  Networked sensing refers to the capability of properly orchestrating multiple
sensing terminals to enhance specific figures of merit, e.g., positioning
accuracy or imaging resolution. Regarding radio-based sensing, it is essential
to understand \textit{when} and \textit{how} sensing terminals should be
orchestrated, namely the best cooperation that trades between performance and
cost (e.g., energy consumption, communication overhead, and complexity). This
paper addresses networked sensing from a physics-driven perspective, aiming to
provide a general theoretical benchmark to evaluate its \textit{imaging}
performance bounds and to guide the sensing orchestration accordingly.
Diffraction tomography theory (DTT) is the method to quantify the imaging
resolution of any radio sensing experiment from inspection of its spectral (or
wavenumber) content. In networked sensing, the image formation is based on the
back-projection integral, valid for any network topology and physical
configuration of the terminals. The \textit{wavefield networked sensing} is a
framework in which multiple sensing terminals are orchestrated during the
acquisition process to maximize the imaging quality (resolution and grating
lobes suppression) by pursuing the deceptively simple \textit{wavenumber
tessellation principle}. We discuss all the cooperation possibilities between
sensing terminals and possible killer applications. Remarkably, we show that
the proposed method allows obtaining high-quality images of the environment in
limited bandwidth conditions, leveraging the coherent combination of multiple
multi-static low-resolution images.
