AI Agents in Emergency Response Applications
This addresses the problem of improving AI support for emergency personnel in critical situations, but it appears incremental as it builds on existing agent and 5G technologies.
The paper tackles the challenge of deploying AI systems for emergency response, which require low-latency, reliable analytics and high accuracy on resource-constrained devices, by proposing an agent-based architecture using 5G service-based architecture.
Emergency personnel respond to various situations ranging from fire, medical, hazardous materials, industrial accidents, to natural disasters. Situations such as natural disasters or terrorist acts require a multifaceted response of firefighters, paramedics, hazmat teams, and other agencies. Engineering AI systems that aid emergency personnel proves to be a difficult system engineering problem. Mission-critical "edge AI" situations require low-latency, reliable analytics. To further add complexity, a high degree of model accuracy is required when lives are at stake, creating a need for the deployment of highly accurate, however computationally intensive models to resource-constrained devices. To address all these issues, we propose an agent-based architecture for deployment of AI agents via 5G service-based architecture.