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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 data
compression approach for IOT streaming services and applications. Specifically, we apply frame similarity on various aspects such as illumination and structure to
streaming 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 things
for 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 systems
for 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, Fangen_US
dc.contributor.author (Authors) 許峻基zh_TW
dc.contributor.author (Authors) Hsu, Chun-Chien_US
dc.creator (作者) 許峻基zh_TW
dc.creator (作者) Hsu, Chun-Chien_US
dc.date (日期) 2017en_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) G0104356012en_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) 104356012zh_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 data
compression approach for IOT streaming services and applications. Specifically, we apply frame similarity on various aspects such as illumination and structure to
streaming 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 1
2 Related Work 3
2.1 Video Compression 3
2.2 Image Processing 4
2.2.1 Mean Squared Error 4
2.2.2 Structural Similarity Index Measure(SSIM) 5
2.3 Prototyping for the Internet of Things system 6
2.3.1 Micro Computing Devices 6
2.3.2 Machine to Machine Communication 7
3 Methodology 9
3.1 Fixed Rate Compression 10
3.2 Flexible Rate Compression 11
4 EXPERIMENTS 14
4.1 System layout 15
4.2 Sample Data Generation 16
4.3 Base weights 18
4.4 Performance of Flexible Rate Compression 18
4.4.1 Compression Methods 18
4.4.2 Evaluation Method 19
4.4.3 Compression Method Comparison 19
4.5 Compression Rate 20
4.6 Weights 23
4.6.1 The Most Significant Factor 23
4.6.2 The Least Significant Factor 23
4.7 Results 24
4.8 Result Examination 29
5 EXPERIMENT SUMMARY 30
6 Conclusion 32
References 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/#G0104356012en_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) IoTen_US
dc.subject (關鍵詞) Streamingen_US
dc.subject (關鍵詞) Data compressionen_US
dc.subject (關鍵詞) Image processingen_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 Servicesen_US
dc.type (資料類型) thesisen_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 things
for 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 systems
for 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