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.<br>
<br><pre>@article{Savazzi_19_usestraywireless,
  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
  		  fidelity}
}
</pre>