Federated Edge-Semantic Learning for Decentralized Resilient Evacuation (FESL-DRE): Simulation Dataset

This dataset supports the research presented in the paper: “Federated Edge-Semantic Learning for Decentralized and Resilient Indoor Evacuation under Dynamic Hazards” The dataset was generated using a simulation framework designed to evaluate decentralized evacuation systems operating under dynamic hazards and partial infrastructure failures. The simulation models an indoor environment as a graph-based structure, where nodes represent spatial locations and edges represent navigable paths. Hazard conditions, such as fire intensity, are dynamically simulated using distance-based decay functions. Crowd density evolves during the simulation based on agent movement and interactions. The dataset is designed to enable reproducibility of experiments involving: decentralized coordination using gossip protocols semantic reasoning for hazard interpretation adaptive evacuation routing resilience analysis under node failures human-centric behavioral modeling本数据集支撑下述论文的相关研究：《面向动态灾害下去中心化韧性室内疏散的联邦边缘语义学习》（Federated Edge-Semantic Learning for Decentralized and Resilient Indoor Evacuation under Dynamic Hazards）。 本数据集由专为评估动态灾害及局部基础设施失效场景下去中心化疏散系统而设计的仿真框架生成。 该仿真将室内环境建模为图结构，节点代表空间位置，边代表可通行路径。灾害状态（如火灾强度）通过基于距离的衰减函数实现动态仿真。仿真过程中，人群密度会随智能体（Agent）的移动与交互发生动态演化。 本数据集旨在支撑可复现以下各类实验： - 基于流言协议（gossip protocols）的去中心化协同 - 面向灾害解读的语义推理 - 自适应疏散路径规划 - 节点失效场景下的韧性分析 - 以人为中心的行为建模