A graph-based evaluation framework for smart disaster response systems using IoT design quality metrics.

Disaster-response systems have been progressively installed in smart cities through IoT-enabled systems that assist in early warning, situational awareness, and coordination of emergencies. Nevertheless, the majority of existing studies measure such systems by means of data analytics, accuracy of prediction, or anomaly detection by AI, and the quality of the system-level design is not quantified in most cases. Specifically, no standardized framework exists to evaluate the complexity of architecture, resilience, recoverability, and reusability of IoT disaster-response systems before their implementation. The paper presents a graph-based design evaluation model, which represents the IoT disaster-response architectures as layered graphs and suggests a collection of quantitative design quality measures. In contrast to the learning-based approaches, the suggested framework is also algorithm-free and assesses the structure of the system, which can be compared objectively among the various IoT system setups. The framework is validated through a flood-response case study conducted in Pakistan where changes in sensor density, gateway connectivity, and interactions on decision-layers are examined. Evidence indicates that network redundancy is a validated variable that minimizes the complexity of detection and increases system recoverability, whereas sparse connectivity enhances the severity propagation and response latency. The developed framework offers a viable pre-deployment assessment tool, which augments AI-based monitoring systems and that will aid in robustly designed disaster-response systems.