Present status and perspective of global sensor network

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Present status and perspective of global sensor
network

• Masayuki Hirafuji
• Takuji Kiura
• National Agricultural Research Center
• Tsukuba, Japan

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Sensor Network Nodes
MICA2DOTwww.xbow.com
UC Berkley, Smart Dust http//robotics.eecs.berkel
ey.edu/pister/SmartDust/
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Sensor Nodes
Intel
Hitachi
NEC
Mitsubishi
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BAND-AID Projectby An Amateur
http//www.miniprobe.com/Bandaid/bandaid.htm/
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Sensor Web (NASA)
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Sensor Networks for that Planet
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Field Monitoring Server

• Case Acryl resin
• Core Fieldserver-Engine or PICNIC
• Sensors Temperature, Humidity, PPFD Soil
  moisture, Leaf-wetness UV, IR CO2
  Camera, Microscope
• Data-collection and AI Fieldserver-Agent
• Networking Fieldserver-Gateway
• GRID MetBroker

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Multi-functional Airflow in Field Server

• Accurate Measurement Assmann's aspiration
  psychrometer
• Air-temperature
• Humidity
• Cooling
• Sampling
• Gas (CO2, NOx, SOx)
• Insects
• Microbes
• Virus
• Dusts

Filter or Sampler
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Accuracy
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WDS Wi-Fi Hotspot

Cable
Solar-energy Driven Field Servers(Client
connection, Intermittent work)
Hotspot
Repeating by WDS (Wireless Distributed System)
Conventional Field Servers (Access-point,
Continuously work)
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Full-Wireless Field ServersIntermittent Drive
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A Sample of Observed Data

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Mesh Network Field Servers at a Park
Field Sever
Field Sever
Field Sever
Field Sever
Wireless Router
Robot with Remote Control
PDA
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Fieldserver-Engines
DDS (Direct Digital Synthesizer)16ch A/D
Converter
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Control of DDS and Relays
Input Range
16ch A/D converter
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Function of Fieldserver-Engine

• Web server
• A/D converter (10bit 8ch, 24bit 8ch)
• DDS (1Hz-70MHz)
• Power photo MOS relay(2ch)
• Serial IO (RS-232C)
• DC-DC converter(in 4-34V, out 5V)

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FPAA Fieldserver-EngineFPAA Field Programmable
Analogue Array
FPAA (Field Programmable Analogue Array)Anadigm
AN220E04
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Universal Measurement Circuits
FPAA Field Programmable Analog Array 20
programmable modules and Cross-bar Switch
Soil-moisture sensor EC sensor Plant moisture
sensor Humidity sensor pH,Pressure,Acceleration
Infrared-Visible LED/LD Visible-Ultraviolet
sensor Microphone/Vibration sensor Speaker/Ultra
sonic emitter RF antenna
Analogue Multipliers
DDS Direct Digital Synthesizer
ADC CS5528 24bit 8ch
Ethernet Controller
CPU H8/3069
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CAD(Anadigm designer)
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Double Super Heterodyne Receiver
RF input
FPAA output
Voice of Kyoto Broadcasting, JOBR, 1.143MHz
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Data-collection and Networking
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Fieldserver-Agentfor Data-Collection and Control
XML rule-base
Rule-base editor on Web
Open DB Web
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Fieldserver-Gateway (FSG)Open VPN on Linux
Firewall
Firewall
Private network of Field servers
Global IP
Fieldserver-Agent (Web crawler)
FSG
Cellar
Private IP
FSG
Cable
Global IP
Web Server
VPN router
ADSL
PC-Cluster
The Internet
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Fieldserver Data-viewer

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Image Viewer
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Installation at Fields
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Instauration of Field Monitoring Server
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Camera and Sensors
Camera,Temperature, HumidityCO2 Concentration,
Solar RadiationUltra Violet, Leaf Wetness
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Data-mining for UV and CO2
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Options for Field Servers
Insect Counter Pheromone Trap
Stacked Solar Cells
Large Solar Cells
Thermo Vision Camera
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Metallic Field Server using a garden light
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Ceramic Field Server at a Garden
Garden in Tsukuba
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Ceramic Field Server
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Ubiquitous networking around Field Servers
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Hybrid Solar and WindToo much energy only for
field monitoring!
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Field Storage Server

• HDD 250GB - 1TB
• File Server
• Field Server Agent
• Web Server
• Field Server Gateway
• Data-Viewer
• Image-Viewer

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Underground Field Server
Summer 5080 ?
Winter -30-60 ?
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Sensor Grid Data Grid Sensor Net Active
Database
Storage/Application Web Server (Native XML-DB)
Private network of Field servers
Agent Server Rule-base Editor Meta-rule Agent
Fieldserver-Agents Web crawler Remote Controller
The Internet
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Camera As a Versatile Sensor
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Growth Rate Measurement by Motion
Circumnutation
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Growth Rate Measurement by Optical Flow
Optical flow
Circumnutation
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Tomato at a Greenhouse(Kagome)
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Microscopic Live in a Paddy Field
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Monitoring Insect Pest
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High-Resolution Camera
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Image Database
2004.12.5 1544
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Mobile Field Server
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GPS
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Automatic Emergency Call in Farm Work Accident
Emergency !
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Color Collection by Color Chart
Calibration Image
Digital Camera
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Applications

• Ubiquitous Computing
• Crime Prevention
• Traceability System
• Agricultural Production Control
• Earth Observation
• Education

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Traceability System
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Traceability System by Field Severs
Internet
Image
ID
Historical data of Food Production
Wi-Fi RFID Reader/Writer
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Multi-Directional Images
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Organic Vegetable Farm
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Orange Farm (Miikkabi)
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Farming Information Disclosure
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APAN has beenan incubator of Field Servers.
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Evolving Field Server
2004 Field Server II
2001 First Field Server
2003 Field Server WSThai, NECTEC
2004 Thai Field Server
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Field Server Model

• 8 channels
• Store to Compact Flash (up to 1GB)
• Time interval from 10 sec. to 24 hr.
• Easy , Cheap
• Battery backup

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Wireless Sensor Network Workshopat Taichung
Univ., Taiwan 2004 June 24
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Data Fusion Takuji KIURA
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MetBroker

• A middle ware that provides agricultural models
  with consistent access to many different weather
  databases
• 23000 Observation Stations.
• SOAP Interface
• Spatial Access Mode
• Interpolation
• etc.
• http//www.agmodel.net/

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Field Servers linked to MetBroker
Client APP
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Weather DB
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DB
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DB
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DB
W
DB
Client APP
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DB
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DB
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DB
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DB
Weather DB
MetBroker
MetBroker
MetBroker
MetBroker
MetBroker
Client APP
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Weather DB
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DB
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DB
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DB
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DB
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DB
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DB
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DB
Weather Data XML
FieldServerDB
FieldServerDB
FieldServerDB
Field Server DB
Field Server Data Archive
Station Conf. XML
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MetBroker
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Data-fusion with Conventional Weather Databases
and Field Servers
Field Servers
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Applications forField Servers and MetBroker
Rice growth model
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MetBlastamRice Blast Prediction Model Using
MetBroker


Infective condition
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MetBroker

• Concrete Data Modeling
• Difficult to add new item
• Hard Corded Metadata
• Difficult to add a number of new data sources
• Long data transfer path

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Field Server Data

• Different Type of Sensors, Time Resolution
• Described in XML files not controlled
• Semantic Problems
• Sensors are added or removed dynamically
• Time resolution may be changed.
• Small data size (1KB) for each, Large data size
  as total

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System Overview (New MetBroker)
Metadata database
Decision-Making Support Services Operational
Products Simulation Models Detailed
Digital Forecast
Item Definition OWL
Station metadata RDF
2. Request
3. Request metadata
1. Register
Meteorological databases
DB Wrapper
Inference Engine
Broker
DB Wrapper
DB Wrapper
4. Request data
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Roles Of the RDF/OWL files
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Sample Basic Vocabulary
ltowlClass rdfID"DailyMaxAirTemperature"gt
ltrdfssubClassOf rdfresource"MaxAirTemperature"
/gt ltrdfssubClassOfgt ltowlRestrictiongt
ltowlallValuesFromgt ltowlClass
rdfabout"DailyMaximum"/gt
lt/owlallValuesFromgt ltowlonPropertygt
ltowlObjectProperty rdfabout"summaryKind"/gt
lt/owlonPropertygt lt/owlRestrictiongt
lt/rdfssubClassOfgt lt/owlClassgt ltowlClass
rdfabout"DailyMaximum"gt ltrdfssubClassOf
rdfresource"Maximum"/gt ltrdfssubClassOfgt
ltowlRestrictiongt ltowlallValuesFrom
rdfresource"Daily"/gt ltowlonPropertygt
ltowlObjectProperty rdfabout"duration"/gt
lt/owlonPropertygt lt/owlRestrictiongt
lt/rdfssubClassOfgt lt/owlClassgt
DailyMaxAirTemperature is a subclass of
MaxAirTemperature
DailyMaxAirTemperature is recognized as
maximum and daily data
Sample file http//www.agmodel.org/MetBroker.owl
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Available Basic Items (part)
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Sample Item Definition
ame_day.temp_max is recognized as maximum and
daily data based on every 10 minutes data
Local item name
ltmetDailyMaxAirTemperature rdfID"ame_day.temp_m
ax"gt ltmetsummaryKind rdfresource "http//www.ag
model.org/MetBroker.owlDailyMaximumOfSampleEvery1
0Minutes"/gt lt/metDailyMaxAirTemperaturegt ltmetHo
urlySampleAirTemperature rdfID"ame_time.temperat
ure"gt ltmetsummaryKind rdfresource "http//www.a
gmodel.org/MetBroker.owlSampleOnTheHour"/gt lt/met
HourlySampleAirTemperaturegt
A sample file is available on http//www.agmodel.o
rg/Aclima.owl
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Sample Station Metadata
ltmetMetStation rdfID"01"gt ltrdfslabel
xmllangen"gt Ag. Res. Inst.
Representative Observation Station
lt/rdfslabelgt ltmetaltgt64.0lt/metaltgt
ltmetloggt139.2874298095703lt/metloggt
ltmetlatgt35.34185791015625lt/metlatgt
ltmetbelongTogthttp//www.agmodel.org/Kanagawa.rdflt
/metbelongTogt ltmetmetCataloggt
ltmetMetCataloggt ltmetmetElementgtkngwDa
ilyAverageWindVelocitylt/metmetElementgt
ltmetcatalogStartgt1995-12-31T1500000000lt/metca
talogStartgt ltmetmeasurementHeightgt2.0lt/me
tmeasurementHeightgt ltmetMetCataloggt
lt/metmetCataloggt . lt/metMetStationgt
Description of weather station
The details of measured items
This item is defined in Item definition file
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Single station access
Basic Vocabulary ltowlClass rdfID"DailyMeanAirT
emperature/gt
Database Item definition ltmet
DailyMeanAirTemperature rdfIDAir /gt
3. Find the local item that is equivalent to
DailyMeanAirTemperature
2. Find the basic vocabulary that is equivalent
to the request
Inference Engine
4. Pass the local item Air
DB
Broker
1. Reuqest Resolution ? Daily Summary ? Mean Item
? Air Temperature
5. Query the database using the local item Air
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Spatial access
Basic Vocabulary ltowlClass rdfID"DailyMeanAirT
emperature/gt
Item definition
Station metadata Stations name Stations
location Measurement items etc.
Item definition
Station metadata Stations name Stations
location Measurement items etc.
Item definition
Station metadata Stations name Stations
location Measurement items etc.
Item definition
Station metadata Stations name Stations
location Measurement items etc.
3. Find the local items that are equivalent to
DailyMeanAirTemperature for each database that
includes the stations in the requested area
2. Find the stations that are located in the
requested geographical area
1. Reuqest NW ? latitude 35 ?longitude
134 SE ? latitude 25 ?longitude 154 Item ?
Daily Mean Air Temperature
DB
Inference Engine
4. Pass the local items
DB
DB
Broker
DB
5. Query the databases using the local items
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Access with summarization
Database Item definition ltmetHourlySampleAirTem
perature rdfID"ame_time.temperature"gt lt/metHour
lySampleAirTemperaturegt
Basic Vocabulary ltowlClass rdfID"DailyMeanAirT
emperature/gt ltowlClass rdfIDHourlySampleAirT
emperature/gt
2. Find the basic item that has the same or
shorter time resolution as the request
3. Find the local item that is compatible with
the basic item
Inference Engine
4. Pass the local item
DB
Broker
1. Reuqest Resolution ? Daily Summary ? Mean Item
? Air Temperature Summarization ? allowed
5. Query the database using the local item
6. Summarize the obtained data to requested time
resolution
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Solutions by Ontology

• RDF/OWL technology can solve the difficulties
  those MetBroker has
• Ontology can realize seamlessly and virtually
  integrated database
• New MetBroker is a challenge to provide clients
  with access to this virtually integrated database
• Applicable to other point observation data

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Future Plan

• Add information comparable or incomparable from
  some point of view
• Data storage and management system for Field
  Server
• Push data and throw events
• Seamless access to weather forecast information
• Other data broker

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Earth Observation Data Fusion Project

• Data Service
• Huge Database, Active Database
• Ontology Registry
• Seamless access to an application related data
• Data Collection
• Create Huge Data Sets.
• Data Usage

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Toward Global Sensor Network Masayuki HIRAFUJI
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Global Sensor Network
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Integrated experiment Coffee Farm (UCC Hawaii)
December 1, 2002 December 6, 2002) Jan. 25 to
Jan 31 2004
Hawaii island
UCC coffee farm
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Nitrogen Measurement by Portable FT-IR(UCC
Hawaii Coffee Farm)
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2002 Dec. UCC Coffee Kona Site in Big Island, USA
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Long-term test at UCC Coffee Farm, Hawaii USA
2002 December
2004 January
while APAN meeting in Honolulu
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Geckos were living in Field Servers.
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In DesertBy a Farmer
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Sandstorm
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A Desert in China, 2004.2.15 ????(??)
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In North China
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JICA Program
2004, in China
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Habei Ag. Univ. ??????2005 March 22
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Field Server with Soil Moisture Sensor 2005
March 24
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Trial Sites of Field Servers
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Siria, ICARDA
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Wireless Network WS 2004 in USA
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Florida Univ.
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Marine Field ServerOkinawa, Ishigaki-Island
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Farmers start using.
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Next

• Network robot (fixed R2D2)
• Distributed storage
• 1TB/year per 30 Field Servers
• Low cost
• One-chip Field Server Engine
• More functions like cellar phones
• Standardization
• Easy to Use
• Semantic Web server
• Earth Observation
• More sensors
• Cheap CO2 sensor