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In critical infrastructures like airports, much care has to be devoted inprotecting radio communication networks from external electromagneticinterference. Protection of such mission-critical radio communication networksis usually tackled by exploiting radiogoniometers: at least three suitablydeployed radiogoniometers, and a gateway gathering information from them,permit to monitor and localise sources of electromagnetic emissions that arenot supposed to be present in the monitored area. Typically, radiogoniometersare connected to the gateway through relay nodes. As a result, some degree offault-tolerance for the network of relay nodes is essential in order to offer areliable monitoring. On the other hand, deployment of relay nodes is typicallyquite expensive. As a result, we have two conflicting requirements: minimisecosts while guaranteeing a given fault-tolerance. In this paper, we address theproblem of computing a deployment for relay nodes that minimises the relay nodenetwork cost while at the same time guaranteeing proper working of the networkeven when some of the relay nodes (up to a given maximum number) become faulty(fault-tolerance). We show that, by means of a computation-intensivepre-processing on a HPC infrastructure, the above optimisation problem can beencoded as a 0/1 Linear Program, becoming suitable to be approached withstandard Artificial Intelligence reasoners like MILP, PB-SAT, and SMT/OMTsolvers. Our problem formulation enables us to present experimental resultscomparing the performance of these three solving technologies on a real casestudy of a relay node network deployment in areas of the Leonardo da VinciAirport in Rome, Italy.

MILP, pseudo-boolean, and OMT solvers for optimal fault-tolerant placements of relay nodes in mission critical wireless networks