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

Paper in proceedings (conference paper)

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