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題名 基於物聯網應用與服務之串流資料壓縮
Content-sensitive Data Compression for IoT Streaming Services作者 許峻基
Hsu, Chun-Chi貢獻者 郁方
Yu, Fang
許峻基
Hsu, Chun-Chi關鍵詞 物聯網
串流
資料壓縮
影像處理
IoT
Streaming
Data compression
Image processing日期 2017 上傳時間 28-Aug-2017 11:25:19 (UTC+8) 摘要 隨著科技的進步,更快更可靠的網路使得物聯網成為可能。而伴隨著物聯網科技的發展而來的,是如何處理從終端裝置中,所收集的巨量資料。本文希望能藉由影像處理,利用分解與比對來自物聯網裝置的串流影片,找出在一定影片檔案壓縮比率之下,最佳的影片品質。除此之外,我們在實驗中還能夠藉由操作不同的比較參數,讓影片壓縮的演算法能夠在不同的情境下,藉由調整參數權重,找出最適合該情境的壓縮演算法配置,藉此達到減少儲存空間需求的目的。在實驗中,我們建立基本的物聯網串流應用,並且能夠在與原影片96.5%的差異度之下,達到40%的影格儲存空間精簡優化。
The progression of cheaper, faster and more reliable Internet technology makes Internet of Things (IoT) realized in life. While tremendous data are collected from end devices, scalable and effective data compression techniques are needed to balance storage and precision. This paper presents an adjustable content-sensitive datacompression approach for IOT streaming services and applications. Specifically, we apply frame similarity on various aspects such as illumination and structure tostreaming frames, and are able to keep sufficient differences among steaming data while reducing significant amount of storage. We setup a general iot application platform in practice and show that with the presented approach, we are able to keep 96.5% precision with 40% frame reduction on the steaming data collected in real life.參考文獻 [1] L. Atzori, A. Iera, and G. Morabito, “The internet of things: A survey,” Computer networks, vol. 54, no. 15, pp. 2787–2805, 2010.[2] H. Sundmaeker, P. Guillemin, P. Friess, and S. Woelffl ́e, “Vision and challenges for realising the internet of things,” Cluster of European Research Projects on the Internet of Things, European Commision, 2010.[3] A. Prasad, K. Mamun, F. Islam, and H. Haqva, “Smart water quality monitoring system,” in Computer Science and Engineering (APWC on CSE), 2015 2nd Asia-Pacific World Congress on, pp. 1–6, IEEE, 2015.[4] M. Soliman, T. Abiodun, T. Hamouda, J. Zhou, and C.-H. Lung, “Smart home: Integrating internet of things with web services and cloud computing,” in Cloud Computing Technology and Science (CloudCom), 2013 IEEE 5th International Conference on, vol. 2, pp. 317–320, IEEE, 2013.[5] A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, “Internet of thingsfor smart cities,” IEEE Internet of Things journal, vol. 1, no. 1, pp. 22–32, 2014.[6] J. Wei, “How wearables intersect with the cloud and the internet of things: Con-siderations for the developers of wearables.,” IEEE Consumer Electronics Magazine, vol. 3, no. 3, pp. 53–56, 2014.[7] D. Sreekantha and A. Kavya, “Agricultural crop monitoring using iot-a study,” in Intelligent Systems and Control (ISCO), 2017 11th International Conference on,pp. 134–139, IEEE, 2017.[8] J. Chin and A. Tisan, “An iot-based pervasive body hydration tracker (pht),” in In-dustrial Informatics (INDIN), 2015 IEEE 13th International Conference on, pp. 437–441, IEEE, 2015.33[9] M. Kovatsch, S. Mayer, and B. Ostermaier, “Moving application logic from the firmware to the cloud: Towards the thin server architecture for the internet of things,”in Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2012 Sixth International Conference on, pp. 751–756, IEEE, 2012.[10] Y.-K. Chen, “Challenges and opportunities of internet of things,” in Design Automa-tion Conference (ASP-DAC), 2012 17th Asia and South Pacific, pp. 383–388, IEEE,2012.[11] K. Das and P. Havinga, “Evaluation of dect for low latency real-time industrial control networks,” in Sensor, Mesh and Ad Hoc Communications and Networks(SECON), 2013 10th Annual IEEE Communications Society Conference on, pp. 10–17, IEEE, 2013.[12] A. Gogawale, F. Khatib, P. Sontakke, and S. Saigaonkar, “Database-as-a-service for iot,” in Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on, pp. 1436–1438, IEEE, 2016.[13] H. Schwarz, D. Marpe, and T. Wiegand, “Overview of the scalable video coding extension of the h. 264/avc standard,” IEEE Transactions on circuits and systemsfor video technology, vol. 17, no. 9, pp. 1103–1120, 2007.[14] D. Le Gall, “Mpeg: A video compression standard for multimedia applications,” Communications of the ACM, vol. 34, no. 4, pp. 46–58, 1991.[15] K. Jeevan and S. Krishnakumar, “Compression of images represented in hexagonal lattice using wavelet and gabor filter,” in Contemporary Computing and Informatics(IC3I), 2014 International Conference on, pp. 609–613, IEEE, 2014.[16] Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on, vol. 2, pp. 1398–1402, IEEE,2003.34[17] A. Hore and D. Ziou, “Image quality metrics: Psnr vs. ssim,” in Pattern Recognition(ICPR), 2010 20th International Conference on, pp. 2366–2369, IEEE, 2010.[18] G. Chen, Y. Shen, F. Yao, P. Liu, and Y. Liu, “Region-based moving object detection using ssim,” in Computer Science and Network Technology (ICCSNT), 2015 4th International Conference on, vol. 1, pp. 1361–1364, IEEE, 2015.[19] S. Wang, A. Rehman, Z. Wang, S. Ma, and W. Gao, “Ssim-motivated rate-distortion optimization for video coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 4, pp. 516–529, 2012.[20] F. Raphel and S. Sameer, “Ssim based resource optimization for multiuser downlink ofdm video transmission systems,” in Region 10 Conference (TENCON), 2016 IEEE, pp. 1583–1586, IEEE, 2016.[21] “Raspberry pi.” https://www.raspberrypi.org/.[22] “Arduino.” https://www.arduino.cc/.[23] “Ros.” http://www.ros.org/. 描述 碩士
國立政治大學
資訊管理學系
104356012資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104356012 資料類型 thesis dc.contributor.advisor 郁方 zh_TW dc.contributor.advisor Yu, Fang en_US dc.contributor.author (Authors) 許峻基 zh_TW dc.contributor.author (Authors) Hsu, Chun-Chi en_US dc.creator (作者) 許峻基 zh_TW dc.creator (作者) Hsu, Chun-Chi en_US dc.date (日期) 2017 en_US dc.date.accessioned 28-Aug-2017 11:25:19 (UTC+8) - dc.date.available 28-Aug-2017 11:25:19 (UTC+8) - dc.date.issued (上傳時間) 28-Aug-2017 11:25:19 (UTC+8) - dc.identifier (Other Identifiers) G0104356012 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/112154 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 104356012 zh_TW dc.description.abstract (摘要) 隨著科技的進步,更快更可靠的網路使得物聯網成為可能。而伴隨著物聯網科技的發展而來的,是如何處理從終端裝置中,所收集的巨量資料。本文希望能藉由影像處理,利用分解與比對來自物聯網裝置的串流影片,找出在一定影片檔案壓縮比率之下,最佳的影片品質。除此之外,我們在實驗中還能夠藉由操作不同的比較參數,讓影片壓縮的演算法能夠在不同的情境下,藉由調整參數權重,找出最適合該情境的壓縮演算法配置,藉此達到減少儲存空間需求的目的。在實驗中,我們建立基本的物聯網串流應用,並且能夠在與原影片96.5%的差異度之下,達到40%的影格儲存空間精簡優化。 zh_TW dc.description.abstract (摘要) The progression of cheaper, faster and more reliable Internet technology makes Internet of Things (IoT) realized in life. While tremendous data are collected from end devices, scalable and effective data compression techniques are needed to balance storage and precision. This paper presents an adjustable content-sensitive datacompression approach for IOT streaming services and applications. Specifically, we apply frame similarity on various aspects such as illumination and structure tostreaming frames, and are able to keep sufficient differences among steaming data while reducing significant amount of storage. We setup a general iot application platform in practice and show that with the presented approach, we are able to keep 96.5% precision with 40% frame reduction on the steaming data collected in real life. en_US dc.description.tableofcontents 1 Introduction 12 Related Work 32.1 Video Compression 32.2 Image Processing 42.2.1 Mean Squared Error 42.2.2 Structural Similarity Index Measure(SSIM) 52.3 Prototyping for the Internet of Things system 62.3.1 Micro Computing Devices 62.3.2 Machine to Machine Communication 73 Methodology 93.1 Fixed Rate Compression 103.2 Flexible Rate Compression 114 EXPERIMENTS 144.1 System layout 154.2 Sample Data Generation 164.3 Base weights 184.4 Performance of Flexible Rate Compression 184.4.1 Compression Methods 184.4.2 Evaluation Method 194.4.3 Compression Method Comparison 194.5 Compression Rate 204.6 Weights 234.6.1 The Most Significant Factor 234.6.2 The Least Significant Factor 234.7 Results 244.8 Result Examination 295 EXPERIMENT SUMMARY 306 Conclusion 32References 32 zh_TW dc.format.extent 12177262 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104356012 en_US dc.subject (關鍵詞) 物聯網 zh_TW dc.subject (關鍵詞) 串流 zh_TW dc.subject (關鍵詞) 資料壓縮 zh_TW dc.subject (關鍵詞) 影像處理 zh_TW dc.subject (關鍵詞) IoT en_US dc.subject (關鍵詞) Streaming en_US dc.subject (關鍵詞) Data compression en_US dc.subject (關鍵詞) Image processing en_US dc.title (題名) 基於物聯網應用與服務之串流資料壓縮 zh_TW dc.title (題名) Content-sensitive Data Compression for IoT Streaming Services en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] L. Atzori, A. Iera, and G. Morabito, “The internet of things: A survey,” Computer networks, vol. 54, no. 15, pp. 2787–2805, 2010.[2] H. Sundmaeker, P. Guillemin, P. Friess, and S. Woelffl ́e, “Vision and challenges for realising the internet of things,” Cluster of European Research Projects on the Internet of Things, European Commision, 2010.[3] A. Prasad, K. Mamun, F. Islam, and H. Haqva, “Smart water quality monitoring system,” in Computer Science and Engineering (APWC on CSE), 2015 2nd Asia-Pacific World Congress on, pp. 1–6, IEEE, 2015.[4] M. Soliman, T. Abiodun, T. Hamouda, J. Zhou, and C.-H. Lung, “Smart home: Integrating internet of things with web services and cloud computing,” in Cloud Computing Technology and Science (CloudCom), 2013 IEEE 5th International Conference on, vol. 2, pp. 317–320, IEEE, 2013.[5] A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, “Internet of thingsfor smart cities,” IEEE Internet of Things journal, vol. 1, no. 1, pp. 22–32, 2014.[6] J. Wei, “How wearables intersect with the cloud and the internet of things: Con-siderations for the developers of wearables.,” IEEE Consumer Electronics Magazine, vol. 3, no. 3, pp. 53–56, 2014.[7] D. Sreekantha and A. Kavya, “Agricultural crop monitoring using iot-a study,” in Intelligent Systems and Control (ISCO), 2017 11th International Conference on,pp. 134–139, IEEE, 2017.[8] J. Chin and A. Tisan, “An iot-based pervasive body hydration tracker (pht),” in In-dustrial Informatics (INDIN), 2015 IEEE 13th International Conference on, pp. 437–441, IEEE, 2015.33[9] M. Kovatsch, S. Mayer, and B. Ostermaier, “Moving application logic from the firmware to the cloud: Towards the thin server architecture for the internet of things,”in Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2012 Sixth International Conference on, pp. 751–756, IEEE, 2012.[10] Y.-K. Chen, “Challenges and opportunities of internet of things,” in Design Automa-tion Conference (ASP-DAC), 2012 17th Asia and South Pacific, pp. 383–388, IEEE,2012.[11] K. Das and P. Havinga, “Evaluation of dect for low latency real-time industrial control networks,” in Sensor, Mesh and Ad Hoc Communications and Networks(SECON), 2013 10th Annual IEEE Communications Society Conference on, pp. 10–17, IEEE, 2013.[12] A. Gogawale, F. Khatib, P. Sontakke, and S. Saigaonkar, “Database-as-a-service for iot,” in Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on, pp. 1436–1438, IEEE, 2016.[13] H. Schwarz, D. Marpe, and T. Wiegand, “Overview of the scalable video coding extension of the h. 264/avc standard,” IEEE Transactions on circuits and systemsfor video technology, vol. 17, no. 9, pp. 1103–1120, 2007.[14] D. Le Gall, “Mpeg: A video compression standard for multimedia applications,” Communications of the ACM, vol. 34, no. 4, pp. 46–58, 1991.[15] K. Jeevan and S. Krishnakumar, “Compression of images represented in hexagonal lattice using wavelet and gabor filter,” in Contemporary Computing and Informatics(IC3I), 2014 International Conference on, pp. 609–613, IEEE, 2014.[16] Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on, vol. 2, pp. 1398–1402, IEEE,2003.34[17] A. Hore and D. Ziou, “Image quality metrics: Psnr vs. ssim,” in Pattern Recognition(ICPR), 2010 20th International Conference on, pp. 2366–2369, IEEE, 2010.[18] G. Chen, Y. Shen, F. Yao, P. Liu, and Y. Liu, “Region-based moving object detection using ssim,” in Computer Science and Network Technology (ICCSNT), 2015 4th International Conference on, vol. 1, pp. 1361–1364, IEEE, 2015.[19] S. Wang, A. Rehman, Z. Wang, S. Ma, and W. Gao, “Ssim-motivated rate-distortion optimization for video coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 4, pp. 516–529, 2012.[20] F. Raphel and S. Sameer, “Ssim based resource optimization for multiuser downlink ofdm video transmission systems,” in Region 10 Conference (TENCON), 2016 IEEE, pp. 1583–1586, IEEE, 2016.[21] “Raspberry pi.” https://www.raspberrypi.org/.[22] “Arduino.” https://www.arduino.cc/.[23] “Ros.” http://www.ros.org/. zh_TW