Edge AI for Emergency Communications in University Industry Innovation Zones

Abstract

University industry innovation zones represent dense socio technical environments where academic campuses, research laboratories, and industrial pilot facilities coexist. Emergency situations within such zones demand communication systems that are responsive, resilient, and capable of operating under partial infrastructure failure. This article proposes an edge artificial intelligence architecture for emergency communication that integrates sensing, localized analytics, and adaptive routing. By relocating decision making closer to data sources, the proposed approach improves alert latency, reliability, and contextual relevance. Architectural modeling, analytical formulation, and experimental evaluation demonstrate measurable gains over centralized systems in multi stakeholder innovation environments.