Topology Recoverability Prediction for Ad-Hoc Robot Networks: A Data-Driven Fault-Tolerant Approach
This addresses fault tolerance in ad-hoc robot networks, though it appears incremental as it builds on existing classification approaches with a specific model adaptation.
The paper tackles the problem of predicting whether ad-hoc robot networks can recover from faults without needing to perform resource-intensive topology re-synthesis, by developing a two-pathway Bayesian Gaussian mixture model that successfully predicts topology recoverability compared to existing strategies.
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.