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https://nccur.lib.nccu.edu.tw/handle/140.119/158474
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| Title: | 在 NDN 發布訂閱系統中實現自然語言訂閱
Natural-Language Subscription over NDN Pub/Sub System |
| Authors: | 鄞雋衡
Yin, Chun-Heng |
| Contributors: | 蔡子傑
Tsai, Tzu-Chieh
鄞雋衡
Yin, Chun-Heng |
| Keywords: | 命名資料網路
邊緣運算
發布訂閱系統
大型語言模型
自然語言處理
Named Data Networking
Edge Computing
Publish/Subscribe Systems
Large Language Models
Natural Language Processing |
| Date: | 2025 |
| Issue Date: | 2025-08-04 13:57:33 (UTC+8) |
| Abstract: | 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. |
| Reference: | [1] Patrick Th Eugster et al. “The many faces of publish/subscribe”. In: ACM comput- ing surveys (CSUR) 35.2 (2003), pp. 114–131.
[2] Kevin Chan et al. “Fuzzy interest forwarding”. In: Proceedings of the 13th Asian Internet Engineering Conference. 2017, pp. 31–37.
[3] Rishi Bommasani et al. “On the opportunities and risks of foundation models”. In: arXiv preprint arXiv:2108.07258 (2021).
[4] Apache Software Foundation. Apache Kafka Documentation. Accessed: 2023-10- 15. 2023. URL: https://kafka.apache.org/documentation/.
[5] Google Cloud. Google Cloud Pub/Sub Documentation. 2023. URL: https : / / cloud.google.com/pubsub/docs (visited on 10/15/2023).
[6] Andrew Banks and Rahul Gupta. MQTT Version 3.1.1. Tech. rep. OASIS, 2014. URL: https://docs.oasis-open.org/mqtt/mqtt/v3.1.1/mqtt-v3.1.1. html.
[7] Lixia Zhang et al. “Named data networking”. In: ACM SIGCOMM Computer Com- munication Review 44.3 (2014), pp. 66–73.
[8] Minsheng Zhang, Vince Lehman, and Lan Wang. “Scalable name-based data syn- chronization for named data networking”. In: IEEE Infocom 2017-IEEE Confer- ence on Computer Communications. IEEE. 2017, pp. 1–9.
[9] Alexander Afanasyev, Spyridon Mastorakis, et al. PSync: Partial and Full Syn- chronization Library for NDN. 2025. URL: https://github.com/named-data/ PSync.
[10] Li Yujian and Liu Bo. “A normalized Levenshtein distance metric”. In: IEEE trans- actions on pattern analysis and machine intelligence 29.6 (2007), pp. 1091–1095.
[11] Suphakit Niwattanakul et al. “Using of Jaccard coefficient for keywords similarity”. In: Proceedings of the international multiconference of engineers and computer scientists. Vol. 1. 6. 2013, pp. 380–384.
[12] Spyridon Mastorakis et al. “Experimentation with fuzzy interest forwarding in named data networking”. In: arXiv preprint arXiv:1802.03072 (2018).
[13] Adrian Zapletal, Kazuaki Ueda, and Atsushi Tagami. “Evaluation of forwarding strategies for NDN-based multi-access edge computing”. In: GLOBECOM 2020- 2020 IEEE Global Communications Conference. IEEE. 2020, pp. 1–6.
[14] JUNG Heeyoung et al. “A networking scheme for large-scale pub/sub service over NDN”. In: 2019 International Conference on Information and Communication Tech- nology Convergence (ICTC). IEEE. 2019, pp. 1195–1200.
[15] Jason Wei et al. “Chain-of-thought prompting elicits reasoning in large language models”. In: Advances in neural information processing systems 35 (2022), pp. 24824– 24837. |
| Description: | 碩士
國立政治大學
資訊科學系
111753135 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111753135 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
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