NeuroGraphHeal: A Simulation Framework for Evaluating Neuromorphic Detection and Graph Learning in Underwater IoT Fault Recovery

Abstract

Spiking neural networks (SNNs) and graph neural networks (GNNs) are candidate components for fault detection and recovery in underwater IoT networks. This paper builds a simulated recovery environment with a pre-specified validity gate and tests, in it, whether an SNN detector or GNN-based criticality improves a greedy recovery policy over classical alternative. A synthetic 30-node network with hidden node degradation, decoy disturbances, link faults and a budget of three backups was checked against ten pre-specified conditions on 100 validation episodes. Detectors (threshold, logistic regression, SNN, size-matched dense network, GRU) and a two-layer GNN were then compared on 300 test episodes with paired bootstrap intervals. The environment failed its gate in both runs: the best classical greedy captured 82.9 percent of oracle headroom in Run 1 (limit 80 percent), and a permuted-window negative control captured 13.6 percent in Run 2 (limit 10 percent). In the exploratory tests, greedy control with the SNN detector earned 11.78 less reward per episode than with the logistic detector (95 percent confidence interval 6.33 to 17.23 lower), and a size-matched dense network and a GRU were worse by similar margins. GNN criticality earned 8.84 less than live static criticality (interval 4.65 to 13.79 lower), although the GNN predicted isolation with an AUROC of 0.946. No improvement from the SNN or GNN was found, and the gate failure limits what these comparisons show. The contribution is the documented framework and an account of why its validity gate failed.