  In a wireless network that conveys status updates from sources (i.e.,
sensors) to destinations, one of the key issues studied by existing literature
is how to design an optimal source sampling strategy on account of the
communication constraints which are often modeled as queues. In this paper, an
alternative perspective is presented -- a novel status-aware communication
scheme, namely \emph{parallel communications}, is proposed which allows sensors
to be communication-agnostic. Specifically, the proposed scheme can determine,
based on an online prediction functionality, whether a status packet is worth
transmitting considering both the network condition and status prediction, such
that sensors can generate status packets without communication constraints. We
evaluate the proposed scheme on a Software-Defined-Radio (SDR) test platform,
which is integrated with a collaborative autonomous driving simulator, i.e.,
Simulation-of-Urban-Mobility (SUMO), to produce realistic vehicle control
models and road conditions. The results show that with online status
predictions, the channel occupancy is significantly reduced, while guaranteeing
low status recovery error. Then the framework is applied to two scenarios: a
multi-density platooning scenario, and a flight formation control scenario.
Simulation results show that the scheme achieves better performance on the
network level, in terms of keeping the minimum safe distance in both vehicle
platooning and flight control.
