  In critical infrastructures like airports, much care has to be devoted in
protecting radio communication networks from external electromagnetic
interference. Protection of such mission-critical radio communication networks
is usually tackled by exploiting radiogoniometers: at least three suitably
deployed radiogoniometers, and a gateway gathering information from them,
permit to monitor and localise sources of electromagnetic emissions that are
not supposed to be present in the monitored area. Typically, radiogoniometers
are connected to the gateway through relay nodes. As a result, some degree of
fault-tolerance for the network of relay nodes is essential in order to offer a
reliable monitoring. On the other hand, deployment of relay nodes is typically
quite expensive. As a result, we have two conflicting requirements: minimise
costs while guaranteeing a given fault-tolerance. In this paper, we address the
problem of computing a deployment for relay nodes that minimises the relay node
network cost while at the same time guaranteeing proper working of the network
even when some of the relay nodes (up to a given maximum number) become faulty
(fault-tolerance). We show that, by means of a computation-intensive
pre-processing on a HPC infrastructure, the above optimisation problem can be
encoded as a 0/1 Linear Program, becoming suitable to be approached with
standard Artificial Intelligence reasoners like MILP, PB-SAT, and SMT/OMT
solvers. Our problem formulation enables us to present experimental results
comparing the performance of these three solving technologies on a real case
study of a relay node network deployment in areas of the Leonardo da Vinci
Airport in Rome, Italy.
