  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 2D/3D 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).
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.
