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系統識別號 U0026-1208202015432000
論文名稱(中文) 針對物聯網訂閱型資料交易的去中心化基礎建設
論文名稱(英文) A Fully Decentralized Infrastructure for Subscription-based IoT Data Trading
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 林京樺
研究生(英文) Ching-Hua Lin
電子信箱 jkrvivian@gmail.com
學號 P76074591
學位類別 碩士
語文別 英文
論文頁數 59頁
口試委員 指導教授-涂嘉恒
共同指導教授-黃敬群
口試委員-葉羅堯
口試委員-張大緯
中文關鍵字 發布/訂閱  資料交易  去中心化  分散式帳本技術 
英文關鍵字 publish/subscribe  data trading  decentralization  Distributed Ledger Technology 
學科別分類
中文摘要 在物聯網場景中,machine-to-machine(M2M)的發展勢不可擋,它允許機器擁有自己的數位資產並開始參與經濟活動,讓裝置間可以共享和交易其資源。在發布/訂閱(publish/subscribe)通訊模型上將及時資料貨幣化,就可以依據資料使用量來計費,這類似於軟體即服務 (Software-as-a-Service),一種基於訂閱的定價模型。這種定價模型讓資料提供者可以更好地管理預算,且資料消費者可以隨興的訂閱和退訂。然而,動態的資料流提升了資料所有權和身份驗證的重要性和難度。因此,一個不需信任單一機構的資料交易基礎建設是必需的,除了能允許參與者進行交易,還能驗證資料所有權和完整性。此外,訂閱流程的自動化亦是資料貨幣化中至關重要的。在本文中,我們利用分散式帳本技術 (Distributed Ledger Techonology),在 IoT 中介基礎架構之上構建去中心化資料交易平台。這種方法可以有效地提高運作透明度和系統可擴展性,建立在加密訊息協議之上的存儲允許在沒有權限的情況下通過分散式帳本儲存、傳輸、存取和驗證資料流,在我們的設計中也讓交易參與者的數位權益得到保證。
英文摘要 Within IoT scenarios, machine-to-machine (M2M) is an inevitable technology that allows machines to own their digital assets and start participating in an economy to share and trade their resources. Monetizing the real-time data on top of the publish/subscribe (pub/sub) communication model enables the payment of data streams with data usage instead of a particular price for a fixed data set, which is similar to Software-as-a-Service (SaaS), a subscription-based pricing model. This pricing model allows data providers to have a better vision of managing budgets and data consumers to have the flexibility to subscribe and unsubscribe. However, streaming data increases the importance and difficulties of dynamic data ownership and identity verification. A trustless data trading infrastructure is required where the entities can trade, validate data ownership and integrity without trusting any services. In addition, an automated subscription procedure is also demanded for the sake of data monetization. In this thesis, we leverage the usages of distributed ledger technologies (DLTs) to construct a decentralized data trading platform on top of the IoT brokered infrastructure. This approach can efficiently enhance the degree of transparency and scalability. The storage built upon cryptographic message protocols allows transmitting, accessing, and validating data streams over distributed ledgers without authorities, and the digital rights of trading participants deserve a guarantee, which is enabled by design.
論文目次 摘要 i
Abstract ii
誌謝 iii
Table of Contents iv
List of Tables vi
List of Figures vii
Chapter 1. Introduction 1
1.1. Contributions 4
1.2. Thesis Organization 5
Chapter 2. Related Work 7
2.1. The Publish/subscribe Communication Models Over Blockchain 7
2.2. Trusted IoT Trading Infrastructure 9
2.3. Distributed Storage System 11
Chapter 3. System Design Thinking 13
3.1. Data Subscription-based Trading Platform Players 13
3.2. Choice of Data Storage 14
3.3. Digital Identity 18
3.4. Enable Automated Trading Process 21
3.5. Enable Computation Tasks Delegation to Broker with Privacy 24
Chapter 4. Masked Authenticated Messaging 25
4.1. The Message Streams 25
4.2. Enable Access Control and Authentication 27
4.3. The Advantages of Adopting MAM in Subscription-based Data Trading Infrastructure 27
4.3.1. A Scalable Keys and Data Entry Points Management 27
4.3.2. Data Streams Classification and Traceability 28
4.4. Delegate MAM Operations to Tangle-accelerator 28
4.4.1. Communication Protocol 30
4.4.2. End-to-End-Encryption 30
4.4.3. Register with Tangle-accelerator 32
4.4.4. Issues in End-to-End-Encryption 32
Time to Read Message 32
Spam on Message Channel Chain 33
Chapter 5. Decentralized Subscription-based Data Trading Models 34
5.1. Prerequisite 34
5.2. Launch Data Products 34
5.3. Subscribe to Data Product 38
5.4. Unsubscribe to Data Products 39
5.5. Launch a Refund 41
Chapter 6. Evaluation 43
6.1. MAM Performance Evaluation 43
6.1.1. Channel / Endpoint Creation 44
6.1.2. Messages Publishment 45
6.2. MAM vs. the Delegated MAM 48
6.2.1. Experiment 48
6.2.2. Time Complexity 49
6.3. Smart Contract 51
Chapter 7. Conclusion 54
Chapter 8. Future Work 55
References 56
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