## 2 Materials and Methods
### Global Context Fusion Strategy  
In fire detection systems, accurate interpretation of dynamic spatiotemporal signals is vital for timely and effective response. However, conventional fusion mechanisms--based on fixed-rule logic or localized sensor inputs--often fail under complex indoor conditions such as smoke occlusion, irregular thermal diffusion, or partial flame exposure. To overcome these challenges, we propose a lightweight and real-time Global Context Fusion (GCF) strategy specifically designed for embedded fire response environments. Recent studies in remote sensing and medical imaging have introduced various global modeling approaches to improve spatial reasoning and context integration. Among these, Swin Transformers employ hierarchical window-shifting mechanisms to capture long-range dependencies across multi-scale contexts [25; 26; 27]. While effective, these models require deep architectures and large-scale training data, which impose significant computational burdens and limit their deployment in resource-constrained platforms. Global Context Pooling (GCP), by contrast, uses spatial pooling to summarize contextual information and improve feature representation in convolutional neural networks. Although computationally efficient, GCP lacks the responsiveness to abrupt localized anomalies such as sudden particulate spikes or heat surges, which are critical in real-time fire scenarios [28; 29]. Other mechanisms such as Criss-Cross Attention and Cross-Shaped Windows focus on axis-aligned dependencies to balance global awareness and efficiency [30]. While these methods reduce computational cost, they may inadequately capture omnidirectional interactions and irregular fire propagation patterns. To address the above limitations, the proposed GCF module introduces a hybrid spatial attention pipeline optimized for low-latency decision-making. This architecture dynamically fuses global thermal trends and smoke diffusion trajectories with local anomalies such as rapid heat rise or smoke particle density, using a sensor-driven encoder to construct real-time spatial representations. Unlike prior Transformer variants, the GCF module operates through event-triggered feature propagation and avoids large receptive fields or directionally restricted fusion. The resulting design ensures minimal inference delay, robustness to partial occlusion, and energy-efficient edge deployment without reliance on cloud computing. Furthermore, its modular structure facilitates integration into intelligent building systems, enabling decentralized actuation and explainable fire response logic. Table 1 presents a comparative overview of the discussed context modeling strategies. While Swin Transformers and Criss-Cross Attention provide varying degrees of contextual reasoning, only the proposed GCF module demonstrates both the responsiveness and deployability required for embedded safety-critical applications. Additionally, recent Transformer-based methods have advanced significantly across remote sensing tasks. Models such as RS-Lambda, GLOTS, and TTST have demonstrated \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Global Context** & **Computational** & **Spatial Fusion Pattern** & **Suitability for Edge** \\  & **Coverage** & **Efficiency** & **Deployment** \\ \hline Swin Transformer & Hierarchical, multi-scale & Moderate–High & Shifted overlapping windows & Moderate \\ Global Context Pooling & Global summary via & High & Omnidirectional (coarse) & High \\ \multirow{2}{*}{Cris-Cross Attention} & long-range & \multirow{2}{*}{Moderate} & \multirow{2}{*}{Horizontal + Vertical stripes} & \multirow{2}{*}{Moderate} \\  & (axis-aligned) & & & \\ \multirow{2}{*}{Proposed GCF Module} & Dynamic & \multirow{2}{*}{Very High} & \multirow{2}{*}{Sensor-driven spatial encoder} & \multirow{2}{*}{High} \\  & local + inferred global & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of global context modeling techniques.