Transcript LiveE! Project weather sensor network_Seiichi X. Kato.ppt
LiveE! Project weather sensor network
Seiichi X. Kato (Hyogo University of Health Sciences) 1
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Weather hazard information
Global Scale – Global Warming – Large scale hurricane Local Scale – Urban heat island – Urban squalls – Flood Flood Hurricane It is important to observe the weather information in detail in order to predict these phenomena Squalls 2
Scale of meteorological phenomena
• Meteorological phenomena occurs in a variety of time/ spatial scale
Synoptic-scale
1000km 100km 10km 1km 100m Low pressure Warm/cold front Typhoon Squalls Heat Island Tornado Our target
meso/micro -scale
minute hour time scale day week 3
Grass-roots Weather Observing System • • • Background – Low-cost weather sensors are marketed – Broadband network is in widespread use Some companies and individuals have weather station.
It will be possible to observe the weather information in detail if these weather station are connected each other by internet.
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Live E! Project
• • • Founded by WIDE project & some industries in Japan (2005) – WIDE project is a research consortium on the internet technology among industry and academia We’ll establish the platform to share all the digital weather information and devices by individuals and organizations in order to recognize the environment of the Earth.
If you have some weather sensors and are interested in this project, please contact me.
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Live E! sensor map(May. 2009) Kurashiki Tokyo ～ 230 sensor sites http://www.map-asp.net/Spatial_Gateway/pl/Gate_100.html
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What kind of sensors we use.
• • Weather sensors that can read ...
– Temperature – Humidity – Pressure – RainFall – WindDir
Vaisala WXT510 WM918
– WindSpeed Cost – US$200 ～ 3000
WMR968 One-Wire Weather Station VantagePRO2
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Live E! server architecture
Live E! Service SOAP Web Service (Axis) Database (PostgreSQL) Java VM
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Live E! global system
• • DNS like architecture Control information for this system (profile, schema, query etc.) are exchanged by link Delegation of sub-authorities .
Metadata of the sensor data are replicated in all server. jp.
th.
wide.jp.
hoge.jp.
hoge2.jp.
ku.th.
ait.th.
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Live E! service architecture
User Link Resolver & Retriever Manager Profile Schema Data Manager
Archive
Link to Other Sites Live E! service Sensor Data Upload Profile Management
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Current Web service API
• • Get Observation data – GetCurrentDataAll – GetCurrentData – GetDataByTimespan – GetCurrentDataOf – GetCurrentDataByType – GetCurrentDataByAreaRect Ger detail profile of each sensor – GetProfileAll – GetProfile – GetProfileByType – GetProfileByAreaRect 11
Live E! data (xml)
– Location information • Longitude,latitude etc.
Data – Observation value 12
<< Live E! Application >> Provisions for natural disaster • Kurashiki Kurashiki-city, Okayama, JAPAN – Rainfall has a locality; i.e., many sensors are needed to correctly monitor the area.
– About 30 sensors on schools – Weather sensor mesh 3km × 3km – The local government uses these sensor data for flood prediction.
75km http://live-e.naist.jp/map/ 13
Web service -> Overlay network
• • Current system – Server-client model – Single point of failure – Load of server will be enormous if the number of sensor become enormous.
Next system – P2P(We use PIAX developed by Osaka Univ.) – Load distribution system – Realtime alarm for disaster 14
AR Model for forecast
• AR model is one the model of time series analysis, and can forecast future value by validating it from the past data AIC minimization
AIC
AIC ( Akaike’s Information Criterion) is one of the index that selects the best order of the model, and the minimum AIC model is the best model.
2 ( log likelihood ) 2(number of parameters ) ● observed values ○ forecasted values
Example of AR Model Validation results y n
i m
1
a i y n
i
v n
y : time series, a : weight val ue : white noise, n : time, m : order of model PARCOR Method PARCOR means Partical Autocorrelation Cooefficient,and the following expression consists in PARCOR and model’s AR parameter.
a i m
a i m
1
a m m a m m
i
1
i
1 , ,
m
1
i
: time ,
m
: order of model a : AR parameter, From this expression, if PARCOR 1,..,m is a i i : PARCOR obtained, all AR parameters can be calculated. 15
Example of our application for weather
Prediction by AR(auto-regressive) model 25
forecast
T e m p e
20 15 10
r a t
Historical data 5
u r
0
e
Prediction by AR model is independent of the data of the other points.
Time
Obs. data Prediction 16
Test application: contour map
Temperature Barometric pressure
Contour map on Google Map
humidity
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Current problem
• • The number of sensor is small – The accuracy of our interpolation is incredible Not suitable for long-term forecast • We’d like to combine the satellite data with our local sensor data.
– Check the accuracy of interpolation and the value of each sensor(discovery of failure) – Get the information of valuable phenomena 18
Future work
• Collaborate with GeoGRID – Now we are implementing web service in order to convert Live E! xml data to SensorML by OGC (Open Geospatial Consortium).
– To use the satellite data in order to check the error data in Live E! data and predict more accurately.
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