  The rapid development of Industrial Internet of Things (IIoT) technologies
has not only enabled new applications, but also presented new challenges for
reliable communication with limited resources. In this work, we define a
deceptively simple novel problem that can arise in these scenarios, in which a
set of sensors need to communicate a joint observation. This observation is
shared by a random subset of the nodes, which need to propagate it to the rest
of the network, but coordination is complex: as signaling constraints require
the use of random access schemes over shared channels, each sensor needs to
implicitly coordinate with others with the same observation, so that at least
one of the transmissions gets through without collisions. Unlike existing
medium access control schemes, the goal here is not to maximize total goodput,
but rather to make sure that the shared message gets through, regardless of the
sender. The lack of any signaling, aside from an acknowledgment or lack thereof
from the rest of the network, makes determining the optimal collective
transmission strategy a significant challenge. We analyze this coordination
problem theoretically, prove its hardness, and provide low-complexity
solutions. While a low-complexity clustering-based approach is shown to provide
near-optimal performance in certain special cases, for the general scenarios,
we model each sensor as a multi-armed bandit (MAB), and provide a
learning-based solution. Numerical results show the effectiveness of this
approach in a variety of cases.
