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題名 以使用者有感為中心的環境物聯網研究
Internet of Environmental Things : A Human Centered Approach
作者 沙齊
Mahajan, Sachit
貢獻者 陳伶志<br>蔡子傑
Chen, Ling-Jyh<br>Tsai, Tzu-Chieh
沙齊
Sachit Mahajan
關鍵詞 物聯網
空氣污染
人本計算
Internet of things
Air pollution
Human centered computing
日期 2019
上傳時間 3-六月-2019 13:08:25 (UTC+8)
摘要 隨著都市化和工業化，人們對於空氣品質的擔憂也日益漸增，因為空氣品質直接影響了人類的健康和發展。而空氣不見得會是如其所見的乾淨，甚至可能充滿著有害的顆粒物質：小於 2.5微米的細懸浮微粒（PM2.5），這些微小的懸浮微粒對人類有致命的危險，因其極小的尺度可以直接穿過人的肺部，而後直接進到血液中，造成危及生命的疾病。一個有效的資料收集與分析方法搭配視覺化的呈現，可以協助人們更有效率的監控環境以及優化生活中的決策。為此，本論文提出了一個多管齊下的方法，包括群眾外包（Crowd-Sourcing）和開源的框架的設計、實施及評估，並利用物聯網（Internet of Things）平台、機器學習技術開發出新的空氣品質傳感解決方案，以提供服務給民眾與提高對於空品相關議題的意識，同時有助於提升民眾生活品質。
但是要將這樣的系統以即時的方式運作，有許多挑戰需要克服。有效地處理如此大量的資料是一項艱鉅且繁瑣的工作，而要確保有檢測出每一個異常的點也是項極具挑戰性的任務，除此之外，即時預測系統必須做到具備準確和擴展性的同時也盡量降低運算時間，這更是項困難的挑戰。本論文遵循以人為本的理念，使用物聯網裝置搭配認知計算（cognitive computing）來生成大數據資料，而後將這些資料用於強化空氣品質管理和預測系統。典型的流程包含從感測器獲得資料、分析資料、進行預測、視覺化資料，以及當空氣品質異常時發出預警的服務。本論文的主要貢獻為以下四點：

1. 首先，本論文解決了資料收集和可靠性的問題。為了解決此問題，本論文提出了異常偵測框架（Anomaly Detection Framework, ADF），異常偵測框架可以有效地識別原始測量數據中的異常值，並推斷異常事件的發生。此外，此框架還可以評估系統中每個裝置的屬性和狀態，舉例來說，裝置可能是在部署在室內（屬性）或是接近高頻污染源（狀態）。另外，本論文也提出了一套能將能源消耗、資料收集和成本最佳化的移動式 PM2.5 感測模型，並利用自行車作為移動工具進行實測。這個成果幫助我們開發出可以在移動中收集資料，並節省時間及消耗能源的系統。

2. 接著，本文探討了設計即時可擴展性預測模型的問題。設計這種預測系統的主要挑戰之一是確保擁有高精準度和可接受的計算時間，為了解決這個問題，我們先對現有的預測模型進行比較分析，而後提出一基於神經網絡（neural networks）的混合模型（Hybrid model）來進行每小時的 PM2.5 預測，其性能評估是通過與線性模型和其他現行主流模型來進行結果的比較。該模型已實際部署並持續運行中，為台灣 2000 多個監測節點提供預測服務。

3. 而後，本論文解決了建立小尺度空氣污染圖的挑戰，並使用這些圖形結果來設計一種能夠幫助城市居民減少空氣污染物暴露的演算法 - 乾淨空氣導航（Clean Air Routing, CAR）演算法。該演算法會推薦從出發地到目的地的最佳空氣品質路徑，除了會基於台灣的公路網對於 PM2.5 的資料進行空間和時間插值，也會根據不同的旅行模式進行評估。最後將結果與Google 地圖所提供的導航路徑結果和最短路徑（Dijkstra）演算法所得的結果進行比較。

4. 最後，本論文介紹了基於本研究的結果而開發的即時應用，包括空氣品質視覺化服務、用於了解 PM2.5 變化趨勢的動畫服務、短期 PM2.5 預測服務、基於物聯網的個人空氣品質聊天機器人以及基於乾淨空氣導航演算法的路線推薦應用網頁。
此外，本論文藉由與其他先進主流系統進行比較分析，來評估應用系統的表現。本研究所提出的系統架構應用層面，並不僅限應用於空氣品質資料，亦可用於未來新開發之感測系統監測資料。
With the continuous urbanization and industrial growth, the concern about deteriorating air quality is also increasing. This directly impacts the human health and sustainable development. Sometimes even the air that looks clean isn`t clean and is filled with dangerous particulate matter; no more than 2.5 microns (PM2.5). These particles are deadly and can cause life threatening diseases. An effective way to collect, analyze and scientifically visualize the air quality data can help us continuously monitor the environment and can facilitate people`s decision making. This work proposes a multi-pronged approach that encompasses around design, implementation and evaluation of a framework that exploits crowd-sourcing and crowd-sharing using IoT (Internet of Things) platform and machine learning techniques to develop novel solutions to do air quality sensing and provide services to the people that will not just raise awareness related to air quality problem but also assist them in day to day living.
But there are many challenges that need to be addressed before such a system can be deployed in real-time. Efficiently handling such a large volume of data is a tedious job and making sure that any anomaly is detected is also a challenging task. Other than that, having an accurate and scalable forecast system with low computation time is also a difficult task. In this dissertation, a human-centered approach is followed. The idea is to use IoT devices and cognitive computing to generate big data which can be further used to enhance air quality management systems and forecasting. A typical case will include the collection and storage of data obtained from the sensors, data analytics, prediction, visualization and an alert message service in case of unusual behavior in the air quality. The main contributions of this dissertation are:

1. Initially, this dissertation addresses the issue of data collection and reliability. To tackle the issue, an Anomaly Detection Framework (ADF) is proposed that is efficient enough in identifying outliers in the raw measurement data and inferring anomalous events emission. ADF can evaluate attributes and status of each device in the system; that is, whether a device is deployed indoors, or close to an emission source. Also, an energy, data and cost-efficient model for mobile opportunistic PM2.5 sensing via bicycles is proposed which is then implemented and tested for real world scenarios. The results helped us develop a system which would gather data on the move and at the save time would save device energy.
2. Next, this dissertation addresses the problem of designing a scalable forecast model that can be implemented in real-time. One of the major challenges in designing such a forecast system is ensuring high accuracy and an acceptable computation time. To address this issue, we begin with performing a comparative analysis of already existing forecast models. Later on, a Hybrid model based on neural networks is proposed to perform hourly PM2.5 forecast. The performance evaluation of the Hybrid model is done by comparing it with baseline models and other state of the art works. The model has been implemented in real-time and is used to provide forecast service for more than 2000 monitoring nodes in Taiwan.
3. Next, the dissertation deals with the challenge of creating fine-grained air pollution maps and then using those maps to design an algorithm which would assist urban dwellers to reduce their exposure to airborne pollutants. We propose the Clean Air Routing (CAR) algorithm that recommends health-optimal paths from origin to the destination. PM2.5 data are spatially and temporally interpolated on the Taiwan`s road network. The algorithm is evaluated for different travel modes as well as a comparison is provided with Google Maps result and shortest path (Dijkstra).
4. The final part of this dissertation explains about the real-time applications that have been developed based on the results obtained during this research. The applications include visualization service, an animation service to understand the trend in PM2.5, a short-term PM2.5 forecast service, an IoT enabled personal air quality chatbot assistant and a route recommendation application based on CAR algorithm.
Evaluation of the framework`s components has been conducted by performing a comparative analysis with state-of-the-art systems. The proposed framework is not just limited to air
quality data but it can potentially be applied to other emerging data sensing systems as well.
參考文獻 [1] S. Mahajan, L.-J. Chen, and T.-C. Tsai, “Short-term pm2. 5 forecasting using expo- nential smoothing method: A comparative analysis,” Sensors, vol. 18, no. 10, p. 3223, 2018.
[2] A. J. Cohen, M. Brauer, R. Burnett, H. R. Anderson, J. Frostad, K. Estep, K. Balakr- ishnan, B. Brunekreef, L. Dandona, R. Dandona et al., “Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the global burden of diseases study 2015,” The Lancet, vol. 389, no. 10082, pp. 1907–1918, 2017.
[3] D. Hasenfratz, “Enabling large-scale urban air quality monitoring with mobile sensor nodes,” Ph.D. dissertation, ETH Zurich, 2015.
[4] N. Sebe, “Human-centered computing: Challenges and perspectives,” in 23rd Interna- tional Conference on Data Engineering Workshop. IEEE, 2007, pp. 2–2.
[5] A. Anjomshoaa, S. Mora, P. Schmitt, and C. Ratti, “Challenges of drive-by iot sensing for smart cities: City scanner case study,” in 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. ACM, 2018, pp. 1112–1120.
[6] E. G. Snyder, T. H. Watkins, P. A. Solomon, E. D. Thoma, R. W. Williams, G. S. Hagler, D. Shelow, D. A. Hindin, V. J. Kilaru, and P. W. Preuss, “The changing paradigm of air pollution monitoring,” 2013.
[7] H. Pritchard and J. Gabrys, “From citizen sensing to collective monitoring: Work- ing through the perceptive and affective problematics of environmental pollution,” GeoHumanities, vol. 2, no. 2, pp. 354–371, 2016.
120 References
[8] N. Castell, M. Kobernus, H.-Y. Liu, P. Schneider, W. Lahoz, A. J. Berre, and J. Noll, “Mobile technologies and services for environmental monitoring: The citi-sense-mob
approach,” Urban climate, vol. 14, pp. 370–382, 2015.
[9] C. Leonardi, A. Cappellotto, M. Caraviello, B. Lepri, and F. Antonelli, “Secondnose: an air quality mobile crowdsensing system,” in 8th Nordic Conference on Human- Computer Interaction: Fun, Fast, Foundational. ACM, 2014, pp. 1051–1054.
[10] P. Zappi, E. Bales, J. H. Park, W. Griswold, and T. Š. Rosing, “The citisense air quality monitoring mobile sensor node,” in 11th ACM/IEEE Conference on Information Processing in Sensor Networks, Beijing, China, 2012.
[11] P.Dutta,P.M.Aoki,N.Kumar,A.Mainwaring,C.Myers,W.Willett,andA.Woodruff, “Common sense: participatory urban sensing using a network of handheld air quality monitors,” in 7th ACM conference on embedded networked sensor systems. ACM,
2009, pp. 349–350.
[12] B. J. Hubbell, A. Kaufman, L. Rivers, K. Schulte, G. Hagler, J. Clougherty, W. Cascio, and D. Costa, “Understanding social and behavioral drivers and impacts of air quality sensor use,” Science of The Total Environment, vol. 621, pp. 886–894, 2018.
[13] A. Commodore, S. Wilson, O. Muhammad, E. Svendsen, and J. Pearce, “Community- based participatory research for the study of air pollution: A review of motivations, approaches, and outcomes,” Environmental monitoring and assessment, vol. 189, no. 8, p. 378, 2017.
[14] S. Devarakonda, P. Sevusu, H. Liu, R. Liu, L. Iftode, and B. Nath, “Real-time air qual- ity monitoring through mobile sensing in metropolitan areas,” in 2nd ACM SIGKDD international workshop on urban computing. ACM, 2013, p. 15.
[15] L.-J. Chen, Y.-H. Ho, H.-C. Lee, H.-C. Wu, H.-M. Liu, H.-H. Hsieh, Y.-T. Huang, and S.-C. C. Lung, “An open framework for participatory pm2.5 monitoring in smart cities,” IEEE Access, vol. 5, pp. 14 441–14 454, July 2017.
[16] PM2.5 concentration indexes and activity advices. [Online]. Available: http: //taqm.epa.gov.tw/taqm/tw/fpmi.aspx
[17] PM2.5 Open Data Portal. [Online]. Available: https://pm25.lass-net.org
[18] S. Mahajan, H. M. Liu, T. Y. Huang, T. C. Tsai, and L. J. Chen, “Opportunistic pm2.5 sensing: A feasibility study,” in GLOBECOM 2017 - 2017 IEEE Global Communications Conference, Dec 2017, pp. 1–6.
[19] Q. Liang, X. Cheng, S. C. Huang, and D. Chen, “Opportunistic sensing in wireless sensor networks: Theory and application,” IEEE Transactions on Computers, vol. 63, no. 8, pp. 2002–2010, Aug. 2014.
[20] Z. Wang et al., “Opportunistic sensing for object recognition-a unified formulation for dynamic sensor selection and feature extraction,” in IEEE International Conference on Multimedia and Expo (ICME), 2013.
[21] M. A. A. H. Khan, H. M. S. Hossain, and N. Roy, “Sensepresence: Infrastructure-less occupancy detection for opportunistic sensing applications,” in IEEE International Conference on Mobile Data Management, 2015.
[22] Y.-T. Huang, Y.-C. Chen, J.-H. Huang, L.-J. Chen, and P. Huang, “YushanNet: A Delay-Tolerant Wireless Sensor Network for Hiker Tracking in Yushan National Park,” in The International Conference on Mobile Data Management Systems, Services and Middleware, 2009.
[23] R. O. Vasconcelos et al., “An adaptive middleware for opportunistic mobile sensing,” in International Conference on Distributed Computing in Sensor Systems, 2015.
[24] L. L. D. Zhao, H. Ma and J. Zhao, “On opportunistic coverage for urban sensing,” in IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, 2013.
[25] G. Varela et al., “An integrated system for urban pollution monitoring through a public transportation based opportunistic mobile sensor network,” in IEEE Interna- tional Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2009.
[26] M. Fiore, A. Nordio, and C. F. Chiasserini, “Driving factors toward accurate mobile opportunistic sensing in urban environments,” IEEE Trans. on Mobile Computing, vol. 15, no. 10, pp. 2480–2493, Oct. 2016.
[27] W. Kleiminger, C. Beckel, and S. Santini, “Household occupancy monitoring us- ing electricity meters,” in ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015.
122 References
[28] S.Kang,S.Kwon,C.Yoo,S.Seo,K.Park,J.Song,andY.Lee,“Sinabro:opportunistic and unobtrusive mobile electrocardiogram monitoring system,” in 15th ACM Workshop on Mobile Computing Systems and Applications, 2014.
[29] V. Arnaboldi, M. Conti, F. Delmastro, G. Minutiello, and L. Ricci, “Sensor mobile enablement (sme): A light-weight standard for opportunistic sensing services,” in IEEE International Conference on Pervasive Computing and Communications Workshops, 2013.
[30] G.S.Tuncay,G.Benincasa,andA.Helmy,“Participantrecruitmentanddatacollection framework for opportunistic sensing: A comparative analysis,” in 8th ACM MobiCom Workshop on Challenged Networks, 2013.
[31] L. A. Castro et al., “Collaborative opportunistic sensing with mobile phones,” in Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, 2014.
[32] T. Higuchi, H. Yamaguchi, T. Higashino, and M. Takai, “A neighbor collaboration mechanism for mobile crowd sensing in opportunistic networks,” in IEEE International Conference on Communications (ICC), 2014.
[33] C. Cornelius, A. Kapadia, D. Kotz, D. Peebles, M. Shin, and N. Triandopoulos, “Anonysense: privacy-aware people-centric sensing,” in 6th international conference
on Mobile systems, applications, and services. ACM, 2008, pp. 211–224.
[34] G. S. Tuncay, G. Benincasa, and A. Helmy, “Autonomous and distributed recruitment and data collection framework for opportunistic sensing,” in 18th Annual International Conference on Mobile Computing and Networking, 2012.
[35] D. Zhao, H. Ma, S. Tang, and X. Y. Li, “Coupon: A cooperative framework for building sensing maps in mobile opportunistic networks,” IEEE Trans. on Parallel and Distributed Systems, vol. 26, no. 2, pp. 392–402, Feb. 2015.
[36] R. Loomba, L. Shi, and B. Jennings, “State-machine driven opportunistic sensing by mobile devices,” in IEEE Global Communications Conference, 2014.
[37] S. Sigg and X. Fu, “Social opportunistic sensing and social centric networking: En- abling technology for smart cities,” in ACM International Workshop on Wireless and Mobile Technologies for Smart Cities, 2014.
[38] L.-J. Chen, Y.-H. Ho, H.-H. Hsieh, S.-T. Huang, H.-C. Lee, and S. Mahajan, “Adf: an anomaly detection framework for large-scale pm2. 5 sensing systems,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 559–570, 2018.
[39] Us epa smart city air challenge. [Online]. Available: https://www.challenge.gov/ challenge/smart-city-air-challenge/
[40] X. Tang, “An Overview of Air Pollution Problem in Megacities and City Clusters in China,” AGU Spring Meeting Abstracts, May 2007.
[41] VUFO - NGO Resource Centre Vietnam. (2013, Sept. 19) Vietnam Named among Top Ten Nations with Worst Air Pollution. [Online]. Available: http://www.ngocentre. org.vn/news/vietnam-named-among-top-ten-nations-worst-air-pollution
[42] B. Ostro, Outdoor air pollution: Assessing the environmental burden of disease at national and local levels, ser. WHO Environmental Burden of Disease Series. World Health Organization, 2004, no. 5.
[43] Y.-F. Xing, Y.-H. Xu, M.-H. Shi, and Y.-X. Lian, “The impact of PM2.5 on the human respiratory system,” Journal of Thoracic Disease, vol. 8, no. 1, pp. 69–74, January 2016.
[44] AirNow. [Online]. Available: https://airnow.gov
[45] Air Quality Data - Central Pollution Control Board. [Online]. Available: http://cpcb.nic.in/RealTimeAirQualityData.php
[46] Air pollution - Euripean Environment Agency. [Online]. Available: https: //www.eea.europa.eu/themes/air/intro
[47] M. Markiewicz, “A Review of Mathematical Models for the Atmospheric Dispersion of Heavy Gases. Part I. A Classification of Models,” Ecological Chemistry and Engineering S, vol. 19, no. 3, pp. 297–314, July 2012.
[48] S.-C. C. Lung, I.-F. Maod, and L.-J. S. Liu, “Residents’ particle exposures in six different communities in Taiwan,” Science of The Total Environment, vol. 377, no. 1, pp. 81–92, May 2007.
[49] S.-C. C. Lung, P.-K. Hsiao, T.-Y. Wen, C.-H. Liu, C. B. Fu, and Y.-T. Cheng, “Variabil- ity of intra-urban exposure to particulate matter and co from asian-type community pollution sources,” Atmospheric Environment, vol. 83, pp. 6–13, February 2014.
[50] M. Alvarado, F. Gonzalez, A. Fletcher, and A. Doshi, “Towards the Development of a Low Cost Airborne Sensing System to Monitor Dust Particles after Blasting at Open-Pit Mine Sites,” Sensors, vol. 15, pp. 19 667–19 687, 2015.
[51] M. Budde, R. E. Masri, T. Riedel, and M. Beigl, “Enabling low-cost particulate matter measurement for participatory sensing scenarios,” in International Conference on Mobile and Ubiquitous Multimedia, 2013.
[52] Y. Cheng, X. Li, Z. Li, S. Jiang, Y. Li, J. Jia, and X. Jiang, “AirCloud: A Cloud-based Air-Quality Monitoring System for Everyone,” in ACM SenSys, 2014.
[53] S. Devarakonda, P. Sevusu, H. Liu, R. Liu, L. Iftode, and B. Nath, “Real-time air quality monitoring through mobile sensing in metropolitan areas,” in ACM SIGKDD International Workshop on Urban Computing, 2013.
[54] Y. Gao, W. Dong, K. Guo, X. Liu, Y. Chen, X. Liu, J. Bu, and C. Chen, “Mosaic: A low-cost mobile sensing system for urban air quality monitoring,” in IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications. IEEE, 2016, pp. 1–9.
[55] K. Weekly, D. Rim, L. Zhang, A. M. Bayen, W. W. Nazaroff, and C. J. Spanos, “Low- cost coarse airborne particulate matter sensing for indoor occupancy detection,” in 2013 IEEE International Conference on Automation Science and Engineering (CASE). IEEE, 2013, pp. 32–37.
[56] Y. Zhuang, F. Lin, E.-H. Yoo, and W. Xu, “Airsense: A portable context-sensing device for personal air quality monitoring,” in Workshop on Pervasive Wireless Healthcare. ACM, 2015, pp. 17–22.
[57] Array of things. [Online]. Available: https://arrayofthings.github.io
[58] Taipei AirBox. [Online]. Available: http://pm2.5.taipei/
[59] OpenSense at ETH Zurich. [Online]. Available: http://www.opensense.ethz.ch/
[60] AirCasting. [Online]. Available: http://aircasting.org
[61] Clarity. [Online]. Available: http://joinclarity.io
[62] Laser egg. [Online]. Available: http://laseregg.origins-china.com
[63] L.-J. Chen, W. Hsu, M. Cheng, and H.-C. Lee, “LASS: A Location-Aware Sensing
System for Participatory PM2.5 Monitoring,” in ACM MobiSys, 2016.
[64] uhoo. [Online]. Available: http://uhooair.com
[65] H. Ayadi, A. Zouinkhi, B. Boussaid, and M. N. Abdelkrim, “A machine learning methods: Outlier detection in WSN,” in IEEE International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, 2015.
[66] S. A. Haque, M. Rahman, and S. M. Aziz, “Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare,” Sensors, vol. 15, no. 4, pp. 8764–8786, April 2015.
[67] M. A. Hayes and M. A. Capretz, “Contextual anomaly detection framework for big sensor data,” Journal of Big Data, vol. 2, no. 2, p. 22, 2015.
[68] M. Moshtaghi, S. Rajasegarar, C. Leckie, and S. Karunasekera, “Anomaly detection by clustering ellipsoids in wireless sensor networks,” in IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2009.
[69] J. Murphree, “Machine learning anomaly detection in large systems,” in IEEE AU- TOTESTCON, 2016.
[70] I. C. Paschalidis and Y. Chen, “Statistical anomaly detection with sensor networks,” ACM Transactions on Sensor Networks, vol. 7, no. 2, p. 17, August 2010.
[71] W. Wu, X. Cheng, M. Ding, K. Xing, F. Liu, and P. Deng, “Localized Outlying and Boundary Data Detection in Sensor Networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 8, pp. 1145–1157, August 2007.
[72] Edimax Inc. AirBox: PM2.5 Sensing for Smart Cities. [Online]. Available: https://airbox.edimaxcloud.com
[73] Y. Zhang, N. Meratnia, and P. Havinga, “Outlier Detection Techniques for Wireless Sensor Networks: A Survey,” IEEE Communications Surveys & Tutorials, vol. 12, no. 2, pp. 159–170, April 2010.
[74] J. N. R. Jeffers, Practitioner’s Handbook on the Modelling of Dynamic Change in Ecosystems, ser. SCOPE Report. John Wiley & Sons Ltd, 1988.
[75] J. W. Tukey, Exploratory data analysis. Addison-Wesley Pub. Co., 1977.
[76] AQICN.org. The Plantower PMS5003 and PMS7003 Air Quality Sensor experiment.
[Online]. Available: http://aqicn.org/sensor/pms5003-7003/hk/
[77] The API for detecting potential regional emission sources detected (hourly). [Online]. Available: https://data.lass-net.org/data/device_pollution.json
[78] The API for the ranking results of the AirBox devices (daily). [Online]. Available: https://data.lass-net.org/data/device_ranking.json
[79] The API for potential indoor AirBox devices (daily). [Online]. Available: https://data.lass-net.org/data/device_indoor.json
[80] The API for malfunctioning AirBox devices (daily). [Online]. Available: https://data.lass-net.org/data/device_malfunction_daily.json
[81] AirBox Dataset. [Online]. Available: https://sites.google.com/site/cclljj/dataset-airbox
[82] S.Mahajan,L.-J.Chen,andT.-C.Tsai,“Anempiricalstudyofpm2.5forecastingusing neural network,” in Ubiquitous Intelligence Computing, Advanced Trusted Computed, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2017 Intl IEEE Conferences. IEEE, 2017, pp. 327–333.
[83] S. Mahajan, H.-M. Liu, T.-C. Tsai, and L.-J. Chen, “Improving the accuracy and efficiency of pm2. 5 forecast service using cluster-based hybrid neural network model,” IEEE Access, vol. 6, pp. 19 193–19 204, 2018.
[84] S. Mahajan, H.-M. Liu, L.-J. Chen, and T.-C. Tsai, “A machine learning based pm2.5 forecasting framework using internet of environmental things,” in IoT as a Service, Y.-B. Lin, D.-J. Deng, I. You, and C.-C. Lin, Eds. Cham: Springer International Publishing, 2018, pp. 170–176.
[85] J. Jin, J. Gubbi, S. Marusic, and M. Palaniswami, “An information framework for creating a smart city through internet of things,” IEEE Internet of Things Journal, vol. 1, no. 2, pp. 112–121, 2014.
[86] K. A. Delic, “On resilience of iot systems: The internet of things (ubiquity sympo- sium),” Ubiquity, vol. 2016, no. February, p. 1, 2016.
[87] K.-w. Chau, “Use of meta-heuristic techniques in rainfall-runoff modelling,” 2017.
[88] W.-c. Wang, D.-m. Xu, K.-w. Chau, and S. Chen, “Improved annual rainfall-runoff forecasting using pso–svm model based on eemd,” Journal of hydroinformatics, vol. 15, no. 4, pp. 1377–1390, 2013.
[89] R. Taormina, K.-W. Chau, and B. Sivakumar, “Neural network river forecasting through baseflow separation and binary-coded swarm optimization,” Journal of hy- drology, vol. 529, pp. 1788–1797, 2015.
[90] J. Lanza, L. Sánchez, L. Muñoz, J. A. Galache, P. Sotres, J. R. Santana, and V. Gutiér- rez, “Large-scale mobile sensing enabled internet-of-things testbed for smart city services,” International Journal of Distributed Sensor Networks, vol. 11, no. 8, p. 785061, 2015.
[91] S. Zhang and K.-W. Chau, “Dimension reduction using semi-supervised locally linear embedding for plant leaf classification,” in International Conference on Intelligent Computing. Springer, 2009, pp. 948–955.
[92] V. Gholami, K. Chau, F. Fadaee, J. Torkaman, and A. Ghaffari, “Modeling of ground- water level fluctuations using dendrochronology in alluvial aquifers,” Journal of hydrology, vol. 529, pp. 1060–1069, 2015.
[93] P. Sefeedpari, S. Rafiee, A. Akram, K.-w. Chau, and S. H. Pishgar-Komleh, “Proph- esying egg production based on energy consumption using multi-layered adaptive neural fuzzy inference system approach,” Computers and electronics in agriculture, vol. 131, pp. 10–19, 2016.
[94] Y. Zheng, X. Yi, M. Li, R. Li, Z. Shan, E. Chang, and T. Li, “Forecasting fine-grained air quality based on big data,” in 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015, pp. 2267–2276.
[95] A. Grover, A. Kapoor, and E. Horvitz, “A deep hybrid model for weather forecasting,” in 21st ACM SIGKDD International Conference on Knowledge Discovery and Data
Mining. ACM, 2015, pp. 379–386.
[96] D. Lary, T. Lary, and B. Sattler, “Using machine learning to estimate global pm2. 5 for environmental health studies,” Environmental health insights, vol. 9, no. Suppl 1, p. 41, 2015.
[97] Y. Zheng, L. Capra, O. Wolfson, and H. Yang, “Urban computing: concepts, method- ologies, and applications,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 5, no. 3, p. 38, 2014.
[98] X. Li, L. Peng, Y. Hu, J. Shao, and T. Chi, “Deep learning architecture for air quality predictions,” Environmental Science and Pollution Research, vol. 23, no. 22, pp. 22 408–22 417, 2016.
[99] Y. Zheng, F. Liu, and H.-P. Hsieh, “U-air: When urban air quality inference meets big data,” in 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2013, pp. 1436–1444.
[100] C. Voyant, M. Muselli, C. Paoli, and M.-L. Nivet, “Numerical weather prediction (nwp) and hybrid arma/ann model to predict global radiation,” Energy, vol. 39, no. 1, pp. 341–355, 2012.
[101] L. Chen and X. Lai, “Comparison between arima and ann models used in short-term wind speed forecasting,” in Power and Energy Engineering Conference (APPEEC), 2011 Asia-Pacific. IEEE, 2011, pp. 1–4.
[102] N. I. Sapankevych and R. Sankar, “Time series prediction using support vector ma- chines: a survey,” IEEE Computational Intelligence Magazine, vol. 4, no. 2, 2009.
[103] A. D. Syafei, A. Fujiwara, and J. Zhang, “Prediction model of air pollutant levels using linear model with component analysis,” International Journal of Environmental Science and Development, vol. 6, no. 7, p. 519, 2015.
[104] J. Pires, S. Sousa, M. Pereira, M. Alvim-Ferraz, and F. Martins, “Management of air quality monitoring using principal component and cluster analysis—part i: So 2 and pm 10,” Atmospheric Environment, vol. 42, no. 6, pp. 1249–1260, 2008.
[105] T. Chen, J. He, X. Lu, J. She, and Z. Guan, “Spatial and temporal variations of pm2. 5 and its relation to meteorological factors in the urban area of nanjing, china,” International journal of environmental research and public health, vol. 13, no. 9, p. 921, 2016.
[106] P. Huang, J. Zhang, Y. Tang, and L. Liu, “Spatial and temporal distribution of pm2. 5 pollution in xi’an city, china,” International journal of environmental research and public health, vol. 12, no. 6, pp. 6608–6625, 2015.
[107] M.-A. Kioumourtzoglou, E. Austin, P. Koutrakis, F. Dominici, J. Schwartz, and A. Zanobetti, “Pm2. 5 and survival among older adults: effect modification by particu- late composition,” Epidemiology (Cambridge, Mass.), vol. 26, no. 3, p. 321, 2015.
[108] C. Christodoulos, C. Michalakelis, and D. Varoutas, “Forecasting with limited data: Combining arima and diffusion models,” Technological Forecasting and Social Change, vol. 77, no. 4, pp. 558 – 565, 2010.
[109] E. Cadenas, W. Rivera, R. Campos-Amezcua, and C. Heard, “Wind speed prediction using a univariate arima model and a multivariate narx model,” Energies, vol. 9, no. 2, 2016.
[110] H. Rodriguez, V. Puig, J. J. Flores, and R. Lopez, “Combined holt-winters and ga trained ann approach for sensor validation and reconstruction: Application to water demand flowmeters,” in Control and Fault-Tolerant Systems (SysTol), 2016 3rd Conference on. IEEE, 2016, pp. 202–207.
[111] R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice. OTexts, 2014.
[112] G. Zhang, “Time series forecasting using a hybrid {ARIMA} and neural network model,” Neurocomputing, vol. 50, pp. 159 – 175, 2003.
[113] S. Mahajan, Y.-S. Tang, D.-Y. Wu, T.-C. Tsai, and L.-J. Chen, “Car: The cleanest air routing algorithm for path navigation with minimal pm2. 5 exposure on the move,” in 16th Annual International Conference on Mobile Systems, Applications, and Services. ACM, 2018, pp. 532–532.
[114] G. Kiesewetter, W. Schoepp, C. Heyes, and M. Amann, “Modelling pm2.5 impact indicators in europe,” Environ. Model. Softw., vol. 74, no. C, pp. 201–211, Dec. 2015. [Online]. Available: http://dx.doi.org/10.1016/j.envsoft.2015.02.022
[115] Y. Cheng, X. Li, Z. Li, S. Jiang, Y. Li, J. Jia, and X. Jiang, “Aircloud: A cloud-based air-quality monitoring system for everyone,” in 12th ACM Conference on Embedded Network Sensor Systems, ser. SenSys ’14. New York, NY, USA: ACM, 2014, pp. 251–265. [Online]. Available: http://doi.acm.org/10.1145/2668332.2668346
[116] R.-H. Li, L. Qin, J. X. Yu, and R. Mao, “Optimal multi-meeting-point route search.” IEEE Trans. Knowl. Data Eng., vol. 28, no. 3, pp. 770–784, 2016.
[117] F. Kellner, A. Otto, and C. Brabander, “Bringing infrastructure into pricing in road freight transportation – a measuring concept based on navigation service data,” Transportation Research Procedia, vol. 25, no. Supplement C, pp. 794 – 805, 2017, world Conference on Transport Research - WCTR 2016 Shanghai. 10-15 July 2016. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ S2352146517307652
[118] Z. Yi and P. H. Bauer, “Optimal stochastic eco-routing solutions for electric vehicles,” IEEE Transactions on Intelligent Transportation Systems, no. 99, pp. 1–11, 2018.
[119] T. Jurik, A. Cela, R. Hamouche, R. Natowicz, A. Reama, S.-I. Niculescu, and J. Julien, “Energy optimal real-time navigation system,” IEEE Intelligent Transportation Systems
Magazine, vol. 6, no. 3, pp. 66–79, 2014.
[120] T. Jurik, A. Cela, R. Hamouche, A. Reama, R. Natowicz, S.-I. Niculescu, C. Villedieu, and D. Pachetau, “Energy optimal real-time navigation system: application to a hybrid electrical vehicle,” in 16th International IEEE Conference on Intelligent Transporta- tion Systems-(ITSC). IEEE, 2013, pp. 1947–1952.
[121] K. Boriboonsomsin, M. J. Barth, W. Zhu, and A. Vu, “Eco-routing navigation system based on multisource historical and real-time traffic information,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 4, pp. 1694–1704, 2012.
[122] Y. Lv, Y. Duan, W. Kang, Z. Li, and F.-Y. Wang, “Traffic flow prediction with big data: a deep learning approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 865–873, 2015.
[123] T. Liebig and C. Bockermann, “Predictive trip planning-smart routing in smart cities,” in Workshop Proceedings of the EDBT/ICDT 2014 Joint Conference, 2014.
[124] S. Müller and A. Voisard, “Air quality adjusted routing for cyclists and pedestrians,” in 1st ACM SIGSPATIAL International Workshop on the Use of GIS in Emergency Management, ser. EM-GIS ’15. New York, NY, USA: ACM, 2015, pp. 19:1–19:6. [Online]. Available: http://doi.acm.org/10.1145/2835596.2835609
[125] D. Quercia, R. Schifanella, and L. M. Aiello, “The shortest path to happiness: Recommending beautiful, quiet, and happy routes in the city,” in 25th ACM Conference on Hypertext and Social Media, ser. HT ’14. New York, NY, USA: ACM, 2014, pp. 116–125. [Online]. Available: http://doi.acm.org/10.1145/2631775.2631799
[126] M. H. Sharker, H. A. Karimi, and J. C. Zgibor, “Health-optimal routing in pedestrian navigation services,” in 1st ACM SIGSPATIAL International Workshop on Use of GIS in Public Health, ser. HealthGIS ’12. New York, NY, USA: ACM, 2012, pp. 1–10. [Online]. Available: http://doi.acm.org/10.1145/2452516.2452518
[127] H. Yoon, Y. Zheng, X. Xie, and W. Woo, “Smart itinerary recommendation based on user-generated gps trajectories,” in 7th International Conference on Ubiquitous Intelligence and Computing, ser. UIC’10. Berlin, Heidelberg: Springer-Verlag, 2010, pp. 19–34. [Online]. Available: http://dl.acm.org/citation.cfm?id=1929661.1929669
[128] R. Baraglia, C. I. Muntean, F. M. Nardini, and F. Silvestri, “Learnext: Learning to predict tourists movements,” in 22nd ACM International Conference on Information & Knowledge Management, ser. CIKM ’13. New York, NY, USA: ACM, 2013, pp. 751–756. [Online]. Available: http://doi.acm.org/10.1145/2505515.2505656
[129] V. Nallur, A. Elgammal, and S. Clarke, Smart Route Planning Using Open Data and Participatory Sensing. Cham: Springer International Publishing, 2015, pp. 91–100. [Online]. Available: https://doi.org/10.1007/978-3-319-17837-0_9
[130] R. Liu, H. Liu, D. Kwak, Y. Xiang, C. Borcea, B. Nath, and L. Iftode, “Balanced traffic routing: Design, implementation, and evaluation,” Ad Hoc Networks, vol. 37, pp. 14–28, 2016.
[131] H. Kruize, O. Hänninen, O. Breugelmans, E. Lebret, and M. Jantunen, “Description and demonstration of the expolis simulation model: Two examples of modeling popu- lation exposure to particulate matter,” Journal of Exposure Science and Environmental Epidemiology, vol. 13, no. 2, p. 87, 2003.
[132] J. Li and A. D. Heap, “Spatial interpolation methods applied in the environmental sciences,” Environ. Model. Softw., vol. 53, no. C, pp. 173–189, Mar. 2014. [Online]. Available: http://dx.doi.org/10.1016/j.envsoft.2013.12.008
[133] T. J.-J. Li, S. Sen, and B. Hecht, “Leveraging advances in natural language processing to better understand tobler’s first law of geography,” in 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ser. SIGSPATIAL ’14. New York, NY, USA: ACM, 2014, pp. 513–516. [Online]. Available: http://doi.acm.org/10.1145/2666310.2666493
[134] L. De Mesnard, “Pollution models and inverse distance weighting: Some critical remarks,” Comput. Geosci., vol. 52, pp. 459–469, Mar. 2013. [Online]. Available: http://dx.doi.org/10.1016/j.cageo.2012.11.002
[135] M. L. Stein, Interpolation of spatial data: some theory for kriging. Springer Science & Business Media, 2012.
[136] G. Gong, S. Mattevada, and S. E. OBryant, “Comparison of the accuracy of kriging and idw interpolations in estimating groundwater arsenic concentrations in texas,” Environmental research, vol. 130, pp. 59–69, 2014.
[137] N. Ya’acob, A. Azize, N. M. Adnan, A. L. Yusof, and S. S. Sarnin, “Haze monitoring based on air pollution index (api) and geographic information system (gis),” in Systems, Process and Control (ICSPC), 2016 IEEE Conference on. IEEE, 2016, pp. 7–11.
[138]K. Clarke, H.-O. Kwon, and S.-D. Choi, “Fast and reliable source identification of criteria air pollutants in an industrial city,” Atmospheric environment, vol. 95, pp. 239–248, 2014.
[139] M. A. Azpurua and K. D. Ramos, “A comparison of spatial interpolation methods for estimation of average electromagnetic field magnitude,” Progress in electromagnetics research, vol. 14, pp. 135–145, 2010.
[140] M. Kilibarda, T. Hengl, G. Heuvelink, B. Gräler, E. Pebesma, M. Percˇec Tadic ́, and B. Bajat, “Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution,” Journal of Geophysical Research: Atmospheres, vol. 119, no. 5, pp. 2294–2313, 2014.
[141] L. Li, T. Losser, C. Yorke, and R. Piltner, “Fast inverse distance weighting-based spatiotemporal interpolation: a web-based application of interpolating daily fine particulate matter pm2. 5 in the contiguous us using parallel programming and kd tree,” International journal of environmental research and public health, vol. 11, no. 9, pp. 9101–9141, 2014.
[142] M. Percoco, “Temporal aggregation and spatio-temporal traffic modeling,” Journal of transport geography, vol. 46, pp. 244–247, 2015.
[143] E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische mathematik, vol. 1, no. 1, pp. 269–271, 1959.
[144] D. Hasenfratz, T. Arn, I. de Concini, O. Saukh, and L. Thiele, “Health-optimal routing in urban areas,” in 14th International Conference on Information Processing in Sensor Networks. ACM, 2015, pp. 398–399.
[145] D. H. Stolfi and E. Alba, “Eco-friendly reduction of travel times in european smart cities,” in Annual Conference on Genetic and Evolutionary Computation. ACM, 2014, pp. 1207–1214.
[146] M. Hatzopoulou, S. Weichenthal, G. Barreau, M. Goldberg, W. Farrell, D. Crouse, and N. Ross, “A web-based route planning tool to reduce cyclists’ exposures to traffic pollution: A case study in montreal, canada,” Environmental Research, vol. 123, pp. 58–61, 2013.
[147] R. Kar and R. Haldar, “Applying chatbots to the internet of things: Opportunities and architectural elements,” CoRR, vol. abs/1611.03799, 2016. [Online]. Available: http://arxiv.org/abs/1611.03799
[148] N. M. Radziwill and M. C. Benton, “Evaluating quality of chatbots and intelligent conversational agents,” CoRR, vol. abs/1704.04579, 2017. [Online]. Available: http://arxiv.org/abs/1704.04579
描述 博士
國立政治大學
社群網路與人智計算國際研究生博士學位學程(TIGP)
104761502
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104761502
資料類型 thesis
dc.contributor.advisor 陳伶志<br>蔡子傑zh_TW
dc.contributor.advisor Chen, Ling-Jyh<br>Tsai, Tzu-Chiehen_US
dc.contributor.author (作者) 沙齊zh_TW
dc.contributor.author (作者) Sachit Mahajanen_US
dc.creator (作者) 沙齊zh_TW
dc.creator (作者) Mahajan, Sachiten_US
dc.date (日期) 2019en_US
dc.date.accessioned 3-六月-2019 13:08:25 (UTC+8)-
dc.date.available 3-六月-2019 13:08:25 (UTC+8)-
dc.date.issued (上傳時間) 3-六月-2019 13:08:25 (UTC+8)-
dc.identifier (其他 識別碼) G0104761502en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/123695-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 社群網路與人智計算國際研究生博士學位學程(TIGP)zh_TW
dc.description (描述) 104761502zh_TW
dc.description.abstract (摘要) 隨著都市化和工業化，人們對於空氣品質的擔憂也日益漸增，因為空氣品質直接影響了人類的健康和發展。而空氣不見得會是如其所見的乾淨，甚至可能充滿著有害的顆粒物質：小於 2.5微米的細懸浮微粒（PM2.5），這些微小的懸浮微粒對人類有致命的危險，因其極小的尺度可以直接穿過人的肺部，而後直接進到血液中，造成危及生命的疾病。一個有效的資料收集與分析方法搭配視覺化的呈現，可以協助人們更有效率的監控環境以及優化生活中的決策。為此，本論文提出了一個多管齊下的方法，包括群眾外包（Crowd-Sourcing）和開源的框架的設計、實施及評估，並利用物聯網（Internet of Things）平台、機器學習技術開發出新的空氣品質傳感解決方案，以提供服務給民眾與提高對於空品相關議題的意識，同時有助於提升民眾生活品質。
但是要將這樣的系統以即時的方式運作，有許多挑戰需要克服。有效地處理如此大量的資料是一項艱鉅且繁瑣的工作，而要確保有檢測出每一個異常的點也是項極具挑戰性的任務，除此之外，即時預測系統必須做到具備準確和擴展性的同時也盡量降低運算時間，這更是項困難的挑戰。本論文遵循以人為本的理念，使用物聯網裝置搭配認知計算（cognitive computing）來生成大數據資料，而後將這些資料用於強化空氣品質管理和預測系統。典型的流程包含從感測器獲得資料、分析資料、進行預測、視覺化資料，以及當空氣品質異常時發出預警的服務。本論文的主要貢獻為以下四點：

1. 首先，本論文解決了資料收集和可靠性的問題。為了解決此問題，本論文提出了異常偵測框架（Anomaly Detection Framework, ADF），異常偵測框架可以有效地識別原始測量數據中的異常值，並推斷異常事件的發生。此外，此框架還可以評估系統中每個裝置的屬性和狀態，舉例來說，裝置可能是在部署在室內（屬性）或是接近高頻污染源（狀態）。另外，本論文也提出了一套能將能源消耗、資料收集和成本最佳化的移動式 PM2.5 感測模型，並利用自行車作為移動工具進行實測。這個成果幫助我們開發出可以在移動中收集資料，並節省時間及消耗能源的系統。

2. 接著，本文探討了設計即時可擴展性預測模型的問題。設計這種預測系統的主要挑戰之一是確保擁有高精準度和可接受的計算時間，為了解決這個問題，我們先對現有的預測模型進行比較分析，而後提出一基於神經網絡（neural networks）的混合模型（Hybrid model）來進行每小時的 PM2.5 預測，其性能評估是通過與線性模型和其他現行主流模型來進行結果的比較。該模型已實際部署並持續運行中，為台灣 2000 多個監測節點提供預測服務。

3. 而後，本論文解決了建立小尺度空氣污染圖的挑戰，並使用這些圖形結果來設計一種能夠幫助城市居民減少空氣污染物暴露的演算法 - 乾淨空氣導航（Clean Air Routing, CAR）演算法。該演算法會推薦從出發地到目的地的最佳空氣品質路徑，除了會基於台灣的公路網對於 PM2.5 的資料進行空間和時間插值，也會根據不同的旅行模式進行評估。最後將結果與Google 地圖所提供的導航路徑結果和最短路徑（Dijkstra）演算法所得的結果進行比較。

4. 最後，本論文介紹了基於本研究的結果而開發的即時應用，包括空氣品質視覺化服務、用於了解 PM2.5 變化趨勢的動畫服務、短期 PM2.5 預測服務、基於物聯網的個人空氣品質聊天機器人以及基於乾淨空氣導航演算法的路線推薦應用網頁。
此外，本論文藉由與其他先進主流系統進行比較分析，來評估應用系統的表現。本研究所提出的系統架構應用層面，並不僅限應用於空氣品質資料，亦可用於未來新開發之感測系統監測資料。		zh_TW
dc.description.abstract (摘要) With the continuous urbanization and industrial growth, the concern about deteriorating air quality is also increasing. This directly impacts the human health and sustainable development. Sometimes even the air that looks clean isn`t clean and is filled with dangerous particulate matter; no more than 2.5 microns (PM2.5). These particles are deadly and can cause life threatening diseases. An effective way to collect, analyze and scientifically visualize the air quality data can help us continuously monitor the environment and can facilitate people`s decision making. This work proposes a multi-pronged approach that encompasses around design, implementation and evaluation of a framework that exploits crowd-sourcing and crowd-sharing using IoT (Internet of Things) platform and machine learning techniques to develop novel solutions to do air quality sensing and provide services to the people that will not just raise awareness related to air quality problem but also assist them in day to day living.
But there are many challenges that need to be addressed before such a system can be deployed in real-time. Efficiently handling such a large volume of data is a tedious job and making sure that any anomaly is detected is also a challenging task. Other than that, having an accurate and scalable forecast system with low computation time is also a difficult task. In this dissertation, a human-centered approach is followed. The idea is to use IoT devices and cognitive computing to generate big data which can be further used to enhance air quality management systems and forecasting. A typical case will include the collection and storage of data obtained from the sensors, data analytics, prediction, visualization and an alert message service in case of unusual behavior in the air quality. The main contributions of this dissertation are:

1. Initially, this dissertation addresses the issue of data collection and reliability. To tackle the issue, an Anomaly Detection Framework (ADF) is proposed that is efficient enough in identifying outliers in the raw measurement data and inferring anomalous events emission. ADF can evaluate attributes and status of each device in the system; that is, whether a device is deployed indoors, or close to an emission source. Also, an energy, data and cost-efficient model for mobile opportunistic PM2.5 sensing via bicycles is proposed which is then implemented and tested for real world scenarios. The results helped us develop a system which would gather data on the move and at the save time would save device energy.
2. Next, this dissertation addresses the problem of designing a scalable forecast model that can be implemented in real-time. One of the major challenges in designing such a forecast system is ensuring high accuracy and an acceptable computation time. To address this issue, we begin with performing a comparative analysis of already existing forecast models. Later on, a Hybrid model based on neural networks is proposed to perform hourly PM2.5 forecast. The performance evaluation of the Hybrid model is done by comparing it with baseline models and other state of the art works. The model has been implemented in real-time and is used to provide forecast service for more than 2000 monitoring nodes in Taiwan.
3. Next, the dissertation deals with the challenge of creating fine-grained air pollution maps and then using those maps to design an algorithm which would assist urban dwellers to reduce their exposure to airborne pollutants. We propose the Clean Air Routing (CAR) algorithm that recommends health-optimal paths from origin to the destination. PM2.5 data are spatially and temporally interpolated on the Taiwan`s road network. The algorithm is evaluated for different travel modes as well as a comparison is provided with Google Maps result and shortest path (Dijkstra).
4. The final part of this dissertation explains about the real-time applications that have been developed based on the results obtained during this research. The applications include visualization service, an animation service to understand the trend in PM2.5, a short-term PM2.5 forecast service, an IoT enabled personal air quality chatbot assistant and a route recommendation application based on CAR algorithm.
Evaluation of the framework`s components has been conducted by performing a comparative analysis with state-of-the-art systems. The proposed framework is not just limited to air
quality data but it can potentially be applied to other emerging data sensing systems as well.		en_US
dc.description.tableofcontents Introduction 1-10
PM2.5 Sensing and Data Analysis 11-24
ADF: an Anomaly Detection Framework for Large-scale PM2.5 Sensing Systems 25-50
Design and Development of a Machine Learning based PM2.5 Forecast Framework 51-80
CAR: The Cleanest Air Routing Algorithm for Path Navigation with Minimal PM2.5 Exposure on the Move 81-102
Applications 103-114
Conclusions and Future Works 115-118
Reference 119		zh_TW
dc.format.extent 23060736 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104761502en_US
dc.subject (關鍵詞) 物聯網zh_TW
dc.subject (關鍵詞) 空氣污染zh_TW
dc.subject (關鍵詞) 人本計算zh_TW
dc.subject (關鍵詞) Internet of thingsen_US
dc.subject (關鍵詞) Air pollutionen_US
dc.subject (關鍵詞) Human centered computingen_US
dc.title (題名) 以使用者有感為中心的環境物聯網研究zh_TW
dc.title (題名) Internet of Environmental Things : A Human Centered Approachen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] S. Mahajan, L.-J. Chen, and T.-C. Tsai, “Short-term pm2. 5 forecasting using expo- nential smoothing method: A comparative analysis,” Sensors, vol. 18, no. 10, p. 3223, 2018.
[2] A. J. Cohen, M. Brauer, R. Burnett, H. R. Anderson, J. Frostad, K. Estep, K. Balakr- ishnan, B. Brunekreef, L. Dandona, R. Dandona et al., “Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the global burden of diseases study 2015,” The Lancet, vol. 389, no. 10082, pp. 1907–1918, 2017.
[3] D. Hasenfratz, “Enabling large-scale urban air quality monitoring with mobile sensor nodes,” Ph.D. dissertation, ETH Zurich, 2015.
[4] N. Sebe, “Human-centered computing: Challenges and perspectives,” in 23rd Interna- tional Conference on Data Engineering Workshop. IEEE, 2007, pp. 2–2.
[5] A. Anjomshoaa, S. Mora, P. Schmitt, and C. Ratti, “Challenges of drive-by iot sensing for smart cities: City scanner case study,” in 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. ACM, 2018, pp. 1112–1120.
[6] E. G. Snyder, T. H. Watkins, P. A. Solomon, E. D. Thoma, R. W. Williams, G. S. Hagler, D. Shelow, D. A. Hindin, V. J. Kilaru, and P. W. Preuss, “The changing paradigm of air pollution monitoring,” 2013.
[7] H. Pritchard and J. Gabrys, “From citizen sensing to collective monitoring: Work- ing through the perceptive and affective problematics of environmental pollution,” GeoHumanities, vol. 2, no. 2, pp. 354–371, 2016.
120 References
[8] N. Castell, M. Kobernus, H.-Y. Liu, P. Schneider, W. Lahoz, A. J. Berre, and J. Noll, “Mobile technologies and services for environmental monitoring: The citi-sense-mob
approach,” Urban climate, vol. 14, pp. 370–382, 2015.
[9] C. Leonardi, A. Cappellotto, M. Caraviello, B. Lepri, and F. Antonelli, “Secondnose: an air quality mobile crowdsensing system,” in 8th Nordic Conference on Human- Computer Interaction: Fun, Fast, Foundational. ACM, 2014, pp. 1051–1054.
[10] P. Zappi, E. Bales, J. H. Park, W. Griswold, and T. Š. Rosing, “The citisense air quality monitoring mobile sensor node,” in 11th ACM/IEEE Conference on Information Processing in Sensor Networks, Beijing, China, 2012.
[11] P.Dutta,P.M.Aoki,N.Kumar,A.Mainwaring,C.Myers,W.Willett,andA.Woodruff, “Common sense: participatory urban sensing using a network of handheld air quality monitors,” in 7th ACM conference on embedded networked sensor systems. ACM,
2009, pp. 349–350.
[12] B. J. Hubbell, A. Kaufman, L. Rivers, K. Schulte, G. Hagler, J. Clougherty, W. Cascio, and D. Costa, “Understanding social and behavioral drivers and impacts of air quality sensor use,” Science of The Total Environment, vol. 621, pp. 886–894, 2018.
[13] A. Commodore, S. Wilson, O. Muhammad, E. Svendsen, and J. Pearce, “Community- based participatory research for the study of air pollution: A review of motivations, approaches, and outcomes,” Environmental monitoring and assessment, vol. 189, no. 8, p. 378, 2017.
[14] S. Devarakonda, P. Sevusu, H. Liu, R. Liu, L. Iftode, and B. Nath, “Real-time air qual- ity monitoring through mobile sensing in metropolitan areas,” in 2nd ACM SIGKDD international workshop on urban computing. ACM, 2013, p. 15.
[15] L.-J. Chen, Y.-H. Ho, H.-C. Lee, H.-C. Wu, H.-M. Liu, H.-H. Hsieh, Y.-T. Huang, and S.-C. C. Lung, “An open framework for participatory pm2.5 monitoring in smart cities,” IEEE Access, vol. 5, pp. 14 441–14 454, July 2017.
[16] PM2.5 concentration indexes and activity advices. [Online]. Available: http: //taqm.epa.gov.tw/taqm/tw/fpmi.aspx
[17] PM2.5 Open Data Portal. [Online]. Available: https://pm25.lass-net.org
[18] S. Mahajan, H. M. Liu, T. Y. Huang, T. C. Tsai, and L. J. Chen, “Opportunistic pm2.5 sensing: A feasibility study,” in GLOBECOM 2017 - 2017 IEEE Global Communications Conference, Dec 2017, pp. 1–6.
[19] Q. Liang, X. Cheng, S. C. Huang, and D. Chen, “Opportunistic sensing in wireless sensor networks: Theory and application,” IEEE Transactions on Computers, vol. 63, no. 8, pp. 2002–2010, Aug. 2014.
[20] Z. Wang et al., “Opportunistic sensing for object recognition-a unified formulation for dynamic sensor selection and feature extraction,” in IEEE International Conference on Multimedia and Expo (ICME), 2013.
[21] M. A. A. H. Khan, H. M. S. Hossain, and N. Roy, “Sensepresence: Infrastructure-less occupancy detection for opportunistic sensing applications,” in IEEE International Conference on Mobile Data Management, 2015.
[22] Y.-T. Huang, Y.-C. Chen, J.-H. Huang, L.-J. Chen, and P. Huang, “YushanNet: A Delay-Tolerant Wireless Sensor Network for Hiker Tracking in Yushan National Park,” in The International Conference on Mobile Data Management Systems, Services and Middleware, 2009.
[23] R. O. Vasconcelos et al., “An adaptive middleware for opportunistic mobile sensing,” in International Conference on Distributed Computing in Sensor Systems, 2015.
[24] L. L. D. Zhao, H. Ma and J. Zhao, “On opportunistic coverage for urban sensing,” in IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, 2013.
[25] G. Varela et al., “An integrated system for urban pollution monitoring through a public transportation based opportunistic mobile sensor network,” in IEEE Interna- tional Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2009.
[26] M. Fiore, A. Nordio, and C. F. Chiasserini, “Driving factors toward accurate mobile opportunistic sensing in urban environments,” IEEE Trans. on Mobile Computing, vol. 15, no. 10, pp. 2480–2493, Oct. 2016.
[27] W. Kleiminger, C. Beckel, and S. Santini, “Household occupancy monitoring us- ing electricity meters,” in ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015.
122 References
[28] S.Kang,S.Kwon,C.Yoo,S.Seo,K.Park,J.Song,andY.Lee,“Sinabro:opportunistic and unobtrusive mobile electrocardiogram monitoring system,” in 15th ACM Workshop on Mobile Computing Systems and Applications, 2014.
[29] V. Arnaboldi, M. Conti, F. Delmastro, G. Minutiello, and L. Ricci, “Sensor mobile enablement (sme): A light-weight standard for opportunistic sensing services,” in IEEE International Conference on Pervasive Computing and Communications Workshops, 2013.
[30] G.S.Tuncay,G.Benincasa,andA.Helmy,“Participantrecruitmentanddatacollection framework for opportunistic sensing: A comparative analysis,” in 8th ACM MobiCom Workshop on Challenged Networks, 2013.
[31] L. A. Castro et al., “Collaborative opportunistic sensing with mobile phones,” in Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, 2014.
[32] T. Higuchi, H. Yamaguchi, T. Higashino, and M. Takai, “A neighbor collaboration mechanism for mobile crowd sensing in opportunistic networks,” in IEEE International Conference on Communications (ICC), 2014.
[33] C. Cornelius, A. Kapadia, D. Kotz, D. Peebles, M. Shin, and N. Triandopoulos, “Anonysense: privacy-aware people-centric sensing,” in 6th international conference
on Mobile systems, applications, and services. ACM, 2008, pp. 211–224.
[34] G. S. Tuncay, G. Benincasa, and A. Helmy, “Autonomous and distributed recruitment and data collection framework for opportunistic sensing,” in 18th Annual International Conference on Mobile Computing and Networking, 2012.
[35] D. Zhao, H. Ma, S. Tang, and X. Y. Li, “Coupon: A cooperative framework for building sensing maps in mobile opportunistic networks,” IEEE Trans. on Parallel and Distributed Systems, vol. 26, no. 2, pp. 392–402, Feb. 2015.
[36] R. Loomba, L. Shi, and B. Jennings, “State-machine driven opportunistic sensing by mobile devices,” in IEEE Global Communications Conference, 2014.
[37] S. Sigg and X. Fu, “Social opportunistic sensing and social centric networking: En- abling technology for smart cities,” in ACM International Workshop on Wireless and Mobile Technologies for Smart Cities, 2014.
[38] L.-J. Chen, Y.-H. Ho, H.-H. Hsieh, S.-T. Huang, H.-C. Lee, and S. Mahajan, “Adf: an anomaly detection framework for large-scale pm2. 5 sensing systems,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 559–570, 2018.
[39] Us epa smart city air challenge. [Online]. Available: https://www.challenge.gov/ challenge/smart-city-air-challenge/
[40] X. Tang, “An Overview of Air Pollution Problem in Megacities and City Clusters in China,” AGU Spring Meeting Abstracts, May 2007.
[41] VUFO - NGO Resource Centre Vietnam. (2013, Sept. 19) Vietnam Named among Top Ten Nations with Worst Air Pollution. [Online]. Available: http://www.ngocentre. org.vn/news/vietnam-named-among-top-ten-nations-worst-air-pollution
[42] B. Ostro, Outdoor air pollution: Assessing the environmental burden of disease at national and local levels, ser. WHO Environmental Burden of Disease Series. World Health Organization, 2004, no. 5.
[43] Y.-F. Xing, Y.-H. Xu, M.-H. Shi, and Y.-X. Lian, “The impact of PM2.5 on the human respiratory system,” Journal of Thoracic Disease, vol. 8, no. 1, pp. 69–74, January 2016.
[44] AirNow. [Online]. Available: https://airnow.gov
[45] Air Quality Data - Central Pollution Control Board. [Online]. Available: http://cpcb.nic.in/RealTimeAirQualityData.php
[46] Air pollution - Euripean Environment Agency. [Online]. Available: https: //www.eea.europa.eu/themes/air/intro
[47] M. Markiewicz, “A Review of Mathematical Models for the Atmospheric Dispersion of Heavy Gases. Part I. A Classification of Models,” Ecological Chemistry and Engineering S, vol. 19, no. 3, pp. 297–314, July 2012.
[48] S.-C. C. Lung, I.-F. Maod, and L.-J. S. Liu, “Residents’ particle exposures in six different communities in Taiwan,” Science of The Total Environment, vol. 377, no. 1, pp. 81–92, May 2007.
[49] S.-C. C. Lung, P.-K. Hsiao, T.-Y. Wen, C.-H. Liu, C. B. Fu, and Y.-T. Cheng, “Variabil- ity of intra-urban exposure to particulate matter and co from asian-type community pollution sources,” Atmospheric Environment, vol. 83, pp. 6–13, February 2014.
[50] M. Alvarado, F. Gonzalez, A. Fletcher, and A. Doshi, “Towards the Development of a Low Cost Airborne Sensing System to Monitor Dust Particles after Blasting at Open-Pit Mine Sites,” Sensors, vol. 15, pp. 19 667–19 687, 2015.
[51] M. Budde, R. E. Masri, T. Riedel, and M. Beigl, “Enabling low-cost particulate matter measurement for participatory sensing scenarios,” in International Conference on Mobile and Ubiquitous Multimedia, 2013.
[52] Y. Cheng, X. Li, Z. Li, S. Jiang, Y. Li, J. Jia, and X. Jiang, “AirCloud: A Cloud-based Air-Quality Monitoring System for Everyone,” in ACM SenSys, 2014.
[53] S. Devarakonda, P. Sevusu, H. Liu, R. Liu, L. Iftode, and B. Nath, “Real-time air quality monitoring through mobile sensing in metropolitan areas,” in ACM SIGKDD International Workshop on Urban Computing, 2013.
[54] Y. Gao, W. Dong, K. Guo, X. Liu, Y. Chen, X. Liu, J. Bu, and C. Chen, “Mosaic: A low-cost mobile sensing system for urban air quality monitoring,” in IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications. IEEE, 2016, pp. 1–9.
[55] K. Weekly, D. Rim, L. Zhang, A. M. Bayen, W. W. Nazaroff, and C. J. Spanos, “Low- cost coarse airborne particulate matter sensing for indoor occupancy detection,” in 2013 IEEE International Conference on Automation Science and Engineering (CASE). IEEE, 2013, pp. 32–37.
[56] Y. Zhuang, F. Lin, E.-H. Yoo, and W. Xu, “Airsense: A portable context-sensing device for personal air quality monitoring,” in Workshop on Pervasive Wireless Healthcare. ACM, 2015, pp. 17–22.
[57] Array of things. [Online]. Available: https://arrayofthings.github.io
[58] Taipei AirBox. [Online]. Available: http://pm2.5.taipei/
[59] OpenSense at ETH Zurich. [Online]. Available: http://www.opensense.ethz.ch/
[60] AirCasting. [Online]. Available: http://aircasting.org
[61] Clarity. [Online]. Available: http://joinclarity.io
[62] Laser egg. [Online]. Available: http://laseregg.origins-china.com
[63] L.-J. Chen, W. Hsu, M. Cheng, and H.-C. Lee, “LASS: A Location-Aware Sensing
System for Participatory PM2.5 Monitoring,” in ACM MobiSys, 2016.
[64] uhoo. [Online]. Available: http://uhooair.com
[65] H. Ayadi, A. Zouinkhi, B. Boussaid, and M. N. Abdelkrim, “A machine learning methods: Outlier detection in WSN,” in IEEE International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, 2015.
[66] S. A. Haque, M. Rahman, and S. M. Aziz, “Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare,” Sensors, vol. 15, no. 4, pp. 8764–8786, April 2015.
[67] M. A. Hayes and M. A. Capretz, “Contextual anomaly detection framework for big sensor data,” Journal of Big Data, vol. 2, no. 2, p. 22, 2015.
[68] M. Moshtaghi, S. Rajasegarar, C. Leckie, and S. Karunasekera, “Anomaly detection by clustering ellipsoids in wireless sensor networks,” in IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2009.
[69] J. Murphree, “Machine learning anomaly detection in large systems,” in IEEE AU- TOTESTCON, 2016.
[70] I. C. Paschalidis and Y. Chen, “Statistical anomaly detection with sensor networks,” ACM Transactions on Sensor Networks, vol. 7, no. 2, p. 17, August 2010.
[71] W. Wu, X. Cheng, M. Ding, K. Xing, F. Liu, and P. Deng, “Localized Outlying and Boundary Data Detection in Sensor Networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 8, pp. 1145–1157, August 2007.
[72] Edimax Inc. AirBox: PM2.5 Sensing for Smart Cities. [Online]. Available: https://airbox.edimaxcloud.com
[73] Y. Zhang, N. Meratnia, and P. Havinga, “Outlier Detection Techniques for Wireless Sensor Networks: A Survey,” IEEE Communications Surveys & Tutorials, vol. 12, no. 2, pp. 159–170, April 2010.
[74] J. N. R. Jeffers, Practitioner’s Handbook on the Modelling of Dynamic Change in Ecosystems, ser. SCOPE Report. John Wiley & Sons Ltd, 1988.
[75] J. W. Tukey, Exploratory data analysis. Addison-Wesley Pub. Co., 1977.
[76] AQICN.org. The Plantower PMS5003 and PMS7003 Air Quality Sensor experiment.
[Online]. Available: http://aqicn.org/sensor/pms5003-7003/hk/
[77] The API for detecting potential regional emission sources detected (hourly). [Online]. Available: https://data.lass-net.org/data/device_pollution.json
[78] The API for the ranking results of the AirBox devices (daily). [Online]. Available: https://data.lass-net.org/data/device_ranking.json
[79] The API for potential indoor AirBox devices (daily). [Online]. Available: https://data.lass-net.org/data/device_indoor.json
[80] The API for malfunctioning AirBox devices (daily). [Online]. Available: https://data.lass-net.org/data/device_malfunction_daily.json
[81] AirBox Dataset. [Online]. Available: https://sites.google.com/site/cclljj/dataset-airbox
[82] S.Mahajan,L.-J.Chen,andT.-C.Tsai,“Anempiricalstudyofpm2.5forecastingusing neural network,” in Ubiquitous Intelligence Computing, Advanced Trusted Computed, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2017 Intl IEEE Conferences. IEEE, 2017, pp. 327–333.
[83] S. Mahajan, H.-M. Liu, T.-C. Tsai, and L.-J. Chen, “Improving the accuracy and efficiency of pm2. 5 forecast service using cluster-based hybrid neural network model,” IEEE Access, vol. 6, pp. 19 193–19 204, 2018.
[84] S. Mahajan, H.-M. Liu, L.-J. Chen, and T.-C. Tsai, “A machine learning based pm2.5 forecasting framework using internet of environmental things,” in IoT as a Service, Y.-B. Lin, D.-J. Deng, I. You, and C.-C. Lin, Eds. Cham: Springer International Publishing, 2018, pp. 170–176.
[85] J. Jin, J. Gubbi, S. Marusic, and M. Palaniswami, “An information framework for creating a smart city through internet of things,” IEEE Internet of Things Journal, vol. 1, no. 2, pp. 112–121, 2014.
[86] K. A. Delic, “On resilience of iot systems: The internet of things (ubiquity sympo- sium),” Ubiquity, vol. 2016, no. February, p. 1, 2016.
[87] K.-w. Chau, “Use of meta-heuristic techniques in rainfall-runoff modelling,” 2017.
[88] W.-c. Wang, D.-m. Xu, K.-w. Chau, and S. Chen, “Improved annual rainfall-runoff forecasting using pso–svm model based on eemd,” Journal of hydroinformatics, vol. 15, no. 4, pp. 1377–1390, 2013.
[89] R. Taormina, K.-W. Chau, and B. Sivakumar, “Neural network river forecasting through baseflow separation and binary-coded swarm optimization,” Journal of hy- drology, vol. 529, pp. 1788–1797, 2015.
[90] J. Lanza, L. Sánchez, L. Muñoz, J. A. Galache, P. Sotres, J. R. Santana, and V. Gutiér- rez, “Large-scale mobile sensing enabled internet-of-things testbed for smart city services,” International Journal of Distributed Sensor Networks, vol. 11, no. 8, p. 785061, 2015.
[91] S. Zhang and K.-W. Chau, “Dimension reduction using semi-supervised locally linear embedding for plant leaf classification,” in International Conference on Intelligent Computing. Springer, 2009, pp. 948–955.
[92] V. Gholami, K. Chau, F. Fadaee, J. Torkaman, and A. Ghaffari, “Modeling of ground- water level fluctuations using dendrochronology in alluvial aquifers,” Journal of hydrology, vol. 529, pp. 1060–1069, 2015.
[93] P. Sefeedpari, S. Rafiee, A. Akram, K.-w. Chau, and S. H. Pishgar-Komleh, “Proph- esying egg production based on energy consumption using multi-layered adaptive neural fuzzy inference system approach,” Computers and electronics in agriculture, vol. 131, pp. 10–19, 2016.
[94] Y. Zheng, X. Yi, M. Li, R. Li, Z. Shan, E. Chang, and T. Li, “Forecasting fine-grained air quality based on big data,” in 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015, pp. 2267–2276.
[95] A. Grover, A. Kapoor, and E. Horvitz, “A deep hybrid model for weather forecasting,” in 21st ACM SIGKDD International Conference on Knowledge Discovery and Data
Mining. ACM, 2015, pp. 379–386.
[96] D. Lary, T. Lary, and B. Sattler, “Using machine learning to estimate global pm2. 5 for environmental health studies,” Environmental health insights, vol. 9, no. Suppl 1, p. 41, 2015.
[97] Y. Zheng, L. Capra, O. Wolfson, and H. Yang, “Urban computing: concepts, method- ologies, and applications,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 5, no. 3, p. 38, 2014.
[98] X. Li, L. Peng, Y. Hu, J. Shao, and T. Chi, “Deep learning architecture for air quality predictions,” Environmental Science and Pollution Research, vol. 23, no. 22, pp. 22 408–22 417, 2016.
[99] Y. Zheng, F. Liu, and H.-P. Hsieh, “U-air: When urban air quality inference meets big data,” in 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2013, pp. 1436–1444.
[100] C. Voyant, M. Muselli, C. Paoli, and M.-L. Nivet, “Numerical weather prediction (nwp) and hybrid arma/ann model to predict global radiation,” Energy, vol. 39, no. 1, pp. 341–355, 2012.
[101] L. Chen and X. Lai, “Comparison between arima and ann models used in short-term wind speed forecasting,” in Power and Energy Engineering Conference (APPEEC), 2011 Asia-Pacific. IEEE, 2011, pp. 1–4.
[102] N. I. Sapankevych and R. Sankar, “Time series prediction using support vector ma- chines: a survey,” IEEE Computational Intelligence Magazine, vol. 4, no. 2, 2009.
[103] A. D. Syafei, A. Fujiwara, and J. Zhang, “Prediction model of air pollutant levels using linear model with component analysis,” International Journal of Environmental Science and Development, vol. 6, no. 7, p. 519, 2015.
[104] J. Pires, S. Sousa, M. Pereira, M. Alvim-Ferraz, and F. Martins, “Management of air quality monitoring using principal component and cluster analysis—part i: So 2 and pm 10,” Atmospheric Environment, vol. 42, no. 6, pp. 1249–1260, 2008.
[105] T. Chen, J. He, X. Lu, J. She, and Z. Guan, “Spatial and temporal variations of pm2. 5 and its relation to meteorological factors in the urban area of nanjing, china,” International journal of environmental research and public health, vol. 13, no. 9, p. 921, 2016.
[106] P. Huang, J. Zhang, Y. Tang, and L. Liu, “Spatial and temporal distribution of pm2. 5 pollution in xi’an city, china,” International journal of environmental research and public health, vol. 12, no. 6, pp. 6608–6625, 2015.
[107] M.-A. Kioumourtzoglou, E. Austin, P. Koutrakis, F. Dominici, J. Schwartz, and A. Zanobetti, “Pm2. 5 and survival among older adults: effect modification by particu- late composition,” Epidemiology (Cambridge, Mass.), vol. 26, no. 3, p. 321, 2015.
[108] C. Christodoulos, C. Michalakelis, and D. Varoutas, “Forecasting with limited data: Combining arima and diffusion models,” Technological Forecasting and Social Change, vol. 77, no. 4, pp. 558 – 565, 2010.
[109] E. Cadenas, W. Rivera, R. Campos-Amezcua, and C. Heard, “Wind speed prediction using a univariate arima model and a multivariate narx model,” Energies, vol. 9, no. 2, 2016.
[110] H. Rodriguez, V. Puig, J. J. Flores, and R. Lopez, “Combined holt-winters and ga trained ann approach for sensor validation and reconstruction: Application to water demand flowmeters,” in Control and Fault-Tolerant Systems (SysTol), 2016 3rd Conference on. IEEE, 2016, pp. 202–207.
[111] R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice. OTexts, 2014.
[112] G. Zhang, “Time series forecasting using a hybrid {ARIMA} and neural network model,” Neurocomputing, vol. 50, pp. 159 – 175, 2003.
[113] S. Mahajan, Y.-S. Tang, D.-Y. Wu, T.-C. Tsai, and L.-J. Chen, “Car: The cleanest air routing algorithm for path navigation with minimal pm2. 5 exposure on the move,” in 16th Annual International Conference on Mobile Systems, Applications, and Services. ACM, 2018, pp. 532–532.
[114] G. Kiesewetter, W. Schoepp, C. Heyes, and M. Amann, “Modelling pm2.5 impact indicators in europe,” Environ. Model. Softw., vol. 74, no. C, pp. 201–211, Dec. 2015. [Online]. Available: http://dx.doi.org/10.1016/j.envsoft.2015.02.022
[115] Y. Cheng, X. Li, Z. Li, S. Jiang, Y. Li, J. Jia, and X. Jiang, “Aircloud: A cloud-based air-quality monitoring system for everyone,” in 12th ACM Conference on Embedded Network Sensor Systems, ser. SenSys ’14. New York, NY, USA: ACM, 2014, pp. 251–265. [Online]. Available: http://doi.acm.org/10.1145/2668332.2668346
[116] R.-H. Li, L. Qin, J. X. Yu, and R. Mao, “Optimal multi-meeting-point route search.” IEEE Trans. Knowl. Data Eng., vol. 28, no. 3, pp. 770–784, 2016.
[117] F. Kellner, A. Otto, and C. Brabander, “Bringing infrastructure into pricing in road freight transportation – a measuring concept based on navigation service data,” Transportation Research Procedia, vol. 25, no. Supplement C, pp. 794 – 805, 2017, world Conference on Transport Research - WCTR 2016 Shanghai. 10-15 July 2016. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ S2352146517307652
[118] Z. Yi and P. H. Bauer, “Optimal stochastic eco-routing solutions for electric vehicles,” IEEE Transactions on Intelligent Transportation Systems, no. 99, pp. 1–11, 2018.
[119] T. Jurik, A. Cela, R. Hamouche, R. Natowicz, A. Reama, S.-I. Niculescu, and J. Julien, “Energy optimal real-time navigation system,” IEEE Intelligent Transportation Systems
Magazine, vol. 6, no. 3, pp. 66–79, 2014.
[120] T. Jurik, A. Cela, R. Hamouche, A. Reama, R. Natowicz, S.-I. Niculescu, C. Villedieu, and D. Pachetau, “Energy optimal real-time navigation system: application to a hybrid electrical vehicle,” in 16th International IEEE Conference on Intelligent Transporta- tion Systems-(ITSC). IEEE, 2013, pp. 1947–1952.
[121] K. Boriboonsomsin, M. J. Barth, W. Zhu, and A. Vu, “Eco-routing navigation system based on multisource historical and real-time traffic information,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 4, pp. 1694–1704, 2012.
[122] Y. Lv, Y. Duan, W. Kang, Z. Li, and F.-Y. Wang, “Traffic flow prediction with big data: a deep learning approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 865–873, 2015.
[123] T. Liebig and C. Bockermann, “Predictive trip planning-smart routing in smart cities,” in Workshop Proceedings of the EDBT/ICDT 2014 Joint Conference, 2014.
[124] S. Müller and A. Voisard, “Air quality adjusted routing for cyclists and pedestrians,” in 1st ACM SIGSPATIAL International Workshop on the Use of GIS in Emergency Management, ser. EM-GIS ’15. New York, NY, USA: ACM, 2015, pp. 19:1–19:6. [Online]. Available: http://doi.acm.org/10.1145/2835596.2835609
[125] D. Quercia, R. Schifanella, and L. M. Aiello, “The shortest path to happiness: Recommending beautiful, quiet, and happy routes in the city,” in 25th ACM Conference on Hypertext and Social Media, ser. HT ’14. New York, NY, USA: ACM, 2014, pp. 116–125. [Online]. Available: http://doi.acm.org/10.1145/2631775.2631799
[126] M. H. Sharker, H. A. Karimi, and J. C. Zgibor, “Health-optimal routing in pedestrian navigation services,” in 1st ACM SIGSPATIAL International Workshop on Use of GIS in Public Health, ser. HealthGIS ’12. New York, NY, USA: ACM, 2012, pp. 1–10. [Online]. Available: http://doi.acm.org/10.1145/2452516.2452518
[127] H. Yoon, Y. Zheng, X. Xie, and W. Woo, “Smart itinerary recommendation based on user-generated gps trajectories,” in 7th International Conference on Ubiquitous Intelligence and Computing, ser. UIC’10. Berlin, Heidelberg: Springer-Verlag, 2010, pp. 19–34. [Online]. Available: http://dl.acm.org/citation.cfm?id=1929661.1929669
[128] R. Baraglia, C. I. Muntean, F. M. Nardini, and F. Silvestri, “Learnext: Learning to predict tourists movements,” in 22nd ACM International Conference on Information & Knowledge Management, ser. CIKM ’13. New York, NY, USA: ACM, 2013, pp. 751–756. [Online]. Available: http://doi.acm.org/10.1145/2505515.2505656
[129] V. Nallur, A. Elgammal, and S. Clarke, Smart Route Planning Using Open Data and Participatory Sensing. Cham: Springer International Publishing, 2015, pp. 91–100. [Online]. Available: https://doi.org/10.1007/978-3-319-17837-0_9
[130] R. Liu, H. Liu, D. Kwak, Y. Xiang, C. Borcea, B. Nath, and L. Iftode, “Balanced traffic routing: Design, implementation, and evaluation,” Ad Hoc Networks, vol. 37, pp. 14–28, 2016.
[131] H. Kruize, O. Hänninen, O. Breugelmans, E. Lebret, and M. Jantunen, “Description and demonstration of the expolis simulation model: Two examples of modeling popu- lation exposure to particulate matter,” Journal of Exposure Science and Environmental Epidemiology, vol. 13, no. 2, p. 87, 2003.
[132] J. Li and A. D. Heap, “Spatial interpolation methods applied in the environmental sciences,” Environ. Model. Softw., vol. 53, no. C, pp. 173–189, Mar. 2014. [Online]. Available: http://dx.doi.org/10.1016/j.envsoft.2013.12.008
[133] T. J.-J. Li, S. Sen, and B. Hecht, “Leveraging advances in natural language processing to better understand tobler’s first law of geography,” in 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ser. SIGSPATIAL ’14. New York, NY, USA: ACM, 2014, pp. 513–516. [Online]. Available: http://doi.acm.org/10.1145/2666310.2666493
[134] L. De Mesnard, “Pollution models and inverse distance weighting: Some critical remarks,” Comput. Geosci., vol. 52, pp. 459–469, Mar. 2013. [Online]. Available: http://dx.doi.org/10.1016/j.cageo.2012.11.002
[135] M. L. Stein, Interpolation of spatial data: some theory for kriging. Springer Science & Business Media, 2012.
[136] G. Gong, S. Mattevada, and S. E. OBryant, “Comparison of the accuracy of kriging and idw interpolations in estimating groundwater arsenic concentrations in texas,” Environmental research, vol. 130, pp. 59–69, 2014.
[137] N. Ya’acob, A. Azize, N. M. Adnan, A. L. Yusof, and S. S. Sarnin, “Haze monitoring based on air pollution index (api) and geographic information system (gis),” in Systems, Process and Control (ICSPC), 2016 IEEE Conference on. IEEE, 2016, pp. 7–11.
[138]K. Clarke, H.-O. Kwon, and S.-D. Choi, “Fast and reliable source identification of criteria air pollutants in an industrial city,” Atmospheric environment, vol. 95, pp. 239–248, 2014.
[139] M. A. Azpurua and K. D. Ramos, “A comparison of spatial interpolation methods for estimation of average electromagnetic field magnitude,” Progress in electromagnetics research, vol. 14, pp. 135–145, 2010.
[140] M. Kilibarda, T. Hengl, G. Heuvelink, B. Gräler, E. Pebesma, M. Percˇec Tadic ́, and B. Bajat, “Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution,” Journal of Geophysical Research: Atmospheres, vol. 119, no. 5, pp. 2294–2313, 2014.
[141] L. Li, T. Losser, C. Yorke, and R. Piltner, “Fast inverse distance weighting-based spatiotemporal interpolation: a web-based application of interpolating daily fine particulate matter pm2. 5 in the contiguous us using parallel programming and kd tree,” International journal of environmental research and public health, vol. 11, no. 9, pp. 9101–9141, 2014.
[142] M. Percoco, “Temporal aggregation and spatio-temporal traffic modeling,” Journal of transport geography, vol. 46, pp. 244–247, 2015.
[143] E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische mathematik, vol. 1, no. 1, pp. 269–271, 1959.
[144] D. Hasenfratz, T. Arn, I. de Concini, O. Saukh, and L. Thiele, “Health-optimal routing in urban areas,” in 14th International Conference on Information Processing in Sensor Networks. ACM, 2015, pp. 398–399.
[145] D. H. Stolfi and E. Alba, “Eco-friendly reduction of travel times in european smart cities,” in Annual Conference on Genetic and Evolutionary Computation. ACM, 2014, pp. 1207–1214.
[146] M. Hatzopoulou, S. Weichenthal, G. Barreau, M. Goldberg, W. Farrell, D. Crouse, and N. Ross, “A web-based route planning tool to reduce cyclists’ exposures to traffic pollution: A case study in montreal, canada,” Environmental Research, vol. 123, pp. 58–61, 2013.
[147] R. Kar and R. Haldar, “Applying chatbots to the internet of things: Opportunities and architectural elements,” CoRR, vol. abs/1611.03799, 2016. [Online]. Available: http://arxiv.org/abs/1611.03799
[148] N. M. Radziwill and M. C. Benton, “Evaluating quality of chatbots and intelligent conversational agents,” CoRR, vol. abs/1704.04579, 2017. [Online]. Available: http://arxiv.org/abs/1704.04579		zh_TW
dc.identifier.doi (DOI) 10.6814/DIS.NCCU.TIGP.001.2019.B02en_US