(PDF) Sensor Interoperability and Accessibility

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Sensor Web Enablement (SWE):

Sensor Interoperability and

Accessibility

Dr. Alaa Khamis

Pattern Analysis and Machine Intelligence

University of Waterloo

akhamis@pami.uwaterloo.ca

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Outline

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• Introduction

• SWE Overview

• Sensor Web Concept

• SWE Framework

• Information Models and Schema

• Web Services

• Applications

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Outline

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• Introduction

• SWE Overview

• Sensor Web Concept

• SWE Framework

• Information Models and Schema

• Web Services

• Applications

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Introduction

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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Introduction

• 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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Introduction

• 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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Introduction

• 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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Introduction

• 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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Introduction

• 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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Introduction

• Semantic Web

These two data sources can publish data in XML as:

Internet

data 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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Introduction

• 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 are

xs:boolean xs:integer

xs:date xs:string

xs: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/

Introduction

• Situation Awareness

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Collect

Relevant

Data

Provenance

Relate

Situation

Entities

Semantic Analysis

Semantic AnalysisSemantic Analysis

Semantic Analysis

•thematic

•Spatio-Temporal

•trust

Identify

Situation

Entities

Ref. [7]

Introduction

• 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)

Introduction

• Ontology

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Outline

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• Introduction

• SWE Overview

• Sensor Web Concept

• SWE Framework

• Information Models and Schema

• Web Services

• Applications

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SWE 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 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 Overview

• Objectives

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Outline

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• Introduction

• SWE Overview

• Sensor Web Concept

• SWE Framework

• Information Models and Schema

• Web Services

• Applications

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Sensor Web Concept

Clients

Surveillance

Health Monitor

Industrial

Process

Monitor

Automobile as

sensor probe

Traffic

Stored Sensor Data

Airborne

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 Concept

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http://www.ndbc.noaa.gov/

Sensor 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 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 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 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 Concept

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• Challenging Problems

Ref. [4]

Sensor Web Concept

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Outline

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• 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 Framework

Users—Decision Support Tools

Providers—Heterogeneous

Sensor Network

In-Situ

monitors

Bio/Chem/

Rad Detectors

Surveillance

Airborne Satellite

- 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 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 Framework

2008

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SWE Framework

Web ServicesInformation Models and Schema

SWE Components

TransducerML

Observations &

Measurements

(O&M)

Observations &

Measurements

(O&M) SensorML

SensorML

GML

Observations

Application

Schema

GML

Observations

Application

Schema

Client

Sensor

Observation

Service

Sensor

Planning

Service

Sensor

Alert

Service

Web

Notification

Service

SOS

Client

SPS

Client

SAS

Client

WNS

Client

Internet

Catalog Service

Sensor

Registries

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SWE Framework

Observations and

Measurements SensorML TransducerML

Information Layer

Sensor Observation

Service

Sensor Planning

Service

Sensor Alert

Service

Service Layer

Integrated Ocean

Observing System GEOSS …

Sensor Web Layer

The Sensor Web Standards Stack

Ref. [5]

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Outline

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 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 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

Observed

property

Results

uses

estimates value of

has

observes

carries

Ref. [5]

O&M Conceptual Model

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Information 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

Observed

property

Results

uses

estimates value of

has

observes

carries

O&M Conceptual Mod el

– 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 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 Schema

• Observations and Measurements (O&M)

«FeatureType»

Observ ation

+ qual ity: DQ_E lement [0..1]

+ responsible : CI_Responsibl eParty [0..1]

+ result: An y

«FeatureType»

Event

+ eventP arameter: T ypedVa lue [0. .*]

+ time : TM_ Object

«DataT ype»

TypedValue

+ property: ScopedNam e

+ value : Any

«Union»

Procedure

+ procedu reType: P rocedureSystem

+ procedu reUse: Proced ureEvent

AnyIdenti fiableObject

«FeatureType»

AnyIdentifiableFeature

AnyDefi nition

«ObjectType»

Phenomenon

+fol lowingE vent 0..*+preced ingEven t 0..*

+generatedObservation

0..*

+procedure 1

+observedProperty

1

{Defini tion m ust be of a

phen omenon that i s a prope rty

of the featureO fInterest}

+propertyValueProvider

0..*

+featureOfInterest

1

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Information 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 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

GML

geometry, topology, temporal, etc.

Traffic

Management

Environ-

ment Road

Infra-

structure

Cadastre,

Land Use

Traffic

Information

[6]

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Information 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 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 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 Schema

• Sensor Model Language (SensorML)

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Information Models and Schema

• Sensor Model Language (SensorML)

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Information Models and Schema

• Sensor Model Language (SensorML)

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Information Models and Schema

• Sensor Model Language (SensorML)

Person

Company

Coordinates

Coordinate System

Spatial

Ontology

Domain

Ontology

Event

Situation

Situation Awareness

Ontology

Time Units

Timezone

Temporal

Ontology

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Information 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 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 Schema

• TransducerML

Transducer

ComputerComputer

User

Transducer

Specific

Interface

Transducer

Normalized

Exchange

Interface

Domain

Specific

Products

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 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]

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Subscriber

3D Space-time

Data Associations

Sensor

Representations

at t

n

Reference

Products

Common Space-Time Reference

Time Line >> <<Archive

Live

Remote: Video, audio, IR, Sonar,…

In Situ: Temp, pres, pos, vib, time, …

Live Sensors

Network

TML

Data

Broker Network

Data

Archive

Data

Archive

Data

Archive

Reference Data, Raw Sensor Data (TML),

Processed Sensor Data (Products)

Archive Data

Network

Archive

Publishers

Live

Publishers

Sensor

System

Sensor

System

Sensor

System

Live publisher

TML

TML TML TML

TML

TML TML

Common modeling

for any sensor

enabled by TML

Data Discovery

enabled by TML

Pulg-n-play

enabled 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 Schema

• TransducerML

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TML

Proprietary

Best at the Sensor

OK downstream

Information Models and Schema

• TransducerML: Where?

Ref. [8]

Computer

Transducer

TML

S/W

Transducer System

TML

S/W

Computer

Transducer

Computer

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3

6

9

12

System Clock

Sensor

Processor

TML Formatter

TML

Description

Document

.xml

010011

Information Models and Schema

• TransducerML: How it works?

Sensor

Id=01

System

Ref. [8]

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<tml>

<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>

<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 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 >