extraction, Wireless