In this thesis, a data collection application based on MapReduce
programming is described. This application aims to collect tempera-
ture data stream continuously from a specied set of sensors. Instead
of collecting the temperature information of all the sensors by one
machine, the sensors are divided into several subsets each of which
is handled as a Map task. In each Map task, the temperature data
stream of the assigned sensors is collected continuously and stored in
a predened database. All the Map tasks can run simultaneously on
several machines. This method can reduce the delay time and improve
the eciency of the data collection service, especially in the case of
having a huge number of sensors monitored remotely by a data center
through Internet. We can use the value of remote sensors to predict
the next value of remote sensors by some methods such as linear regres-
sion and K-means. And, we can use it to predict the system alarm.
Experimental results show that the proposed method is eective in
temperature data collection,and eective in carbon reduction.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-1025112-224316 |
| Date | 25 October 2012 |
| Creators | Cheng, Wen-Hao |
| Contributors | Shie-Jue Lee, Chen-Sen Ouyang, Hsien-Liang Tsai, Chun-Liang Hou |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025112-224316 |
| Rights | unrestricted, Copyright information available at source archive |
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