A Conceptual Framework for Integrating Small Language Models into Edge Gateways to Enable Context-Aware and Efficient Cold Chain Data Processing

Cold chain logistics is crucial for preserving the quality and safety of temperature-sensitive products across sectors such as food and pharmaceuticals. However, current solutions often rely on cloud-based analytics, leading to network congestion, higher latency, and limited real-time responsiveness. Although edge computing has emerged as a promising alternative, the integration of small language models (SLMs) to process both numeric and textual data, such as shipping documents and regulatory guidelines, remains underexplored. To address this gap, the proposed conceptual framework embedding SLMs within edge gateways, aiming to enhance localized data processing, anomaly detection, and context-aware decision support under resource constraints. By employing SLMs' compact nature, this approach could reduce reliance on cloud services, lower operational costs, and provide timely insights for cold chain stakeholders. Furthermore, SLM-based methods facilitate advanced analytics, enabling early interventions when anomalies occur. This framework lays the groundwork for future research on efficient, resilient, and scalable monitoring solutions in cold chain environments.