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ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2018,3,26]]},"abstract":"<jats:p>Air pollution has raised people's public health concerns in major cities, especially for Particulate Matter under 2.5\u03bcm (PM2.5) due to its significant impact on human respiratory and circulation systems. In this paper, we present the design, implementation, and evaluation of a mobile application, Third-Eye, that can turn mobile phones into high-quality PM2.5 monitors, thereby enabling a crowdsensing way for fine-grained PM2.5 monitoring in the city. We explore two ways, crowdsensing and web crawling, to efficiently build large-scale datasets of the outdoor images taken by mobile phone, weather data, and air-pollution data. Then, we leverage two deep learning models, Convolutional Neural Network (CNN) for images and Long Short Term Memory (LSTM) network for weather and air-pollution data, to build an end-to-end framework for training PM2.5 inference models. Our App has been downloaded more than 2,000 times and runs more than 1 year. The real user data based evaluation shows that Third-Eye achieves 17.38 \u03bcg\/m3 average error and 81.55% classification accuracy, which outperforms 5 state-of-the-art methods, including three scattered interpolations and two image based estimation methods. The results also demonstrate how Third-Eye offers substantial enhancements over typical portable PM2.5 monitors by simultaneously improving accessibility, portability, and accuracy.<\/jats:p>","DOI":"10.1145\/3191752","type":"journal-article","created":{"date-parts":[[2018,3,27]],"date-time":"2018-03-27T12:06:45Z","timestamp":1522152405000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":60,"title":["Third-Eye"],"prefix":"10.1145","volume":"2","author":[{"given":"Liang","family":"Liu","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wu","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zheng","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huadong","family":"Ma","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,3,26]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2010.2045890"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1518701.1518762"},{"key":"e_1_2_1_3_1","volume-title":"Fintan Hurley, Hans-Guido M\u00fccke, Ferr\u00e1n Ballester, Inmaculada Aguilera, and others.","author":"Boldo Elena","year":"2006","unstructured":"Elena Boldo, Sylvia Medina, Alain Le Tertre, Fintan Hurley, Hans-Guido M\u00fccke, Ferr\u00e1n Ballester, Inmaculada Aguilera, and others. 2006. 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