Accelerating Smart City Resiliency: AI-Agent Approach to Isotope Detection Development

Stať ve sborníku v databázi WoS či Scopus

en

Urban resilience requires rapid deployment of monitoring systems capable of detecting radiological threats in transport networks without disrupting normal operations. This paper presents a multi-agent AI framework designed to accelerate the development and integration of isotope detection systems within smart city ecosystems. The proposed framework employs a hierarchical orchestrator-executor architecture in which specialized AI agents collaborate to generate synthetic training data via GEANT4 Monte Carlo simulations of NaI(TI) scintillation spectra, automate the development workflow including script generation, test-driven development, and result aggregation, and prepare a data foundation for deploying classification models at urban transport checkpoints. The modular architecture with a TCP API interface enables seamless integration with existing smart city platforms and scales for metropolitan deployment. This work demonstrates that heterogeneous LLM-based multi-agent orchestration substantially accelerates radiological simulation pipelines and establishes the direction for autonomous radiological monitoring at smart city transport nodes.

IoT sensor networks | isotope detection | Monte Carlo simulation | multi-agent systems | radiation monitoring | smart city resilience

2026-06-10

Institute of Electrical and Electronics Engineers Inc.

979-8-3315-4640-3

Proceedings Smart City Symposium Prague Scsp

2026

1–6

6