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.

[1] C. Metter, M. Seufert, F. Wamser, T. Zinner, and P. Tran-Gia, “Analytical model for SDN
signaling traffic and flow table occupancy and its application for various types of traffic,”
IEEE Transactions on Network and Service Management, vol. 14, no. 3, pp. 603–615, Sep.
2017.
[2] G. Levy, S. Pontarelli, and P. Reviriego, “Flexible packet matching with single double
cuckoo hash,” IEEE Communications Magazine, vol. 55, no. 6, pp. 212–217, Jun. 2017.
[3] T.-S. Chen, D.-Y. Lee, T.-T. Liu, and A.-Y. Wu, “Dynamic reconfigurable ternary content
addressable memory for openflow-compliant low-power packet processing,” IEEE Transactions
on Circuits and Systems I: Regular Papers, vol. 63, no. 10, pp. 1661–1672, Oct.
2016.
[4] N. Kitsuwan, S. Ba, E. Oki, T. Kurimoto, and S. Urushidani, “Flows reduction scheme
using two MPLS tags in software-defined network,” IEEE Access, vol. 5, pp. 14 626–14 637,
Jul. 2017.
[5] D. Hu, S. Li, H. Huang, W. Fang, and Z. Zhu, “Flexible flow converging: A systematic
case study on forwarding plane programmability of protocol-oblivious forwarding (POF),”
IEEE Access, vol. 4, pp. 4707–4719, Aug. 2016.
[6] B. Leng, L. Huang, X. Wang, H. Xu, and Y. Zhang, “A mechanism for reducing flow
tables in software defined network,” in Proceedings of IEEE International Conference on
Communications (ICC), Jun. 2015, pp. 5302–5307.
[7] X. Jia, Y. Jiang, Z. Guo, and Z. Wu, “Reducing and balancing flow table entries in
software-defined networks,” in Proceedings of IEEE Conference on Local Computer Networks
(LCN), Nov. 2016, pp. 575–578.
[8] T. Wang, F. Liu, and H. Xu, “An efficient online algorithm for dynamic SDN controller
assignment in data center networks,” IEEE/ACM Transactions on Networking, vol. 25,
no. 5, pp. 2788–2801, Oct. 2017.
[9] S. A. Astaneh and S. S. Heydari, “Optimization of SDN flow operations in multi-failure
restoration scenarios,” IEEE Transactions on Network and Service Management, vol. 13,
no. 3, pp. 421–432, Sep. 2016.
[10] H. Xu, Z. Yu, C. Qian, X.-Y. Li, Z. Liu, and L. Huang, “Minimizing flow statistics collection
cost using wildcard-based requests in SDNs,” IEEE/ACM Transactions on Networking,
vol. 25, no. 6, pp. 3587–3601, Dec. 2017.
[11] T. K. Phan, D. Griffin, E. Maini, and M. Rio, “Utility-centric networking: Balancing transit
costs with quality of experience,” accepted by IEEE/ACM Transactions on Networking
in 2017 (available at IEEE Early Access Articles).
[12] E. Sakic and W. Kellerer, “Response time and availability study of RAFT consensus in
distributed SDN control plane,” accepted by IEEE Transactions on Network and Service
Management in 2017 (available at IEEE Early Access Articles).
[13] P. Wang, H. Xu, L. Huang, C. Qian, S. Wang, and Y. Sun, “Minimizing controller response
time through flow redirecting in SDNs,” accepted by IEEE/ACM Transactions on
Networking in 2018 (available at IEEE Early Access Articles).
[14] P. Wang, H. Xu, L. Huang, J. He, and Z. Meng, “Control link load balancing and low
delay route deployment for software defined networks,” IEEE Journal on Selected Areas
in Communications, vol. 35, no. 11, pp. 2446–2456, Nov. 2017.
[15] H. Xu, Z. Yu, X.-Y. Li, L. Huang, C. Qian, and T. Jung, “Joint route selection and update
scheduling for low-latency update in SDNs,” IEEE/ACM Transactions on Networking, vol.
25, no. 5, pp. 3073–3087, Oct. 2017.
[16] H. Zhou, C. Wu, Q. Cheng, and Q. Liu, “Sdn-liru: A lossless and seamless method for
SDN inter-domain route updates,” IEEE/ACM Transactions on Networking, vol. 25, no.
4, pp. 2473–2483, Aug. 2017.
[17] S. Vissicchio, L. Vanbever, L. Cittadini, G. G. Xie, and O. Bonaventure, “Safe update of
hybrid SDN networks,” IEEE/ACM Transactions on Networking, vol. 25, no. 3, pp. 1649–
1662, Jun. 2017.
[18] M. Azizian, S. Cherkaoui, and A. S. Hafid, “Vehicle software updates distribution with
SDN and cloud computing,” IEEE Communications Magazine, vol. 55, no. 8, pp. 74–79,
Aug. 2017.
[19] S. Vissicchio and L. Cittadini, “Safe, efficient, and robust SDN updates by combining
rule replacements and additions,” IEEE/ACM Transactions on Networking, vol. 25, no.
5, pp. 3102–3115, Oct. 2017.
[20] T. Dargahi, A. Caponi, M. Ambrosin, G. Bianchi, and M. Conti, “A survey on the security
of stateful SDN data planes,” IEEE Communications Surveys and Tutorials, vol. 19, no.
3, pp. 1701–1725, Mar. 2017.
[21] M. Ambrosin, M. Conti, F. D. Gaspari, and R. Poovendran, “Lineswitch: Tackling control
plane saturation attacks in software-defined networking,” IEEE/ACM Transactions on
Networking, vol. 25, no. 2, pp. 1206–1219, Apr. 2017.
[22] J. H. Cox, R. Clark, and H. Owen, “Leveraging SDN and WebRTC for rogue access point
security,” IEEE Transcations on Network and Service Management, vol. 14, no. 3, pp. 756–
770, Sep. 2017.
[23] C. Qi, J. Wu, G. Cheng, J. Ai, and S. Zhao, “An aware-scheduling security architecture
with priority-equal multi-controller for SDN,” China Communications, vol. 14, no. 9,
pp. 144–154, Sep. 2017.
[24] C.-L. Hsieh and N. Weng, “Many-field packet classification for software-defined networking
switches,” in Proceedings of ACM/IEEE Symposium on Architectures for Networking and
Communications Systems (ANCS), Mar. 2016, pp. 13–24.
[25] K. G. Pérez, X. Yang, S. Scott-Hayward, and S. Sezer, “A configurable packet classification
architecture for software-defined networking,” in Proceedings of IEEE International
System-on-Chip Conference (SOCC), Sep. 2014, pp. 353–358.
[26] ——, “Optimized packet classification for software-defined networking,” in Proceedings of
IEEE International Conference on Communications (ICC), Jun. 2014, pp. 859–864.
[27] E. Alasadi and H. S. Al-Raweshidy, “Ssed: Servers under software-defined network architectures
to eliminate discovery messages,” accepted by IEEE/ACM Transactions on
Networking in 2017 (available at IEEE Early Access Articles).
[28] J. Saldana, D. de Hoz, J. Fernández-Navajas, J. Ruiz-Mas, F. Pascual, D. R. Lopez, D.
Florez, J. A. Castell, and M. Nuñez, “Small-packet flows in software defined networks:
Traffic profile optimization,” Journal of Networks, vol. 10, no. 4, pp. 176–187, Apr. 2015.
[29] S. C.-H. Huang and Hsiao-Chun Wu, “Software-defined multiplexing codes,” in Proceedings
of IEEE Global Communications Conference (GLOBECOM), Dec. 2015, pp. 1–6.
[30] Uzma, J. A. Sheikh, S. A. Parah, G. M. Bhat, and Safeena-al-Nisa, “Energy efficient image
transmission through orthogonal frequency division multiplexing (OFDM) based multiple
input multiple output (MIMO) systems,” in Proceedings of International Conference on
Image Information Processing (ICIIP), Dec. 2017, pp. 1–6.
[31] X. Ming, T. Shu, and X. Xianzhong, “An energy-efficient wireless image transmission
method based on adaptive block compressive sensing and softcast,” in Proceedings of
International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), Dec.
2017, pp. 712–717.
[32] L. Zhao, Y. Liu, and L. Wang, “Image compression and reconstruction of transmission line
monitoring images using compressed sensing,” in Proceedings of International Conference
on Mechanical and Aerospace Engineering (ICMAE), Jul. 2017, pp. 371–375.
[33] P. Natu, S. Natu, and T. Sarode, “Hybrid image compression using VQ on error image,” in
Proceedings of International Conference on Intelligent Communication and Computational
Techniques (ICCT), Dec. 2017, pp. 173–176.
[34] K. Michalopoulos, M. Tsakalakis, and N. Bourbakis, “Detecting texture paths and patterns
in aerial images,” in Proceedings of International Conference on Information, Intelligence,
Systems and Applications (IISA), Jul. 2013, pp. 1–5.
[35] N. Bourbakis and R. Patil, “A methodology for automatically detecting texture paths
and patterns in images,” in Proceedings of IEEE International Conference on Tools with
Artificial Intelligence (ICTAI), vol. 1, 2007, pp. 504–512.
[36] Y. Cai, R. Wang, T. Cui, H. Lv, and S. Ma, “Intermediate view synthesis based on edge
detecting,” in Proceedings of IEEE International Conference on Image Processing, Sep.
2013, pp. 3172–3175.
[37] G. Cheung, E. Magli, Y. Tanaka, and M. K. Ng, “Graph spectral image processing,”
accepted by Proceedings of the IEEE in 2018 (available at IEEE Early Access Articles).
[38] L. M. C. Leon and P. A. V. D. Miranda, “Multi-object segmentation by hierarchical layered
oriented image foresting transform,” in Proceedings of Conference on Graphics, Patterns
and Images (SIBGRAPI), Oct. 2017, pp. 79–86.
[39] M. A. T. Condori, F. A. M. Cappabianco, A. X. Falcao, and P. A. V. D. Miranda,
“Extending the differential image foresting transform to root-based path-cost functions
with application to superpixel segmentation,” in Proceedings of Conference on Graphics,
Patterns and Images (SIBGRAPI), Oct. 2017, pp. 7–14.
[40] D. C. Cernea, A. Munteanu, A. Alecu, J. Cornelis, and P. Schelkens, “Scalable joint source
and channel coding of meshes,” IEEE Transactions on Multimedia, vol. 10, no. 3, pp. 503–
513, Mar. 2008.
[41] D. C. Cernea, A. Munteanu, A. Alecu, J. Cornelis, P. Schelkens, and F. M. Burgos,
“Efficient error control in 3D mesh coding,” in Proceedings of IEEE International Workshop
on Multimedia Signal Processing, Oct. 2010, pp. 292–297.
[42] Y. Liu, Y. Liu, and H. Yang, “A progressive transmission scheme for 3D models in VR/AR
based on UEP-LT code,” in Proceedings of Annual International Symposium on Personal,
Indoor, and Mobile Radio Communications (PIMRC), Oct. 2017, pp. 1–6.
[43] Zafar Shahid, Marc Chaumont, and William Puech, “Fast protection of H.264/AVC by
selective encryption of CAVLC and CABAC for I and P frames,” IEEE Transactions on
Circuits and Systems for Video Technology, vol. 21, no. 5, pp. 565–576, May 2011.
[44] Mamoona Naveed Asghar and Mohammad Ghanbari, “An efficient security system for
CABAC bin-strings of H.264/SVC,” IEEE Transactions on Circuits and Systems for
Video Technology, vol. 23, no. 3, pp. 425–437, Mar. 2013.
[45] Sanjay Kumar and Sandeep Srivastava, “Image encryption using simplified data encryption
standard (S-DES),” International Journal of Computer Applications, no. 2, pp. 38–42, Oct.
2014.
[46] Manjula K G and M N Ravikumar, “Color image encryption and decryption using DES
algorithm,” International Research Journal of Engineering and Technology (IRJET), vol.
3, no. 7, pp. 1715–1718, Jul. 2016.
[47] Sujoy Sinha Roy, Chester Rebeiro, and Debdeep Mukhopadhyay, “Theoretical modeling
of elliptic curve scalar multiplier on LUT-based FPGAs for area and speed,” IEEE Transactions
on Very Large Scale Integration (VLSI) Systems, vol. 21, no. 5, pp. 901–909, May
2013.
[48] Yuling Luo, Xue Ouyang, Junxiu Liu, and Lvchen Cao, “An image encryption method
based on elliptic curve elgamal encryption and chaotic systems,” IEEE Access, vol. 7,
pp. 38 507–38 522, Apr. 2019.
[49] M. Dzwonkowski and R. Rykaczewski, “Quaternion encryption method for image and
video transmission,” Telecom. Overv. and Telecom. News, vol. 8–9, pp. 1216–1220, 2013.
[50] Jinwei Wang, Ting Li, Xiangyang Luo, Yun-Qing Shi, and Sunil Kr. Jha, “Identifying
computer generated images based on quaternion central moments in color quaternion
wavelet domain,” IEEE Transactions on Circuits and Systems for Video Technology, vol.
29, no. 9, pp. 2775–2785, Sep. 2019.
[51] F. Checconi, L. Rizzo, and P. Valente, “Qfq: Efficient packet scheduling with tight guarantees,”
IEEE Transactions on Networking, vol. 21, no. 3, pp. 802–816, Jun. 2013.
[52] L. Huang and M. J. Neely, “Utility optimal scheduling in energy-harvesting networks,”
IEEE/ACM Transactions on Networking, vol. 21, no. 4, pp. 1117–1130, Aug. 2013.
[53] Z. Mao, C. E. Koksal, and N. B. Shroff, “Optimal online scheduling with arbitrary hard
deadlines in multihop communication networks,” IEEE/ACM Transactions on Networking,
vol. 24, no. 1, pp. 177–189, Feb. 2016.
[54] G. C. Sankaran and K. M. Sivalingam, “Design and analysis of scheduling algorithms for
optically groomed data center networks,” IEEE/ACM Transactions on Networking, vol.
25, no. 6, pp. 3282–3293, Dec. 2017.
[55] D. E. Knuth, J. J. H. Morris, and V. R. Pratt, “Fast pattern matching in strings,” SIAM
Journal on Computing, vol. 6, no. 2, pp. 323–350, 1977, Available: http://dx.doi.org/
10.1137/0206024.
[56] Ying Fu, Yinqiang Zheng, Hua Huang, Imari Sato, and Yoichi Sato, “Hyperspectral image
super-resolution with a mosaic RGB image,” IEEE Transactions on Image Processing,
vol. 27, no. 11, pp. 5539–5552, Nov. 2018.
[57] Xin Chang, Chunxi Dong, Zhengzhao Tang, and Yang-Yang Dong, “Mosaic scene deception
jamming based on 2D separation modulation against SAR,” IET Radar, Sonar and
Navigation, vol. 13, no. 2, pp. 310–315, Feb. 2019.
[58] Kristen L. Lurie, Roland Angst, and Audrey K. Ellerbee, “Automated mosaicing of featurepoor
optical coherence tomography volumes with an integrated white light imaging system,”
IEEE Transactions on Biomedical Engineering, vol. 61, no. 7, pp. 2141–2153, Jul.
2014.
[59] Jin Tang, Yanghui Liang, Fan Guo, Zhifu Wu, and Xiaoming Xiao, “Sequential far infrared
image mosaic using coarse-to-fine scheme,” IEEE Access, vol. 7, pp. 70 185–70 199, May
2019.
[60] Tatsuya Chuman, Warit Sirichotedumrong, and Hitoshi Kiya, “Encryption-then-compression
systems using grayscale-based image encryption for JPEG images,” IEEE Transactions on
Information Forensics and Security, vol. 14, no. 6, pp. 1515–1525, Jun. 2019.
[61] Xuejing Kang and Ran Tao, “Color image encryption using pixel scrambling operator
and reality-preserving MPFRHT,” IEEE Transactions on Circuits and Systems for Video
Technology, vol. 29, no. 7, pp. 1919–1931, Jul. 2019.
[62] “The berkeley segmentation dataset and benchmark,” [Online]. Available: https://www2.
eecs.berkeley.edu/Research/Projects/CS/vision/bsds/.
[63] “Labeled faces in the wild,” [Online]. Available: http://vis-www.cs.umass.edu/lfw/
index.html.
[64] “Number plate datasets,” [Online]. Available: https://platerecognizer.com/numberplate-
datasets/.
[65] “Graphis inc.,” [Online]. Available: http://www.graphis.com/logos/.