DEIM Forum 2010 D5-3 432-8011 3-5-1 E-mail: {cs06062,cs06015}@s.inf.shizuoka.ac.jp, {yokoyama,fukuta,ishikawa}@.inf.shizuoka.ac.jp Development of a Large-scale Visualization System Based on Sensor Network Abstract Takashi NAKANE, Masaaki EDA, Shohei YOKOYAMA, Naoki FUKUTA, and Hiroshi ISHIKAWA Department of Computer Science, Faculty of Informatics, Shizuoka University Johoku 4-5-6, Nakaku, Hamamatsu-shi, Shizuoka, 432-8011 Japan E-mail: {cs06062,cs06015}@s.inf.shizuoka.ac.jp, {yokoyama,fukuta,ishikawa}@.inf.shizuoka.ac.jp Recently, it is said to connect with the power saving by making power consumption at home etc. visible. In the resaerch, the visualization system to tie to the power saving or more is developed by making not only power consumption but also the man action visible, and making uselessness easy to discover by using the sensor network. Moreover, the visualization system,for example when sensor network in building visualized,not only browsing sensing data with other floor but also browsing all floor at a time,and to aim at a large-scale visualization system by which the person can look down upon the entire Sensing data Key words Visualization Sensing Data 1. Google Maps [1] Ajax Asynchronous JavaScript + XML [2] Web Ajax Web Ajax JSON JavaScript Object Notation [3] JSON Web JSON JavaScript Ajax Web Web 2 DBMS PHP CGI Web Web 2
2. [4] [5] [4] [5] ID Web JSON JSON JSON DOM DOM Web JSON Freddy[6] JSON DOM Freddy SAX 3. Web 2 Web DBMS PHP Web Ajax Web 3. 1 1 1 2 ID IEEE802.15.4 ZigBee 1 4 3. 2 1 DBMS YUI2 Yahoo! User Interface Ver.2 [9] Ext JS 3.0 [10] 3. 3 DB ID 100
sensorid character varying 5 primary key sensorarea smalllint foreign key source character varying 10 1 sensortable sensorarea smalllint primary key room boolean areachar character varying 10 2 areatable id integer primary key sensingtime timestamp sensorid character varying 5 foreign key light smalllint motion smallint temperature double precision moisture double precision battery real acvrms double precision effectivepower double precision powerfactor double precision 3 sensingdatatable XML 2 XML i S0 S1 2 I M L T B t ID 100, 60 3. 3. 1 DB DB DB 1 3 1 sensortable 2 areatable 1 0J1401 3 DBMS PostgreSQL8.4.1 3. 4 Web URL Web YUI2 OS 3. 5 Web Web Web Web JavaScript XMLHttpRequest 1 Web
4 3 2 XML- HttpRequest Web Web Web Web 4. 4. 1 3 Top Left Center Right Bottom 5 Center 1 4 Right Left Bottom Top 4. 2 4 1 0. 4 5 ID 4. 2. 1 1 2 5 5
6 30 1 5 JavaScript Vector Graphics Library [8] 2 4. 2. 2 Right 6 ID VW % 3 Ajax 4. 2. 3 2 x 7 ID 7 left x 7 8 8 Left 8 Center 9 2, 1 24 1 1
8 10 JSON 10 5. 9 2 YUI2 4. 2. 4 10 ID Ajax PHP Pacific Northwest National Laboratory [7] 10% DB CO2
6. Web DB 2 CO2 II B 19300026 B 20300027 [1] Google Maps, http://maps.google.com/ [2] J.J.Garrett, Ajax A New Approach to Web Applications, http://www.adaptivepath.com/ideas/essays/ archives/000385.php [3] JSON, http://json.org/ [4] Toshihiro Takada, Satoshi Kurihara, Toshio Hirotsu, and Toshiharu Sugawara, Proximity mining: Finding proximity using sensor data history,in IEEE Workshop on WMCSA, 2003. [5],,,,,, JSAI2006,2006 [6], Web SAX Freddy, D Vol.J91-D No.3 pp.585-594,2008. [7] JavaScript Vector Graphics Library http://www.walterzorn.com/jsgraphics/ jsgraphics e.html [8] Yahoo! User Interface Ver.3, http://developer.yahoo.com/yui/ [9] EXT JS 3.0, http://developer.yahoo.com/yui/ [10] Pacific Northwest National Laboratory, Department of Energy putting power in the hands of consumers through technology, http://www.pnl.gov/topstory.asp?id=285