因應智慧商圈資訊爆炸與即時推播需求，傳統中心化發布/訂閱系 統在彈性與擴展性上遇到瓶頸。Named Data Networking(NDN)雖具 備去中心化與內容導向優勢，但其結構化命名機制難以支援自然語言 訂閱需求。本研究提出整合大型語言模型(LLM)與 NDN 的混合式 架構，設計「智慧商圈命名約定」(SBD-NC)，並在邊緣運算架構上實 現語意解析，將用戶查詢自動轉換為結構化命名，解決命名自由度與 一致性問題。實驗結果顯示，本系統於語意轉換準確率、效能與擴展 性均優於傳統方法，展現去中心化架構於動態物聯網環境的應用潛力。

In response to the information explosion and real-time push demands in smart business districts, traditional centralized publish/subscribe systems encounter bottlenecks in flexibility and scalability. Although Named Data Networking (NDN) offers the advantages of decentralization and content- orientation, its structured naming mechanism struggles to support natural lan- guage subscription demands. This research proposes a hybrid architecture integrating Large Language Models (LLMs) with NDN, designing a ”Smart Business District Naming Convention” (SBD-NC) and implementing seman- tic parsing on an edge computing architecture to automatically convert user queries into structured names, addressing the challenges of naming freedom and consistency. Experimental results demonstrate that our system surpasses traditional methods in semantic conversion accuracy, performance, and scal- ability, showcasing the potential of a decentralized architecture in dynamic IoT environments.

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