A graph-theoretic approach to real-time IoT fault detection in mixed-mobility networks

Abstract

Maintaining IoT network reliability in mixed-mobility megacities like Ho Chi Minh City (HCMC) presents a formidable challenge due to high stochasticity and complex, non-Euclidean traffic topologies. Traditional monitoring systems predominantly treat sensors as isolated spatial points, frequently failing to differentiate between legitimate traffic congestion and latent cascading sensor anomalies. To address this gap, this paper proposes the Graph-Theoretic Spatiotemporal Transformer (GTST). The core innovation of the GTST framework is the integration of a spectral positional encoding scheme derived from the Laplacian eigenvectors of the transport graph, capturing the network's underlying functional structure. Evaluated using a high-fidelity synthetic dataset comprising 100,000 transactions across 28 urban nodes under extreme stochastic noise, empirical results demonstrate the GTST model prioritizes the mitigation of critical false negatives. It achieves a peak fault recall of 0.96, matching computationally heavy deep-learning baselines with a vastly lower computational footprint. Crucially, the GTST framework accomplishes this with an ultra-low inference time of 0.0061ms per transaction, proving its superior viability and efficiency for real-time edge deployment in noisy urban zones.