在物聯網蓬勃發展的時代裡，愈來愈多新的通訊方式也隨之出現。然而在這樣變化多端的網路通訊世代，如何有效率的結合以及傳輸多樣的資訊流成了現在一個重要的課題。在傳統的通訊協議裡，固定的封包格式（含表頭資料以及資訊負載）
扮演一個重要的架構。而在無線隨意網路裡，如何利用傳統通訊協議有效結合時變個數的資訊流為一個很大的挑戰。由於現有的封包格式無法隨時安插新的資訊流，因此傳統通訊協議只能侷限在預處理之通道分配的前提下達成信息融合。根據我們近期的發表裡，軟體多路複用碼跳脫了傳統通訊協議的限制，因此可以有效率地隨時插入不同資訊流。軟體多路複用碼技術可根據插入的分格碼完成編碼以及解碼的演算法，進而達成信息融合。在這篇論文裡，我們提供了軟體多路複用碼技術的完整理論分析。不同於以往的傳統通訊協議裡，我們加入了分隔碼的概念。在分隔碼的架構下，我們提出且分析三種數據流的綁定模式：分散式，蜂巢式，以及混合式。為了比較傳統通訊協議以及軟體多路複用碼的有效性，我們提出了兩種效率測量：編碼效率性以及資訊傳輸間歇性。在這兩種度量下，我們發現分散式，蜂巢式，以及混合式的融合模式各自有優點。軟體多路複用碼技術的提出，不僅給信息融合一個新的方向，更開啟了新的傳輸模式。在目前現有的技術裡，圖的傳輸絕大多數是利用鄰接矩陣。然而鄰接矩陣用在大的分散圖上會存在很多的零元素，傳輸此種鄰接矩陣是沒有效率的。我們提出了一個進化的演算法，此種演算法可以達成圖的切割以及有效率的傳輸大的分散圖，而且可以滿足移動裝置針對更新動態圖通訊的需求。針對物聯網以及移動裝置的技術發展下，資料加密也是一個重要的研究議題。近年來，由於物聯網裝置講求快速、便利以及輕巧，傳統的加密系統因為複雜的運算而不敷使用，因此找到一個有效率的加密系統對於物聯網裝置而言是很急迫的。在這篇論文裡，我們提出了一種有效率的加密馬賽克技術，並稱之為有效恢復加密馬賽克技術，此種技術成功加密原始資料裡的秘密資訊。相對於傳統通訊協議的加密系統下，我們提出的技術更有彈性且有效率，這種技術在未來發展下必定可以達成更好的加密以及傳輸效果。

Since internet of things (IoT) has emerge as a crucial technology nowadays, more and more new communication scenarios have been emerging. To efficiently transmit multiple types of data-streams across smart devices, how to combine multiple data-streams for transmission in aggregate is a very important problem. The conventional packet-based protocols cannot provide the flexibility for combining data-streams in the ad hoc nature. If the number of data-streams changes over time, the existing packet formats cannot handle the transmission of multiple data-streams effectively. Superior to our recently propose software-defined multiplexing code (SDMC) which enable pocketless communications in the future, we can combine (multiplex) multiple data-streams easily and effectively. The SDMC scheme simply relies on simple algorithms to insert delimiters as needed such that no fixed packet boundaries are required anymore. In this thesis, we give a complete theoretical analysis of chosen delimiters for SDMC. Furthermore, we propose three SDMC schemes (distributed, hierarchical, and hybrid) which are compared theoretically and by simulation. The effectiveness is measured by two performance metrics, namely coding efficiency and data-transmission intermittency. A trade-off between these two performance metrics can be found when one selects one of this three SDMC schemes for combining multiple data-streams. Moreover, SDMC approach can also fulfill the needs of dynamic mapping for mobile devices, such as autonomous vehicles. In the past, it is common to transmit a graph by adjacency matrix. However, it is inefficient to transmit a big sparse graph under adjacency matrix because of many zero entries. Hence, we dedicate a novel coding scheme to segmenting and arranging the data efficiently from a huge mesh-grid graph. In addition, data-encryption becomes more important, especially for IoT, mobile devices, and smart devices. Since conventional cryptographic techniques need lots of calculations, most smart devices cannot afford high computation-complexity. It is important to design an efficienct cryptographic technique. In this thesis, we propose a new efficient cryptographic mosaic technique, namely efficient recoverable cryptographic mosaic technique by permutations. Our proposed new mosaic technique can successfully construct the encrypted data containing the mosaicked data therein from the original data. Compare to the existing protocol-based communications and the modern cryptography, our proposed techniques are very flexible and efficient scheme for future transmission and data protection.

摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv
Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi
Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Software-Defined Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Information Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Graph-Transmission. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Data-Encryption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.5 Summary of Contributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Software-Defined Multiplexing Code 8
2.1 Basic SDMC Structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Delimiter Construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3 Theoretical Analysis of Software-Defined Multiplexing Code . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1 D-SDMC, H-SDMC, and D/H-SDMC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 FSS and SFSS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3 Expected Numbers of Stuffed Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.4 Coding efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.5 Data-transmission Intermittency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.6 Numerical Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.7 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4 Novel Efficient Coding Scheme for Data-Rate Limited Journey-Aware
Graph-Data Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.1 New Code Structure for Graph Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.2 Transmission and Partition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.3 Journey-Aware Graph-Data Transmission under Dynamic Mapping . . . . . . . . . . . . . . . . . . 51
4.4 Numerical Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5 Efficient Recoverable Cryptographic Mosaic Technique by Permutations . . . . . . . . . . . . . . . 56
5.1 Sums of Absolute Differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.1.1 Busch’s Permutation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.1.2 Wu’s Permutation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.1.3 Sun’s/Minmax Permutation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.1.4 Analysis of Minimum Absolute Difference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.1.5 Examples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.2 Analysis of Permuted Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5.2.1 Summed Cross-Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.2.2 Kullback-Leibler Divergence of Discrete Cosine Transform. . . . . . . . . . . . . . . . . . . . . . . . 70
5.3 Average Cross-correlation Minimization over Permutation. . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.4 Efficient Recoverable Cryptographic Mosaic Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.5 Numerical Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
A Proof . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
A.1 Proofs of Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
A.2 Proofs of Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
A.3 Proofs of Chapter 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
B Derivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

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