臺灣博碩士論文加值系統

近年來人們對於身體健康、環保等議題漸漸關注，加上政府的政策推動，對於空氣品質的監控需求越來越多。本研究中主要的目的乃運用無線感測網路技術以及雲端運算兩技術，設計一套室內空氣品質監控系統，利用ARIMA預測模型推估二氧化碳未來趨勢，其預測的結果導入模糊控制器當中，讓監控不再只是監控，同時具有決策以及分析的功能。此外，提出一套金字塔功能更新機制，增加監控的彈性，可分別進行(1)參數調整；(2)功能微調；(3)程式重建等三種模式軟體更新。透過CMMBCR演算法，一方面找出最短路徑增加更新效率，另一方面加入節點電量判斷機制，避開過度使用的節點，藉以延長感測時間。在進行程式碼更新時，採用SPIN方法確保封包可送達，並運用廣播的方式提高更新節點接收封包的機率，加快更新的時間，有效降低能源消耗。
由實驗結果可知，(1)無線感測軟體更新機制：在5*5的網路拓樸中傳送4 頁程式碼資料，在不同封包遺失率下，本研究方法跟Deluge比較，更新時間可減少19%，總耗能平均節省21%；(2)藉由程式碼的比對機制，可以降低83.3%的程式碼傳輸量，在4*1的直線拓樸下，散佈時間以及電量方面皆可節省約80%。(3)透過參數調整，資料傳輸量更可由程式重建的26,496 Bytes大幅降到22 Bytes；(4)ARIMA預測模型，使用每50分鐘計算其二氧化碳濃度平均值去預測未來10分鐘的二氧化碳濃度變化，可達96.73%的準確度；(5)在節能實驗下，利用模擬箱，模擬一整天的工作情況，維持在最佳的工作環境下，同時又能節省55%的能源。

Individuals are becoming progressively more aware of health and the environment issues. In response to community concerns, government policy is increasingly focused on air quality monitoring as a means of measuring environmental health. This paper describes the construction of an air quality monitoring system that employs wireless sensor networks (WSN) in conjunction with cloud computing technology. The advantage of the system is the multifunctional capability that supports predicting for future trends in carbon dioxide using an ARIMA model, while integrating the forecast results into a fuzzy controller, which enables decision making and analysis. The flexibility of the system can be enhanced using a pyramid function update mechanism comprised of three components that include parameters adjustment, functional trimming, and program reconstruction. The benefit associated with employing a CMMBCR algorithm to calculate the transmission path of the sensors is a mechanism for determining the shortest path, which not only increases efficiency but also decreases the overall energy requirements, effectively extending the period of time the sensors can operate. The transmission period and energy use of the system is optimized by employing the SPIN method to transmit data, ensuring that retention of packets and broadcasting data is achieved. Results from the study have demonstrated a number of advantages from the use of the multifunctional air quality monitoring system. The advantages are associated with the software update mechanism for WSNs, a reduction in code image size, optimization of parameter adjustment, the accuracy of the ARIMA prediction model and reduced operational energy requirements. The software update mechanism employed in the study is comparable to Deluge when sending 12 pages in a 5 x 5 network topology with a varying rate of packet loss, while reducing transmission time by 19% and overall energy use by 21%. Achieved reductions in code image size from the system method were approximately 83.3%, with the transmission time and energy savings made in a 4 x 1 network topology recorded at 80%. The amount of transmission data was reduced from 26,496 bytes to 22 bytes via adjustment of the parameters used in the system. Furthermore, by calculating the average carbon dioxide concentration over 50 minutes and then extrapolating the data to predict the next 10 minutes using the ARIMA prediction model, accuracies of 96% were achieved. Investigations into the energy saving potential of the system using simulation boxes to replicated daily work situations revealed energy savings of 55% for an optimal working environment.

目錄
長庚大學碩士論文指導教授推薦書
長庚大學碩士論文口試委員會審定書
長庚大學碩士論文著作授權書 IV
致謝 V
中文摘要 VI
ABSTRACT VIII
目錄 X
表目錄 XII
圖目錄 XIII
第1章 緒論 - 1 -
1.1研究背景 - 1 -
1.2研究動機 - 4 -
1.3研究目的 - 6 -
1.4論文架構 - 7 -
第2章 相關研究 - 8 -
2.1無線感測網路 - 8 -
2.2雲端運算 - 11 -
2.3預測模型 - 14 -
2.4自動控制 - 20 -
第3章 研究方法 - 24 -
3.1環境參數接收 - 25 -
3.2無線感測器軟體更新 - 27 -
3.3自動控制平台 - 45 -
3.4網頁平台與雲端平台 - 56 -
第4章 實驗方法與實作成果 - 59 -
4.1系統實作成果 - 59 -
4.2實驗方法設計與結果 - 60 -
第5章 結論與未來展望 - 78 -
參考文獻 - 82 -

表目錄
表 1.1空氣品質標準建議值 - 3 -
表 2.1節點執行動作消耗電量統計表 - 9 -
表 2.2現行雲端平台比較表 - 14 -
表 2.3 (p,q)辨認準則 - 18 -
表 3.1指令/資料封包格式 - 26 -
表 3.2興趣封包規劃表 - 34 -
表 3.3廣告訊息封包規劃表 - 41 -
表 3.4資料要求訊息封包規劃表 - 42 -
表 3.5資料訊息封包規劃表 - 44 -
表 3.6 CO2_L模糊控制規則 - 52 -
表 3.7 CO2_M模糊控制規則 - 53 -
表 3.8 CO2_H模糊控制規則 - 53 -
表 4.1 Loss Rate 標準差統計表 - 69 -
表 4.2節點(1) 5/10、5/11每小時人員統計表 - 72 -

圖目錄
圖 1.1現階段空氣品質推動重要議題 - 4 -
圖 2.1雲端服務應用於健康照護系統架構 - 8 -
圖 2.2程式碼切割示意圖 - 10 -
圖 2.3軟體更新流程 - 10 -
圖 2.4二氧化碳原始數列自我相關函數圖 - 17 -
圖 2.5 AR之相關函數 - 18 -
圖 2.6 MA之相關函數 - 18 -
圖 2.7模糊化步驟示意圖 - 23 -
圖 3.1系統架構圖 - 25 -
圖 3.2輪詢機制(Polling)示意圖 - 26 -
圖 3.3金字塔功能更新機制 - 28 -
圖 3.4參數調整之流程圖 - 29 -
圖 3.5感測節點韌體更新流程圖(左)與對應步驟之示意圖(右) - 31 -
圖 3.6資料封包傳送示意圖 - 33 -
圖 3.7程式碼差異比較 - 38 -
圖 3.8程式碼資料傳遞範例 - 39 -
圖 3.9 Code Image傳輸架構圖 - 39 -
圖 3.10空氣品質調適性ARIMA模型示意圖 - 45 -
圖 3.11二氧化碳原始數列相關函數 - 46 -
圖 3.12二氧化碳差分後相關函數 - 46 -
圖 3.13殘差自我相關檢定圖 - 47 -
圖 3.14模糊邏輯控制器之行為示意圖 - 48 -
圖 3.15模糊邏輯控制器系統圖 - 49 -
圖 3.16 Temp之模糊變數與歸屬函數 - 51 -
圖 3.17 H之模糊變數與歸屬函數 - 51 -
圖 3.18 CO2之模糊變數與歸屬函數 - 51 -
圖 3.19 Indicator之模糊變數與歸屬函數 - 52 -
圖 3.20Mamdani之Min.-Max.推論法 - 55 -
圖 3.21 Mamdani.之推論結果 - 56 -
圖 3.22 程式碼上傳流程 - 56 -
圖 3.23感測資料瀏覽流程 - 58 -
圖 4.1感測環境介紹 - 59 -
圖 4.2 感測資料觀看畫面 - 60 -
圖 4.3 5*5拓撲關係圖 - 62 -
圖 4.4在不同網路狀態下程式碼散佈時間比較圖 - 63 -
圖 4.5整體網路節點耗能比較 - 63 -
圖 4.6更新節點耗能比較 - 64 -
圖 4.7閘道器節點耗能比較 - 64 -
圖 4.8直線拓樸 - 65 -
圖 4.9程式碼比對機制對散佈時間的影響 - 65 -
圖 4.10程式碼比對機制對耗能的影響 - 66 -
圖 4.11金字塔功能更新機制之時間比較 - 67 -
圖 4.12金字塔功能更新之耗能比較 - 67 -
圖 4.13本研究(左)與Deluge(右)節點更新示意圖 - 69 -
圖 4.14佈點位置示意圖 - 71 -
圖 4.15佈點現場圖 - 71 -
圖 4.16不同擷取平均值下預測模型的表現 - 73 -
圖 4.17ARIMA模型預測結果 - 74 -
圖 4.18回饋實驗環境圖 - 76 -
圖 4.19 二氧化碳氣體輸入量 - 76 -
圖 4.20自動控制模組輸出與二氧化碳濃度之變化圖 - 77 -

參考文獻
[1] WHO, “Indoor air pollution and health” [Online]. Available: http://www.who.int/mediacentre/factsheets/fs292/en/index.html (last date visited: June 10, 2010).
[2] JD Spengler, K Sexton, “Indoor air pollution: a public health perspective, ” Science, Vol. 221, no. 4605 pp. 9-17, 1983
[3] W. Fisk, A. Rosenfeld, “Estimates of improved productivity and health from better indoor environments,” International Journal of Air Quality and Climate, 1997 (7): 158-172.
[4] W. Fisk, A. Rosenfeld. “Potential nationwide improvements in productivity and health from better indoor environments,” Proceedings of the 1998 Summer Study on Energy Efficiency in Buildings, American Council for an Energy-Efficiency Economy.
[5] 蘇慧貞，室內空氣品質標準草案及管制策略探討，行政院環境保護署研究報告，1999年6月。
[6] 行政院環保署, “室內空氣品質建議值”. [Online]. Available: http://w3.epa.gov.tw/epalaw/docfile/044310.pdf (last date visited: June 10, 2010).
[7] M. Alan, C. David, P. Joseph, S. Robert, and A. John, “Wireless sensor networks for habitat monitoring,” Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications Atlanta, Georgia, USA: ACM, pp. 88-97, 2002
[8] Chris Otto, Aleksandar Milenkovic, Corey Sandres, and Emil Jovanov, “System Architecture Of A Wireless Body Area Sensor Network For Ubiquitous Health Monitoring,” Journal of Mobile Multimedia, vol. 1, no. 4, pp. 307-326, 2006.
[9] G. Werner-Allen, K. Lorincz, M. Ruiz, O. Marcillo, J. Johnson, J. Lees, and M. Welsh, “Deploying a wireless sensor network on an active volcano,” Internet Computing, IEEE, vol. 10, no. 2, pp. 18-25, 2006.
[10] Xuan Hung Le, Sungyoung Lee, Phan Tran Ho Truc, La The Vinh, Asad Masood Khattak, Manhyung Han, Dang Viet Hung, Mohammad M. Hassan, Miso (Hyung-Il) Kim, Kyo-Ho Koo, Young-Koo Lee, Eui-Nam Huh, “secured WSN-integrated Cloud Computing for u-Life Care,” 7th IEEE Consumer Communications and Networking Conference, pp. 1-2, Jan. 2010
[11] Mainwaring, A., J. Polastre, R. Szewczyk, D.Culler and J. Anderson, “ Wireless Sensor Networks for Habitat Monitoring,” Proceedings of the First ACM International Workshop on Wireless Sensor Networks and Applications, pp. 88-97, 2002
[12] Cardell-Oliver, R., K. Smettem, M. Kranz, and Mayer K, “Field Testing a Wireless Sensor Network for Reactive Environmental Monitoring,” Proceedings of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pp. 7-12, 2004.
[13] 李瑋倫，無線感測網路軟體更新技術之研究，碩士論文，長庚大學，資訊工程研究所，桃園，2007。
[14] C. K. Toh, “Maximum battery life routing to support ubiquitous mobile computing in wireless ad hoc networks,” Communications Magazine, IEEE, vol. 39, no. 6, pp. 138-147, 2001.
[15] N. Bambos, “Toward power-sensitive network architectures in wireless communications: concepts, issues, and design aspects,” Personal Communications, IEEE [see also IEEE Wireless Communications], vol. 5, no. 3, pp. 50-59, 1998.
[16] C. K. Toh, H. Cobb, and D. A. Scott, “Performance evaluation of battery-life-aware routing schemes for wireless ad hoc networks,” IEEE International Conference on Communications, vol. 9, pp. 2824-2829, 2001
[17] R. H. Wendi, K. Joanna, and B. Hari, “Adaptive protocols for information dissemination in wireless sensor networks,” Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking, pp. 174-185, 1999
[18] 鐘國家、黃勝榮、洪國鈞, “基於雲端服務之公文線上簽核資安偵測系統,” Journal of Computer Science and Application, Vol.6, No.1, pp. 119-140, June 2010
[19] Amazon, “EC2” [Online]. Available: http://aws.amazon.com/ec2 (last date visited: June 10, 2011).
[20] Google App Engine, “Google App Engine” [Online]. Available: http://code.google.com/intl/zh-TW/appengine/ (last date visited: June 10, 2011).
[21] Windows Azure, “Windows Azure” [Online]. Available: http://oakleafblog.blogspot.com/ (last date visited: June 10, 2011).
[22] Rajkumar Buyya, Chee Shin Yeo1, and Srikumar Venugopal, “Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities,” The 10th IEEE International Conference on High Performance Computing and Communications, pp.5-13, Sept. 2008
[23] Box, G. E. P., & Jenkins, G.M., “Time Series Analysis Forecasting and Control, ” Management Science, vol.17, no.4, pp.141-164, 1970
[24] AIC, “AIC” [Online]. Available: http://en.wikipedia.org/wiki/Akaike_information_criterion (last date visited: June 10, 2011).
[25] SBC, “SBC” [Online]. Available: http://en.wikipedia.org/wiki/Bayesian_information_criterion (last date visited: June 10, 2011).
[26] Jaques Reifman, Srinivasan Rajaraman, Andrei Gribok, and W. Kenneth Ward, “Predictive Monitoring for Improved Management of Glucose Levels,” Journal of Diabetes Science and Technology, Vol. 1, pp.478-486, July 2007
[27] L. A. Zadeh, “Fuzzy Set,” Information and Control, pp. 338-353,1965
[28] Jose E. Naranjo, Carlos Gonzalez, Ricardo Garcia, Teresa de Pedro, and Miguel A. Sotelo, “Using Fuzzy Logic in Automated Vehicle Control,” IEEE intelligent Systems, vol. 22, pp. 36-45, 2007
[29] Lee, C. C., “Fuzzy Logic in Control Systems:Fuzzy Logic Controller - Part I and Part II, ” IEEE Transactions on Systems, Man and Cybernetics, vol. 20, pp. 419-435, 1990
[30] 國立台灣大學無線感測網路中心, “Super node”. [Online]. Available: http://www.wsnc.ntu.edu.tw/Files/SuperNode.pdf (last date visited: June 10, 2011).
[31] C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva, “Directed diffusion for wireless sensor networking,” IEEE/ACM Transactions on Networking, vol. 11, no. 1, pp. 2-16, 2003.
[32] Z. Xiao and B. Sarikaya, “Code Dissemination in Sensor Networks with MDeluge,” Sensor and Ad Hoc Communications and Networks, pp. 661-666, Sept. 2006
[33] 張載享，以模糊理論設計變頻式之空調驅動器，碩士論文，逢甲大學，電機工程學系碩士班，台中，2004。
[34] W. H. Jonathan and C. David, “The dynamic behavior of a data dissemination protocol for network programming at scale,” Proceedings of the 2nd international conference on Embedded networked sensor systems Baltimore, MD, USA: ACM, pp. 81-94., 2004
[35] S. Victor, H. Mark, C. Bor-rong, W. A. Geoff, and W. Matt, “Simulating the power consumption of large-scale sensor network applications,” Proceedings of the 2nd international conference on Embedded networked sensor systems Baltimore, MD, USA: ACM, pp. 188-200., 2004
[36] 勞工安全衛生研究所, “二氧化碳中毒”. [Online]. Available: http://www.iosh.gov.tw/Publish.aspx?cnid=16&P=235 (last date visited: June 10, 2011).