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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/102591

    题名: SeparaTags : 結合Android及Hadoop之智慧城市感測資料處理平台
    其它题名: Separatags : a sensor data processing platform based on Android and Hadoop for building intelligent cities
    作者: 廖韋哲;Liao, Wei-Che
    贡献者: 淡江大學資訊工程學系資訊網路與通訊碩士班
    林其誼;Lin, Chi-Yi
    关键词: Hadoop;HBase;Hipi;Android;智慧城市;感測資訊;Intelligent Cities;EXIF
    日期: 2014
    上传时间: 2015-05-04 09:59:35 (UTC+8)
    摘要:   由於現今感測器的多元化發展,以及行動裝置、行動網路的普及,所以每天使用者所產生的資料量也隨之龐大,其中包括感測資訊Exif。在快速遞增的大量感測資訊當中,要如何將其利用並且做出有效率的分析以及運算是個嶄新的議題。在這篇論文中,我們把重點放在處理這些各種感測裝置環境底下所產生的大量數據,這些數據包括我們生活周遭所產生的資訊,例如溫度、道路狀況和空氣品質、噪音值等等,最後我們透過雲端運算技術進行數值的分析。
      因此,我們在基於Hadoop的智慧城市中開發了一個感測資訊處理平台,我們設想這些感測資訊被嵌入在車輛上的行車紀錄器或是使用者的行動裝置所擷取、拍攝的圖片當中,藉著Android作業系統的智慧手機,使用者可以將這些圖像檔案上傳到我們開發的Separatags Android應用程序Hadoop叢集當中;在圖像檔案上傳之後,我們使用MapReduce框架來處理它們。具體而言,在Map Task
    我們利用開發完善的Hadoop Image Processing Interface (HIPI) 套件擷取圖像檔案中我們所需的感測資訊,然後在Reduce Task這些感測資訊將會被儲存到HBase當中。此外,我們使用Hadoop分散式檔案系統(HDFS)儲存安裝在車輛上的行車紀錄器、行動裝置所拍攝的街道圖片,那麼使用者就可以使用他們的Android智慧手機或是網路瀏覽器來訪問這些感測資訊和街道圖片。
      With the rapid development of sensor technologies, along with the increasing popularity of mobile devices and wireless/mobile networks, the volume of data generated by human beings and all sorts of devices are getting larger and larger every day. It is without doubt that how to deal with the huge amount of data in an efficient way and to transform these data into useful information for people to make use of has become an important research topic. In this thesis, we focus on handling the large amount of environmental conditions data collected by various sensor devices. These data produced around us such as the temperature, the road conditions and the air quality can be numerically analyzed by utilizing the cloud computing technology. Therefore, we implemented a sensor data processing platform for intelligent cities based on Hadoop. We assume that sensor data are embedded in the image files captured by the vehicle drive recorders and the smartphones. With Android smartphones, users can upload the image files to the Hadoop cluster by the Separatags Android App we developed. After the image files are uploaded, we use the MapReduce framework to process them. Specifically, in the Map task we utilize the well-developed Hadoop Image Processing Interface (HIPI) library to extract the desired sensor data from the image files, and then in the Reduce task these sensor data are inserted into HBase. Besides, we use the Hadoop Distributed File System (HDFS) to store the street images captured by driving recorders installed in vehicles. People can then use their Android smartphones or standard web browsers to access the sensor data and the street images. In sum, the data processing platform we developed can be an important building block for constructing various useful and creative applications to serve people living in intelligent cities.
    显示于类别:[資訊工程學系暨研究所] 學位論文


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