Topology Recoverability Prediction for Ad-Hoc Robot Networks: A
  Data-Driven Fault-Tolerant Approach
Matin Macktoobian Zhan Shu Qing Zhao
Faults occurring in ad-hoc robot networks may fatally perturb their
topologies leading to disconnection of subsets of those networks. Optimal
topology synthesis is generally resource-intensive and time-consuming to be
done in real time for large ad-hoc robot networks. One should only perform
topology re-computations if the probability of topology recoverability after
the occurrence of any fault surpasses that of its irrecoverability. We
formulate this problem as a binary classification problem. Then, we develop a
two-pathway data-driven model based on Bayesian Gaussian mixture models that
predicts the solution to a typical problem by two different pre-fault and
post-fault prediction pathways. The results, obtained by the integration of the
predictions of those pathways, clearly indicate the success of our model in
solving the topology (ir)recoverability prediction problem compared to the best
of current strategies found in the literature.