AI Agents in Emergency Response Applications

TL;DR

This paper proposes an agent-based AI system architecture utilizing 5G to support emergency response operations, addressing challenges of low latency, high accuracy, and resource constraints in critical situations.

Contribution

It introduces a novel agent-based architecture for deploying AI agents in emergency response, leveraging 5G service-based architecture for improved performance.

Findings

Enhanced low-latency communication via 5G

Improved deployment of accurate AI models on resource-constrained devices

Framework supports diverse emergency scenarios

Abstract

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.