[Global Livingry Governance Architecture | Part 11]: Volcanic Monitoring and Early Warning Architecture: Integration of Community Camera Networks, Edge AI, and Tensor Stress Indices (TSI_{t+1})

Abstract

Active volcanic regions require a fundamental shift from manual, expert-committee-dependent response models toward closed-loop automated architectures. This study proposes an integrated early warning framework that combines the Tensor Stress Index (TSI_{t+1}) function, edge computing (Edge AI), and distributed community-based observation networks. By utilizing consumer and fixed surveillance cameras as edge nodes within a micro-sandbox tier, the system achieves continuous 24/7 baseline observation at a fraction of traditional infrastructure costs. When multivariate geological indicators—including magma reservoir pressure (P), surface deformation (\epsilon), micro-seismic frequency (S), and gas emission rates (G)—drive the TSI_{t+1} function beyond safety tolerance thresholds, the architecture autonomously flags anomalies, triggers real-time visual verification via low-latency mobile interfaces, and coordinates rapid evacuation routes. Furthermore, the framework anchors operational management into the Local Ecological Restoration Credit (LERC) economic structure, leveraging formally verified smart contracts for transparent resource allocation and automated financial disbursement without administrative bottlenecks.