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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-Jun-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.
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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 (Authors) 沙齊zh_TW
dc.contributor.author (Authors) 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-Jun-2019 13:08:25 (UTC+8)-
dc.date.available 3-Jun-2019 13:08:25 (UTC+8)-
dc.date.issued (上傳時間) 3-Jun-2019 13:08:25 (UTC+8)-
dc.identifier (Other Identifiers) 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
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dc.identifier.doi (DOI) 10.6814/DIS.NCCU.TIGP.001.2019.B02en_US