sensor web enablement (swe): sensor … · resource description framework. ... •spatio-temporal...
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MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 1/221ECE750:2008-2009 © PAMI Research Group – University of Waterloo
Sensor Web Enablement (SWE):
Sensor Interoperability and
Accessibility
Dr. Alaa KhamisPattern Analysis and Machine Intelligence
University of Waterloo
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OutlineOutline
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo
• Introduction
• SWE Overview
• Sensor Web Concept
• SWE Framework
• Information Models and Schema
• Web Services
• Applications
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 3/223ECE750:2008-2009 © PAMI Research Group – University of Waterloo
OutlineOutline
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo
• Introduction
• SWE Overview
• Sensor Web Concept
• SWE Framework
• Information Models and Schema
• Web Services
• Applications
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IntroductionIntroduction
The objective of this talk is to highlight the new open spatial
standards that can be used to guarantee the accessibility and
interoperability of spatial information resources.
Different applications of these widely selected and rapidly
growing standards will be described.
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IntroductionIntroduction
• What is Spatial Information?
� Location & exploitation of natural resources – minerals, soils,
vegetation, landscape.
� Viewing and analysis of networks – transport, water, energy and
telecoms.
� Location & distribution of people, businesses, assets, new
developments, services and other built infrastructure.
� Coordination of responses to emergencies, natural and man made
disasters – floods, epidemics, terrorism.
� Monitoring and management of spatially distributed variables –
health statistics, demographics, weather.
Ref. [1]
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IntroductionIntroduction
• What is Spatial Information?
� Multi-purpose recording and dissemination of information –
topographic mapping, hydrographic charting, weather forecasting.
� Monitoring of environmental change – ecology, sea level rise,
pollution, temperature, etc.
Ref. [1]
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IntroductionIntroduction
• Interoperability
� Interoperability is the ability of two or more systems or
components to exchange information and to use the information
that has been exchanged [2].
� The ability of two or more autonomous,
heterogeneous, distributed digital entities (e.g.
systems, applications, procedures, directories,
inventories or data sets) to communicate and
cooperate among themselves despite differences
in language, context, format or content. These
entities should be able to interact with one
another in meaningful ways without special
effort by the user - the data producer or
consumer - be it human or machine [3].
X
Y
X and Y are able to interact effectively at run-time to achieve shared goals
client
server
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IntroductionIntroduction
• Accessibility
Accessibility is a general term used to describe the degree to which a
product (e.g., device, service, environment) is accessible by as many
people as possible. Accessibility can be viewed as the “ability to
access” the functionality, and possible benefit, of some system or
entity. [Wikipedia]
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IntroductionIntroduction
• Semantic Web
1. Data is encoded with self-describing XML identifiers, enabling a
standard XML parser to parse the data.
2. The identifiers’ meanings (properties) are expressed using the
Resource Description Framework. RDF encodes the meaning in
sets of triples, each triple being like an elementary sentence’s
subject, verb, and object, with each element defined by a URI
(uniform resource identifier) on the Web.
3. Ontologies express the relationships between identifiers.
The Semantic Web has three key aspects.
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IntroductionIntroduction
• Semantic Web
These two data sources can publish data in XML as:
Internetdata sources-1 data sources-2
“<Temperature><Celsius>20</Celsius></Temperature>” and
“<Temperature><Fahrenheit>68</Fahrenheit></Temperature>.”
“<Temperature><Celsius>20</Celsius></Temperature>” and
“<Temperature><Fahrenheit>68</Fahrenheit></Temperature>.”
An associated RDF document can describe that Celsius and
Fahrenheit are temperature units, and an ontology can define the
relationship between Celsius and Fahrenheit. So, a data-processing
system can automatically infer that these two data points represent
the same temperature value.Ref. [4]
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IntroductionIntroduction
• XML Schema
XML Schemas express shared vocabularies and allow machines to
carry out rules made by people. They provide a means for defining the
structure, content and semantics of XML documents [W3C].
• A simple element is defined as<xs:element name=“name” type=“type” />
where:
– name is the name of the element
– the most common values for type arexs:boolean xs:integerxs:date xs:stringxs:decimal xs:time
• Other attributes a simple element may have:
– default=“default value” if no other value is specified
– fixed=“value” no other value may be specified
The XML
Schema
Definition
language
provides
more control
over
structure
and content
than DTD.
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“Situation awareness is the perception of elements in the
environment within a volume of time and space, the
comprehension of their meaning, and the projection of
their status in the near future.”
(Mica Endsley, 1988)
http://www.satechnologies.com/
IntroductionIntroduction
• Situation Awareness
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Collect
Relevant
Data
Pr
ov
en
an
ce
Relate
Situation
Entities
Semantic AnalysisSemantic AnalysisSemantic AnalysisSemantic Analysis
•thematic
•Spatio-Temporal
•trust
Identify
Situation
Entities
Ref. [7]
IntroductionIntroduction
• Situation Awareness
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Domain Ontology
Spatial Ontology
Temporal Ontology
• Situation Awareness Ontology
“Ontology is about the exact description of things and their
relationships.” World Wide Web Consortium (W3C)
IntroductionIntroduction
• Ontology
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OutlineOutline
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo
• Introduction
• SWE Overview
• Sensor Web Concept
• SWE Framework
• Information Models and Schema
• Web Services
• Applications
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SWE OverviewSWE Overview
www.opengeospatial.org/
• Open Geospatial Consortium (OGS)
� Consortium of 370+ companies,
government agencies, and academic
institutes.
� OGC Mission is to lead in the
development, promotion and
harmonization of open spatial standards.
� Open Standards development by
consensus process.
� Interoperability Programs provide end-to-
end implementation and testing before
specification approval.
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High-level Sensor (S-H) Low-level Sensor (S-L)
A-H E-H A-L E-L
H L
• How do we determine if A-H = A-L? (Same time? Same place?)
• How do we determine if E-H = E-L? (Same entity?)
• How do we determine if E-H or E-L constitutes a threat?
Ref. [3]
SWE OverviewSWE Overview
• Motivating Scenario
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� Quickly discover sensors and sensor data (secure or public) that
can meet my needs – location, observables, quality, ability to
task.
� Obtain sensor information in a standard encoding that is
understandable by me and my software.
� Readily access sensor observations in a common manner, and in
a form specific to my needs.
� Task sensors, when possible, to meet my specific needs.
� Subscribe to and receive alerts when a sensor measures a
particular phenomenon.
SWE OverviewSWE Overview
• Objectives
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OutlineOutline
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo
• Introduction
• SWE Overview
• Sensor Web Concept
• SWE Framework
• Information Models and Schema
• Web Services
• Applications
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Sensor Web ConceptSensor Web Concept
Clients
Surveillance
Health Monitor
Industrial
Process
Monitor
Automobile as
sensor probe
Traffic
Stored Sensor DataAirborne
imaging device
Environmental
Monitor
- All sensors reporting position
- All readable remotely
- All connected to the Web
- Some controllable remotely
- All with metadata registered.
Internet
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http://research.microsoft.com/en-us/projects/senseweb/
Pod
Sensor Web ConceptSensor Web Concept
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http://www.ndbc.noaa.gov/
Sensor Web ConceptSensor Web Concept
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• Sensor Pods
� one or more sensor leading to one or more data
channel,
� a processing unit such as a micro-controller or
microprocessor,
� a two-way communication component such as a
radio and antenna,
� an energy source such as a battery coupled with
a solar cell,
� a package to protect components against
sometimes harsh environment,
� a support such as a pole or tripod.
Sensor Web ConceptSensor Web Concept
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� Sensors will be web accessible.
� Sensors and sensor data will be discoverable.
� Sensor descriptions will use a standard encoding.
� Sensor observations will be delivered using standard encodings.
� Sensor observations will be accessible in real time or from
archives, to any legitimate user, over the web with a standard
request syntax.
� Sensors will be tasked in a standard way.
• Sensor Web Vision
Sensor Web ConceptSensor Web Concept
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� Sensors will be capable of issuing alerts based on observations, as
well as be able to respond to alerts issued by other sensors.
� Sensor services will be capable of real-time mining of
observations to find phenomena of immediate interest.
� Sensors and sensor nets will be able to act on their own (i.e. be
autonomous).
� Software will immediately be capable of geolocating and
processing observations from a newly-discovered sensor.
• Sensor Web Vision (Cont’d)
Sensor Web ConceptSensor Web Concept
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• Challenging Problems
Ref. [4]
- Query processing - Continuous, integrated push-based and pull-based processing
- Reliability - Federated and shared infrastructure.
Sensor Web ConceptSensor Web Concept
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• Challenging Problems
Ref. [4]
Sensor Web ConceptSensor Web Concept
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OutlineOutline
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo
• Introduction
• SWE Overview
• Sensor Web Concept
• SWE Framework
• Information Models and Schema
• Web Services
• Applications
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- vendor neutral
- extensive
- flexible
- adaptable
SWE FrameworkSWE Framework
Users—Decision Support ToolsProviders—Heterogeneous Sensor Network
In-Situ monitors
Bio/Chem/ Rad DetectorsSurveillance
AirborneSatellite
- sparse
- disparate
- mobile/in-situ
- extensible
Models and Simulations
- nested
- national, regional, urban
- adaptable - data assimilation
Sensor Web Enablement
- discovery
- access
- tasking
- alert notification
Web services and encodings
based on Open Standards
(OGC, ISO, OASIS, IEEE)
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SensorML initiated
at University of
Alabama in Huntsville:
NASA AIST funding
SensorML initiated
at University of
Alabama in Huntsville:
NASA AIST funding
OGC Web Services
Testbed 1.1:
Sponsors: EPA, NASA,
NIMA
Specs: SOS, O&M,
SensorML
Demo: NYC Terrorist
Sensors: weather
stations, water quality
OGC Web Services
Testbed 1.1:
Sponsors: EPA, NASA,
NIMA
Specs: SOS, O&M,
SensorML
Demo: NYC Terrorist
Sensors: weather
stations, water quality
OGC Web Services
Testbed 1.2:
Sponsors: EPA,
General Dynamics,
NASA, NIMA
Specs: SOS, O&M,
SensorML, SPS, WNS
Demo: Terrorist,
Hazardous Spill and
Tornado
Sensors: weather
stations, wind profiler,
video, UAV, stream
gauges
OGC Web Services
Testbed 1.2:
Sponsors: EPA,
General Dynamics,
NASA, NIMA
Specs: SOS, O&M,
SensorML, SPS, WNS
Demo: Terrorist,
Hazardous Spill and
Tornado
Sensors: weather
stations, wind profiler,
video, UAV, stream
gauges
1999 - 2000 2001
Specs advanced
through independent
R&D efforts in
Germany, Australia,
Canada and US
Sensor Web Work
Group established
Specs: SOS, O&M,
SensorML, SPS, WNS,
SAS
Sensors: wide variety
Specs advanced
through independent
R&D efforts in
Germany, Australia,
Canada and US
Sensor Web Work
Group established
Specs: SOS, O&M,
SensorML, SPS, WNS,
SAS
Sensors: wide variety
2002 2003-2004
SWE FrameworkSWE Framework
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OGC Web Services
Testbed 3.0:
Sponsors: NGA, ORNL,
LMCO, BAE
Specs: SOS, O&M,
SensorML, SPS,
TransducerML
Demo: Forest Fire in
Western US
Sensors: weather stations,
wind profiler, video, UAV,
satellite
SAS Interoperabilty
Experiment
OGC Web Services
Testbed 3.0:
Sponsors: NGA, ORNL,
LMCO, BAE
Specs: SOS, O&M,
SensorML, SPS,
TransducerML
Demo: Forest Fire in
Western US
Sensors: weather stations,
wind profiler, video, UAV,
satellite
SAS Interoperabilty
Experiment
SWE Specifications
toward approval:
SensorML – V0.0
TransducerML – V0.0
SOS – V0.0
SPS – V0.0
O&M – Best Practices
SAS – Best Practices
SWE Specifications
toward approval:
SensorML – V0.0
TransducerML – V0.0
SOS – V0.0
SPS – V0.0
O&M – Best Practices
SAS – Best Practices
OGC Web Services
Testbed 4.0:
Sponsors: NGA, NASA,
ORNL, LMCO
Specs: SOS, O&M,
SensorML, SPS,
TransducerML, SAS
Demo: Emergency
Hospital
Sensors: weather
stations, wind profiler,
video, UAV, satellite
OGC Web Services
Testbed 4.0:
Sponsors: NGA, NASA,
ORNL, LMCO
Specs: SOS, O&M,
SensorML, SPS,
TransducerML, SAS
Demo: Emergency
Hospital
Sensors: weather
stations, wind profiler,
video, UAV, satellite
2005 2006
SWE FrameworkSWE Framework
2008
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SWE FrameworkSWE Framework
Web ServicesInformation Models and Schema
SWE Components
TransducerMLTransducerML
Observations & Measurements
(O&M)
Observations & Measurements
(O&M)SensorMLSensorML
GML ObservationsApplicationSchema
GML ObservationsApplicationSchema
Client
SensorObservationService
SensorPlanningService
SensorAlertService
WebNotificationService
SOSClient
SPSClient
SASClient
WNSClient
Internet
Catalog Service
Sensor Registries
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SWE FrameworkSWE Framework
Observations and
MeasurementsSensorML TransducerML
Information Layer
Sensor Observation
Service
Sensor Planning
Service
Sensor Alert
Service
Service Layer
Integrated Ocean
Observing SystemGEOSS …
Sensor Web Layer
The Sensor Web Standards Stack
Ref. [5]
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OutlineOutline
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo
• Introduction
• SWE Overview
• Sensor Web Concept
• SWE Framework
• Information Models and Schema
• Web Services
• Applications
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Information Models and SchemaInformation Models and Schema
TransducerML
Multiplexed, Real Time
Streaming Protocol
TransducerML
Multiplexed, Real Time
Streaming Protocol
Observations &
Measurements
(O&M)
Information Model
for Observations and Sensing
Observations &
Measurements
(O&M)
Information Model
for Observations and Sensing
SensorML
Sensor and Processing
Description Language
SensorML
Sensor and Processing
Description Language
GML Observations
Application Schema
SWE Common Data
Structure And Encodings
GML Observations
Application Schema
SWE Common Data
Structure And Encodings
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Information Models and SchemaInformation Models and Schema
• Observations and Measurements (O&M)
– An Observation is an Event whose result is an estimate of the value
of some Property of the Feature-of-interest, obtained using a
specified Procedure.
Procedure
Observation
Feature-of-interest
Observedproperty
Results
uses
estimates value of
has
observes
carries
Ref. [5]
O&M Conceptual Model
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Information Models and SchemaInformation Models and Schema
• Observations and Measurements (O&M)
– The featureOfInterest is a
feature of any type, which is a
representation of the
observation target, being the
real-world object regarding
which the observation is made.
Procedure
Observation
Feature-of-interest
Observedproperty
Results
uses
estimates value of
has
observes
carries
O&M Conceptual Model
– The observedProperty identifies or describes the phenomenon for
which the observation result provides an estimate of its value. It
must be a property associated with the type of the feature of
interest.
– The procedure is the description of a process used to generate the
result. It must be suitable for the observed property.
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Information Models and SchemaInformation Models and Schema
• Observations and Measurements (O&M)
Example: Marine Science Observation Ref. [5]
Feature-of-interest:Ship’ s cruise track
Observed property:Seawater temperature
Procedure:Thermosalinograph
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Information Models and SchemaInformation Models and Schema
• Observations and Measurements (O&M)
«FeatureType»Observation
+ quality: DQ_Element [0..1]+ responsible: CI_ResponsibleParty [0..1]+ result: Any
«FeatureType»Event
+ eventParameter: TypedValue [0..*]+ time: TM_Object
«DataType»TypedValue
+ property: ScopedName+ value: Any
«Union»Procedure
+ procedureType: ProcedureSystem+ procedureUse: ProcedureEvent
AnyIdentifiableObject
«FeatureType»AnyIdentifiableFeature
AnyDefinition
«ObjectType»Phenomenon
+followingEvent 0..*+precedingEvent 0..*
+generatedObservation
0..*
+procedure 1
+observedProperty1{Definition must be of aphenomenon that is a propertyof the featureOfInterest}
+propertyValueProvider
0..*
+featureOfInterest
1
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Information Models and SchemaInformation Models and Schema
• Observations and Measurements (O&M)
simple observation to determine the mass of a specific banana
Feature-of-interest:banana
Observed property:mass
Procedure:mass
Result:Mass in kg
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Information Models and SchemaInformation Models and Schema
• GML Observations Application Schema
Geography Markup Language provides the basis for domain- or
community-specific “Application Schemas”, which in turn support
data interoperability within a community of interest.
XML Schema basic data types
GMLgeometry, topology, temporal, etc.
Traffic ManagementEnviron-
ment RoadInfra-structureCadastre,
Land Use
Traffic Information
[6]
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Information Models and SchemaInformation Models and Schema
• GML Observations Application Schema
GML contains a rich set of primitives which are used to build
application specific schemas or application languages:
� Feature
� Geometry
� Coordinate Reference System
� Topology
� Time
� Dynamic feature
� Coverage (including geographic images)
� Unit of measure
� Directions
� Observations
� Map presentation
styling rules
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Information Models and SchemaInformation Models and Schema
• GML Observations Application Schema
<gml:Point gml:id=“p21” srsName="urn:ogc:def:crs:EPSG:6.6:4326">
<gml:coordinates>45.67, 88.56</gml:coordinates>
</gml:Point>
<gml:Point gml:id=“p21” srsName="urn:ogc:def:crs:EPSG:6.6:4326">
<gml:pos dimension="2">45.67 88.56</gml:pos>
</gml:Point>
<gml:LineString gml:id="p21" srsName="urn:ogc:def:crs:EPSG:6.6:4326">
<gml:posList dimension="2">45.67 88.56 55.56 89.44</gml:posList>
</gml:LineString >
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Information Models and SchemaInformation Models and Schema
• Sensor Model Language (SensorML)
– SensorML is an XML schema for defining the geometric, dynamic,
and observational characteristics of a sensor.
– The primary focus of SensorML is to define processes and
processing components associated with the measurement and
post-measurement transformation of observations such as
actuators, spatial transforms, and data processes, to name a few.
– SensorML can, but generally does not, provide a detailed
description of the hardware design of a sensor. Rather it is a
general schema for describing functional models of the sensor.
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The purposes of SensorML are to:
� Provide general sensor information in support of data discovery
� Support the processing and analysis of the sensor measurements
� Support the geolocation of the measured data.
� Provide performance characteristics (e.g. accuracy, threshold,
etc.).
� Archive fundamental properties and assumptions regarding
sensor.
Information Models and SchemaInformation Models and Schema
• Sensor Model Language (SensorML)
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Information Models and SchemaInformation Models and Schema
• Sensor Model Language (SensorML)
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Information Models and SchemaInformation Models and Schema
• Sensor Model Language (SensorML)
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Information Models and SchemaInformation Models and Schema
• Sensor Model Language (SensorML)
Person
Company
Coordinates
Coordinate System
SpatialOntology
DomainOntology
Event
Situation
Situation AwarenessOntology
Time Units
Timezone
TemporalOntology
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Information Models and SchemaInformation Models and Schema
• TransducerML
– Transducer Markup Language (TML or TransducerML) aims at
standardizing a way to exchange raw or preprocessed sensor data.
TML facilitates the interoperability and fusion of transducer data.
Here a transducer is either a receiver (i.e. sensor) or a transmitter.
– TML exchanges both data and metadata in the same or separate
packages.
– Data: is formatted in a natural transducer format.
– Metadata: describes everything an engineer needs to know about
the data, such as: data content and structure, transducer
characteristics, transducer geometry model, transducer
interrelationships. Time sensitive metadata is treated as sensor
data.
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Information Models and SchemaInformation Models and Schema
• TransducerML
– TML is a sensor data exchange language that allows for the fusion
of heterogeneous sensor data at levels not presently achievable
(i.e. upstream data).
– TML can be used to communicate data from remote and/or in situ
sensors (Vision, audio, IR, Sonar, magnetic, GPS, odometry,
climate, environmental, position, temperature, force, sound, etc.).
– TML is a domain independent language (i.e. can be used for any
application that requires the exchange of sensor data, e.g.
environmental, image or signals surveillance and reconnaissance,
medical imaging, factory automation, etc),
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Information Models and SchemaInformation Models and Schema
• TransducerML
Transducer
ComputerComputerUser
TransducerSpecificInterface
TransducerNormalizedExchangeInterface
DomainSpecificProducts
� TML is Sensor Agnostic
- Metadata is Common for all Type Sensors
- Enables a “Common Sensor Processor”
� TML is Application Domain Agnostic
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Information Models and SchemaInformation Models and Schema
• TransducerML
– TML is Software; “Loaded” on or Near Sensor
– Exchanges Raw Sensor Data
– XML Based—Fully Compatible with XML
– Normalizes All Data Variables
* Temporal
* Spatial
* Phenomena Values
* Enables Data for Fusion
Ref. [8]
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 53/2253ECE750:2008-2009 © PAMI Research Group – University of Waterloo
Subscriber
3D Space-timeData Associations
Sensor Representations at tn
ReferenceProducts
Common Space-Time Reference
Time Line >><<Archive
Live
Remote: Video, audio, IR, Sonar,…In Situ: Temp, pres, pos, vib, time, …
Live Sensors
NetworkTML Data
BrokerNetwork
DataArchive
DataArchive
DataArchive
Reference Data, Raw Sensor Data (TML),Processed Sensor Data (Products)
Archive Data
Network
Archive
Publishers
Live
Publishers Sensor
System
SensorSystem
SensorSystem
Live publisher
Live publisher
Live publisher
TML
TML
TML
TMLTML
TML
TML
TMLTML
Common modeling for any sensor enabled by TML
Data Discovery enabled by TML
Pulg-n-playenabled by TML
Common format for posting raw, streaming, real-time, multi-sensor data enabled by TML
Common spatial and temporal datum’s enabled by TML
Exchange of data via web enabled by TML
Smart Pull of Sensor data
Smart Pull of Sensor data
Smart pull of sensor data enabled by TML
Information Models and SchemaInformation Models and Schema
• TransducerML
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 54/2254ECE750:2008-2009 © PAMI Research Group – University of Waterloo
TML
Proprietary
Best at the Sensor
OK downstream
Information Models and SchemaInformation Models and Schema
• TransducerML: Where?
Ref. [8]
Computer
Transducer
Transducer
TMLS/W
Transducer System
TMLTMLS/W
Computer
Transducer
Transducer
Computer
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 55/2255ECE750:2008-2009 © PAMI Research Group – University of Waterloo
3
6
9
12
System Clock
Sensor
Processor
TML Formatter
<data id=“01” clk=“32:24:12”>010011</data>
TMLDescriptionDocument
.xml
010011
Information Models and SchemaInformation Models and Schema
• TransducerML: How it works?
Sensor
Id=01
System
Ref. [8]
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<tml>
<system>
<systemClock>…period, count accy</systemClock>
<transducers>…transducer models…</transducers>
<process>…process models…</process>
<relations>…transducer relationships…</relations>
</system>
<prodDataDesc>ID mapping,parsing, encoding and
sequencing…<prodDataDesc>
<tml>
<tml>
<system>
<systemClock>…period, count accy</systemClock>
<transducers>…transducer models…</transducers>
<process>…process models…</process>
<relations>…transducer relationships…</relations>
</system>
<prodDataDesc>ID mapping,parsing, encoding and
sequencing…<prodDataDesc>
<tml>
- Static data
TML describes the transducer data (Common Transducer Model)
Information Models and SchemaInformation Models and Schema
• TransducerML: Static/Dynamic Data
Ref. [8]
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<tml><data ref=“t001” clk=“3F63B6432674”>…transducer data…</ data ><data ref=“t001” clk=“3F63B64326A1”>…transducer data…</ data ><data ref=“t002” clk=“3F63B6432701”>… transducer data …</ data ><data ref=“t001” clk=“3F63B6432723”>… transducer data …</ data ><data ref=“t003” clk=“3F63B643273C”>… transducer data …</ data ><data ref=“t001” clk=“3F63B6432767”>… transducer data …</ data ><data ref=“t006” clk=“3F63B6432788”>… transducer data …</ data ><data ref=“t001” clk=“3F63B64327E9”>… transducer data …</ data ><data ref=“t001” clk=“3F63B6432810”>… transducer data …</ data ><data ref=“t001” clk=“3F63B6432825”>… transducer data …</ data ><data ref=“t008” clk=“3F63B643281B”>… transducer data …</ data ><data ref=“t001” clk=“3F63B6432856”>… transducer data …</ data ><data ref=“t002” clk=“3F63B6432850”>… transducer data …</ data >
<tml>
<tml><data ref=“t001” clk=“3F63B6432674”>…transducer data…</ data ><data ref=“t001” clk=“3F63B64326A1”>…transducer data…</ data ><data ref=“t002” clk=“3F63B6432701”>… transducer data …</ data ><data ref=“t001” clk=“3F63B6432723”>… transducer data …</ data ><data ref=“t003” clk=“3F63B643273C”>… transducer data …</ data ><data ref=“t001” clk=“3F63B6432767”>… transducer data …</ data ><data ref=“t006” clk=“3F63B6432788”>… transducer data …</ data ><data ref=“t001” clk=“3F63B64327E9”>… transducer data …</ data ><data ref=“t001” clk=“3F63B6432810”>… transducer data …</ data ><data ref=“t001” clk=“3F63B6432825”>… transducer data …</ data ><data ref=“t008” clk=“3F63B643281B”>… transducer data …</ data ><data ref=“t001” clk=“3F63B6432856”>… transducer data …</ data ><data ref=“t002” clk=“3F63B6432850”>… transducer data …</ data >
<tml>
TML transports the transducer data
Information Models and SchemaInformation Models and Schema
• TransducerML: Static/Dynamic Data
Ref. [8]
- Dynamic data
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Information Models and SchemaInformation Models and Schema
• TransducerML: Example
JPEGCompress
Process
Digital Camera
GPS
IMU
Compass
Transducer
IMAGE SIZE
Base64Produces Base64JPEG Videoclusters
Produces IMAGE_SIZEclusters
Produces GPSclusters
Produces IMUclusters
Produces Compassclusters
TMLNode
Sys clk
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 59/2259ECE750:2008-2009 © PAMI Research Group – University of Waterloo
Information Models and SchemaInformation Models and Schema
• TransducerML: Example
<data clk='28118774' ref='IMU'>22.8,1.1,3.4</data> <!—IMU: true heading, pitch roll-->
<data clk='28118792' ref='COMPASS'>21.1</data> <!—COMPASS: mag heading-->
<data clk='28118795' ref='GPS'>516866,-4702126,4264297.2005-08-26T16:31:49Z</data><!--GPS: X, Y, Z, Time-->
<data clk='28118800' ref='IMAGE_SIZE'>49094</data><data clk='28118800' ref='CAM'>...base64 JPEG video...</data><data clk='28118874' ref='IMU'>23.9,2.7,-1.1</data><data clk='28118888' ref='Weather'>0,15.3,35,18.8,18,0.0,22.5, 82.3</data><!--WX: rain,dewPt,humid,temp,wndchl,wndSpd,wndDir,baroPres--><data clk='28118899' ref='IMAGE_SIZE'>49388</data><data clk='28118899' ref='CAM'>... base64 JPEG video...</data><data clk='28118974' ref='IMU'>0.1 -1.2 1.1</data><data clk='28118999' ref='IMAGE_SIZE'>49252</data><data clk='28118999' ref='CAM'>... base64 JPEG video...</data><data clk='28119174' ref='IMU'>-0.1 1.3 -0.2</data><data clk='28119199' ref='IMAGE_SIZE'>49628</data><data clk='28119199' ref='CAM'>... base64 JPEG video...</data><data clk='28119227' ref='COMPASS'>22.5 0.2 -1.2</data>
<data clk='28118774' ref='IMU'>22.8,1.1,3.4</data> <!—IMU: true heading, pitch roll-->
<data clk='28118792' ref='COMPASS'>21.1</data> <!—COMPASS: mag heading-->
<data clk='28118795' ref='GPS'>516866,-4702126,4264297.2005-08-26T16:31:49Z</data><!--GPS: X, Y, Z, Time-->
<data clk='28118800' ref='IMAGE_SIZE'>49094</data><data clk='28118800' ref='CAM'>...base64 JPEG video...</data><data clk='28118874' ref='IMU'>23.9,2.7,-1.1</data><data clk='28118888' ref='Weather'>0,15.3,35,18.8,18,0.0,22.5, 82.3</data><!--WX: rain,dewPt,humid,temp,wndchl,wndSpd,wndDir,baroPres--><data clk='28118899' ref='IMAGE_SIZE'>49388</data><data clk='28118899' ref='CAM'>... base64 JPEG video...</data><data clk='28118974' ref='IMU'>0.1 -1.2 1.1</data><data clk='28118999' ref='IMAGE_SIZE'>49252</data><data clk='28118999' ref='CAM'>... base64 JPEG video...</data><data clk='28119174' ref='IMU'>-0.1 1.3 -0.2</data><data clk='28119199' ref='IMAGE_SIZE'>49628</data><data clk='28119199' ref='CAM'>... base64 JPEG video...</data><data clk='28119227' ref='COMPASS'>22.5 0.2 -1.2</data>
The data entity contains
data multiplexed from all
of the sensor contained
within a system.
The Data entity will
contain a stream of
sensor data transported
in small sensor chunks
called clusters.
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 60/2260ECE750:2008-2009 © PAMI Research Group – University of Waterloo
OutlineOutline
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo
• Introduction
• SWE Overview
• Sensor Web Concept
• SWE Framework
• Information Models and Schema
• Web Services
• Applications
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 61/2261ECE750:2008-2009 © PAMI Research Group – University of Waterloo
Web ServicesWeb Services
CatalogService
SOS
SAS
SPS
Clients
Access Sensor Description and
DataCommand and Task Sensor Systems
Dispatch Sensor Alerts to registered
Users
Discover Services, Sensors, Providers,
Data
Accessible from various types of clients from PDAs and Cell Phones to high end Workstations
SOS: Sensor Observation Service
SPS: Sensor Planning Service
SAS: Sensor Alert Service
Sensor Registries
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Web ServicesWeb Services
• Sensor Observation Service (SOS)
The goal of SOS is to provide access to observations from sensors and
sensor systems in a standard way that is consistent for all sensor
systems including remote, in-situ, fixed and mobile sensors.
Measurements& Observations
ObservableDictionary
ObservationXSD
references
ConstrainedBy
SensorObservation
SWEClient
GetObservation
Observations/Measurements
This may be collection of sensors
Provides accessTo
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Operation Description
GetCapabilities Enables a client to retrieve metadata about the capabilities of a SOS.
DescribeSensor Allows a client to obtain detailed metadata about the sensors and platforms exposed by the SOS service.
GetObservation Operation to query a sensor system to retrieve archived or real-time observation data.
RegisterSensor Allows a client to register a new sensor system with a SOS.
InsertObservation Allows a client to insert a new observation for a registered sensor system.
Web ServicesWeb Services
• Sensor Observation Service (SOS)
Ref. [9]
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Web ServicesWeb Services
• Sensor Alert Service (SAS)
– SAS is a web service that allows a sensor to be monitored for an
“alarming condition”, e.g. temperature exceeds a given value.
– It enables data node to publish alerts and subscribe to alerts from
other nodes.
– SAS is a basic publish/subscribe system with asynchronous
notification.
– A user may subscribe to receive alerts
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Operation Description
GetCapabilities Enables a client to retrieve metadata about the capabilities of a SAS.
Advertise Allows a client to advertise what kind of data could be published.
RenewAdvertisement Allow a client to renew a previous advertisement.
CancelAdvertisement Allows a client to cancel a previously registered advertisement offering.
Subscribe Allows a client to subscribe to an alert that has been advertised by the SAS.
RenewSubscription Allow a client to renew a subscription that has expired.
CancelSubscription Allows a client to cancel a subscription
Web ServicesWeb Services
• Sensor Alert Service (SAS)
Ref. [9]
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Web ServicesWeb Services
• Sensor Planning Service (SPS)
SPS enables an interoperable service by which a client can determine
collection feasibility for a desired set of collection requests for one or
more sensors/platforms, or a client may submit collection requests
directly to these sensors/platforms.
Sensor PlanningService
Service Registries
Sensor CollectionService
Available SCS for Water Quality
Sensor/Platform & Observable Registries
SWEClient
Get Water Quality Observation & Metadata
ObservationArchive& Catalog
LocalArchive & Catalog
Base Station & Sensors
Observ.Request
Observ.Response
Discover SCS for Water Quality
Collection Request
Confirmation of valid collection request
User
Need for Water quality data
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Web ServicesWeb Services
• Web Notification Service
WNS manages message dialogue between client and Web service(s)
for long duration (asynchronous) processes.
Operation Description
GetCapabilities Enables a client to retrieve metadata about the capabilities of a WNS.
Register Register a user (or a group of users) to receive notifications.
UnRegister Un-register a user (or a group of users)
DoNotification Initiate the notification of a user
GetWSDL Retreive a WSDL description of the service interface
UpdateSingleUserReg Update the settings of a registered user.
UpdateMultiUserReg Update the settings of a registered group of users.
GetMessage Allows a client to retrieve a message stored by the WNS, i.e. information of failed messages.
Ref. [9]
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 68/2268ECE750:2008-2009 © PAMI Research Group – University of Waterloo
Web ServicesWeb Services
• Sensor Registries
Applications
Sensor TypesRegistryService
Units of Measure
Phenomena
OGC Catalog Service for the Web (CSW)
Ref. [10]
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Web ServicesWeb Services
• More Services
GeoDSSClient
Decision Maker/Analyst
Geo Decision Support Services GeoDSS
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Web ServicesWeb Services
• More Services
Geo Decision Support Services GeoDSS
GeoDSSClient
Decision Maker/Analyst
Activity 1: Schema Tailoring &
Maintenance.
Activity 2: Define and Implement Data
Aggregation Service.
Activity 3: Symbology Management &
Feature Portrayal Service.
Activity 4: GeoVideo Service.
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 71/2271ECE750:2008-2009 © PAMI Research Group – University of Waterloo
OutlineOutline
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo
• Introduction
• SWE Overview
• Sensor Web Concept
• SWE Framework
• Information Models and Schema
• Web Services
• Applications
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 72/2272ECE750:2008-2009 © PAMI Research Group – University of Waterloo
SWE ApplicationsSWE Applications
• OGC OWS-3
• GEOSS
• INSPIRE
• SANY
• OPENIOOS
• SensorBay
• SunSPOTs
• GITEWS
• PulseNet
• WaterOneFlow
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• OGC OWS-3
SWE ApplicationsSWE Applications
http://www.opengeospatial.org/demo/ows3/
OGC Web Services, Phase 3 (OWS-3) is an
Interoperability Initiative that advanced
OGC technology in the following areas:
� Common Architecture
� OGC Location Services (OpenLS)
� Sensor Web Enablement (SWE)
� Geo-Decision Support Services (GeoDSS)
� Geo-Digital Rights Management (GeoDRM)
� The IEEE 1451™ and OASIS Common alerting Protocol (CAP)
standards were used in OWS-3.
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• GEOSShttp://www.earthobservations.org/
SWE ApplicationsSWE Applications
– the Group on Earth Observations (a grouping of 76 national
governments and other international organizations) aims to
integrate existing observation networks into a Global Earth
Observation System of Systems (GEOSS) to achieve
comprehensive, coordinated and sustained observation of the
Earth system.
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– GEOSS has the objective to continuously monitor the state of the
earth in order to increase knowledge and understanding of our
planet and its processes.
– Timely delivery of earth observation data is a key aspect in
identifying potential natural and human threats, such as tornados,
tsunamis, wild fires, or algae blooms.
– Data from in-situ or remote sensing devices form the basis for
analyzing gradual processes, such as increasing drought, water
shortages, or rising sea levels.
http://www.earthobservations.org/
SWE ApplicationsSWE Applications
• GEOSS
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SWE ApplicationsSWE Applications
• Infrastructure for Spatial Information in Europe
HarmonisedData policy
Collaborativeagreements
CEN / ISO / OGCNational and Sub-
national SDI
Commercial & Professional Users
Citizens
Utility & PublicServices
NGOs and not-for-profit orgs
Government & Administrations
ResearchEuropean Data
National and Sub-national SDI
National and Sub-national SDI
Local data
Discovery Service
Technical Integration/harmonisation
Data resourcesData resources
INSPIRE specificationsINSPIRE specifications
UsersUsers
request for information services
delivery of information services
SDI – Spatial Data Infrastructure
(INSPIRE)
http://inspire.jrc.ec.europa.eu/
Data Resources
INSPIRE Specifications
Users
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SWE ApplicationsSWE Applications
• Sensors Anywhere (SANY)http://www.sany-ip.eu/
Sensors Fusion Services for
Environmental Decision-Support
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 78/2278ECE750:2008-2009 © PAMI Research Group – University of Waterloo
SWE ApplicationsSWE Applications
• Sensors Anywhere (SANY)http://www.sany-ip.eu/
Sensors Fusion Services for Environmental Decision-
Support
– Unified and generic data fusion methodology
– Generic development and re-use of fusion
technology
– Generic standards related to data fusion
services implementations
– Generic and formalized information fusion
framework
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SWE ApplicationsSWE Applications
• OPENIOOS
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 80/2280ECE750:2008-2009 © PAMI Research Group – University of Waterloo
http://www.sensorbay.ca
Canadian Centre for Marine Communications
The Sensor Bay project used Compusult’s Web Enterprise SuiteSWES to set up a SWE compliant sensor network in remarkably short
time and budget.
The Sensor Bay project used Compusult’s Web Enterprise SuiteSWES to set up a SWE compliant sensor network in remarkably short
time and budget.
SWE ApplicationsSWE Applications
• SensorBay
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 81/2281ECE750:2008-2009 © PAMI Research Group – University of Waterloo
SWE ApplicationsSWE Applications
• Sensor Web in SunSPOTs https://www.sunspotworld.com/
Dimensions
o 41 x 23 x 70 mm
o 54 grams
Sun SPOT Processor Board
o 180 MHz 32 bit ARM920T core - 512K RAM/4M Flash
o 2.4 GHz IEEE 802.15.4 radio with integrated antenna
o USB interface
o 3.7V rechargeable 720 mAh lithium-ion battery
o 32 uA deep sleep mode
General Purpose Sensor Board
o 2G/6G 3-axis accelerometer
o Temperature sensor
o Light sensor
o 8 tri-color LEDs
o 6 analog inputs
o 2 momentary switches
o 5 general purpose I/O pins and 4 high current output pins
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SWE ApplicationsSWE Applications
• Sensor Web in SunSPOTs https://www.sunspotworld.com/
SunSPOTs provides a tool for rapidly prototyping sensor-based
applications, and for testing and verifying algorithms on a small scale
prior to deploying them in industrial operation. This tool can be
exploited to reduce costs by evaluating novel algorithms a priori
before adapting them to real-world problems.
Applications: detection and warning systems, environmental
monitoring, automotive engineering, warehouse/container
management, logistics, monitoring of buildings, home automation,
weather forecasting, medical monitoring of patients and diagnosis,
and agriculture and farming
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SWE ApplicationsSWE Applications
• Sensor Web in SunSPOTs
Indoor Environmental Quality Measurement http://www.salzburgresearch.at/
Based on SunSPOT sensor technology, this project develops an
indoor environmental quality application.
Each sensor station is composed of two main components:
(i) an external sensor which can measure electromagnetic pollution,
air pressure, humidity, air temperature, brightness, noise or carbon
dioxide, and
(ii) a SunSPOT module which is responsible for pre-processing
acquired sensor data and propagating them through the sensor
network.
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SWE ApplicationsSWE Applications
• Sensor Web in SunSPOTs
Indoor Environmental Quality Measurement http://www.salzburgresearch.at/
The base station managing this sensor network is an OGC-compliant
Sensor Web application.
It allows for administration of the sensors, and reading and
processing of sensor data; for example, users can visualize and
interaction with current sensor values on a graphical, Web-based
interface. In particular, the Sensor Model Language (SensorML),
Observation & Measurements (O&M), and Sensor Observations
Service (SOS) specifications are adopted.
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 85/2285ECE750:2008-2009 © PAMI Research Group – University of Waterloo
SWE ApplicationsSWE Applications
• Sensor Web in SunSPOTs
Indoor Environmental Quality Measurement http://www.salzburgresearch.at/
Communication between the sensor and the base station can occur
in both push and pull modes and in a regular or on-demand fashion,
where the values are communicated over the meshed wireless sensor
network.
All configurations can be defined by the user during run-time. In
addition, a user can employ a sensor value reader device (essentially
another SunSPOT) in order to get data from a specific sensor by
physically moving into the communication range of that sensor and
querying the accordant data.
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 86/2286ECE750:2008-2009 © PAMI Research Group – University of Waterloo
SWE ApplicationsSWE Applications
• GITEWS
www.gitews.org/ German Indonesian Tsunami Early Warning System
The German
organization
52North
provides a
complete set
of SWE
services under
GPL license.
35 Million
Euro project
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 87/2287ECE750:2008-2009 © PAMI Research Group – University of Waterloo
SWE ApplicationsSWE Applications
• PulseNet:Northrop Grumman Corporation (NGC)
http://www.northropgrumman.com/
PULSENet clients in multiple Web locations can task heterogeneous sensors and sensor systems
In its first year,
PULSENet was
successfully field tested
under a real-life use
case scenario that fused
data from four
unattended ground
sensors, two tracking
cameras, 1,800 NOAA
weather stations and
NASA’s EO-1 satellite.
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 88/2288ECE750:2008-2009 © PAMI Research Group – University of Waterloo
SWE ApplicationsSWE Applications
• WaterOneFlow
WaterOneFlowWeb services will provide a standard mechanism for flow of hydrologic data between hydrologic data servers (databases) and users
http://www.cuahsi.org/
Consortium of Universities for the Advancement of Hydrologic Science (CUAHSI)
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 89/2289ECE750:2008-2009 © PAMI Research Group – University of Waterloo
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[2] Institute of Electrical and Electronics Engineers. IEEE Standard Computer Dictionary: A
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[3] Sensor Data Management. SAVig: Sensor Aided Vigilance Project, Kno.e.sis, Wright State
University: http://knoesis.wright.edu/
[4] M. Balazinska, A. Deshpande, M. Franklin, P. Gibbons, J. Gray, S. Nath, M. Hansen, M.
Liebhold, A. Szalay, V. Tao, “Data Management in the Worldwide Sensor Web,” PERVASIVE
Computing, 1536-1268, 2007.
[5] Andrew Woolf, “Building the Sensor Web - Standard by Standard”,Ercim New, Online Edition:
http://ercim-news.ercim.org/content/view/520/705/
[6] Geography Markup Language(GML). Clemens Portele – interactive instruments GmbH.
[7] Mieczyslaw M. Kokar, Christopher J. Matheus and Kenneth Baclawski , “Ontology-based
situation awareness,” Information Fusion, Volume 10, Issue 1 (January 2009) Pages 83-98,
ISSN:1566-2535, Elsevier Science Publishers, 2009.
[8] Transducer Mark Up Language (TML) a Fusion Enabling Technology, Steve Havens and Don
Jenkins, http://www.argonst.com/.
[9] OSCAR-Sensor Web Enablement Prototype, A Presentation by Paul Martin, ComSine Limited,
http://81.29.75.200/, October 2007.
[10] Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.