Semantic Pervasive Computing:
state-of-the-art approaches
Computer Engineering Department,
Sharif University of Technology.
Behrad Zari (zari@ce.sharif.edu)
Semantic Pervasive Computing, Distributed Systems Course, June 2006
1
Outline
•
Pervasive Computing
–
•
Pervasive Computing Paradigm
–
–
•
Architecture
An Example
Semantic Pervasive Spaces
–
•
•
Introduction
Benefits
Context Modeling Ontologies
Semantic Web Services
–
–
•
Pervasive Services
Semantics of Pervasive Services
Semantic Web Technology
–
–
•
•
Characteristics of Interest
Triple Space Computing
Conclusion & Open Questions
References
Semantic Pervasive Computing, Distributed Systems Course, June 2006
2
Pervasive Computing Characteristics
• Discovery and Matchmaking
• Inter-operability between
different entities
• Context-awareness
Semantic Pervasive Computing, Distributed Systems Course, June 2006
3
Discovery and Matchmaking
• Discovery:
– Pervasive Computing Environments
have one or more registries to keep a
real time state of the system, i.e., the
entities currently present and
available.
– In the Discovery Service, standard
schemas are needed to describe
many kinds of entities, including
people, places, and things.
• Matchmaking:
– what sets or combinations meet
certain criteria, i.e., the requirements
and preferences of the parties.
Semantic Pervasive Computing, Distributed Systems Course, June 2006
4
Smart Devices
Semantic Pervasive Computing, Distributed Systems Course, June 2006
5
Inter-operability
– involve dozens, if not hundreds of
devices (sensors, external input
and output devices, remotely
controlled appliances, etc.)
– Multiple inhomogeneous networks
• Short range: IrDA, Bluetooth,
Wireless LAN
• Wide area: (HS)CSD, GPRS,
UMTS
• Often also no network
– Interoperability Nightmare
– Proliferation of devices that need
to be connected.
Semantic Pervasive Computing, Distributed Systems Course, June 2006
6
Inter-operability (cont’d)
• Accomplish discovery and
configuration of new devices without
“a human in the loop”.
• Automatic formation of device
coalitions.
• Qualitatively stronger means of
representing service semantics are
required.
• Heterogeneity and Autonomy:
– Machine process-able descriptions
Semantic Pervasive Computing, Distributed Systems Course, June 2006
7
Context-Awareness
• Applications in pervasive and mobile
environments need to be contextaware so that they can adapt
themselves to rapidly changing
situations.
• Different kinds of contexts (such as
location of people, activities of
individuals or groups, weather
information, etc.).
• The various types of contextual
information that can be used in the
environment must be well-defined so
that different entities have a common
understanding of context.
Semantic Pervasive Computing, Distributed Systems Course, June 2006
8
Ubiquitous Computing Paradigm
What’d be the magic?
Semantic Pervasive Computing, Distributed Systems Course, June 2006
9
Not A dream…
• The right service
• At the right place
• At the right time
• At the right cost
Semantic Pervasive Computing, Distributed Systems Course, June 2006
10
A Solution
• Wide range of services:
–
–
–
–
–
–
Carrier Service
Ticket Reservation Service
Transport Service
Information Service
Routing Service
…
• We need Pervasive Services.
• But what is a Service?
Semantic Pervasive Computing, Distributed Systems Course, June 2006
11
Pervasive Services
• Independently developed and
deployed
• Life-Cycle Management
– Compare to Agent Systems
• Input/Output, Pre/PostConditions, Effects
• Profile for logic semantics of the
service.
• Discovery
– SLP, Jini, Bluetooth SDP, UDDI, GSD,
DNS-SD …
Semantic Pervasive Computing, Distributed Systems Course, June 2006
12
Semantics for Pervasive Services
•
Data/Information Semantics
–
–
–
What: Formal definition of data in input and output
messages of a service
Why: for discovery and interoperability
How: by annotating input/output data of services using
ontologies
•
Functional/Operational Semantics
•
Execution Semantics
–
–
–
–
–
•
Formally representing capabilities of service
for discovery and composition of Services
by annotating operations of Services as well as provide
preconditions and effects
Formally representing the execution or flow of a services in
a process or operations in a service
for analysis (verification), validation (simulation) and
execution (exception handling) of the process models
QoS Semantics
–
–
Formally describing operational metrics of a
service/process
To select the most suitable service to carry out an activity
in a process
Semantic Pervasive Computing, Distributed Systems Course, June 2006
13
Semantic Web Technology
How it relates to Pervasive Computing?
Semantic Pervasive Computing, Distributed Systems Course, June 2006
14
Introduction
• “The Semantic Web will bring structure to
the meaningful content of Web pages,
creating an environment where software
agents roaming from page to page can
readily carry out sophisticated tasks for
users.” [Berners-Lee 1998]
Semantic Pervasive Computing, Distributed Systems Course, June 2006
15
Key Benefits
• Serendipitous Interoperability: the
ability of software systems to discover
and utilize services they have not seen
before.
• With the Semantic Web approach it is
possible for agents to “learn” new
vocabularies and – via reasoning –
make meaningful use of them.
• Automated service discovery,
selection and composition.
• Has proof-of-application in MultiAgent software systems.
Semantic Pervasive Computing, Distributed Systems Course, June 2006
16
Ontology Benefits
• Enabling semantic discovery of entities.
• Allowing users to gain a better
understanding of the environment and
how different pieces relate to each other.
• Allowing both humans and automated
agents to perform searches on different
components easily.
•
Allowing both humans and automated agents
to interact with different entities easily.
•
Allowing both humans and automated agents
to specify rules for context-sensitive behavior of
different entities easily.
•
Enabling new entities (which follow different
Ontologies) to interact with the system easily.
Semantic Pervasive Computing, Distributed Systems Course, June 2006
17
Context Modeling Ontologies
Some Proposed Ontologies
Semantic Pervasive Computing, Distributed Systems Course, June 2006
18
CoOL ASC Model
Semantic Pervasive Computing, Distributed Systems Course, June 2006
19
CoOL Ontology
Semantic Pervasive Computing, Distributed Systems Course, June 2006
20
GAIA Ontology
• Converts physical spaces and the devices
they contain into a programmable
computing system.
• Multiple Ontologies has used to augment
various system services:
–
–
–
–
–
Configuration management
Discovery and matchmaking
Human Interfaces
Interoperation of components
Context Sensitive behavior
• Implemented on DAML+OIL and CORBA
standards.
Semantic Pervasive Computing, Distributed Systems Course, June 2006
21
GAIA Ontology Infrastructure
Semantic Pervasive Computing, Distributed Systems Course, June 2006
22
CHIL Ontology
• A general-purpose core vocabulary
for the various concepts within a multisensor smart space.
Semantic Pervasive Computing, Distributed Systems Course, June 2006
23
SOUPA
Semantic Pervasive Computing, Distributed Systems Course, June 2006
24
CoBrA Ontology
Semantic Pervasive Computing, Distributed Systems Course, June 2006
25
CoBrA Architecture
Semantic Pervasive Computing, Distributed Systems Course, June 2006
26
SOCAM Ontology
Semantic Pervasive Computing, Distributed Systems Course, June 2006
27
SOCAM Architecture
Semantic Pervasive Computing, Distributed Systems Course, June 2006
28
An Extensible Ontology
• User
– Important properties include a
user’s profile, but also his
preferences, mood and current
activity.
• Environment
– the environment in which the user
interacts is an important aspect of
the context specification. It
consists of time and location
information, and environmental
conditions, such as temperature
and lighting.
Semantic Pervasive Computing, Distributed Systems Course, June 2006
29
An Extensible Ontology (2)
• Service
– provides specific functionality to
the user. Specifying semantic and
syntactic information sustains easy
service discovery and service
interaction.
• Platform
– hardware and software
description of a specific device.
Semantic Pervasive Computing, Distributed Systems Course, June 2006
30
Ontology Core Overview
Semantic Pervasive Computing, Distributed Systems Course, June 2006
31
User Ontology
Semantic Pervasive Computing, Distributed Systems Course, June 2006
32
Environment Ontology
Semantic Pervasive Computing, Distributed Systems Course, June 2006
33
Platform Ontology
Semantic Pervasive Computing, Distributed Systems Course, June 2006
34
Service Ontology
Semantic Pervasive Computing, Distributed Systems Course, June 2006
35
Semantic Web Services
A further step towards Pervasive Systems
needs
Semantic Pervasive Computing, Distributed Systems Course, June 2006
36
Architecture
Semantic Pervasive Computing, Distributed Systems Course, June 2006
37
<xsd:complexType name=“Date"> WSDL
<xsd:sequence>
<xsd:element name=“year" type="xsd:integer" />
<xsd:element name=“month" type="xsd:integer" />
<xsd:element name=“day" type="xsd:byte" />
= Time - Ontology
</xsd:sequence>
Temporal-Entity
</xsd:complexType>
Web Service
Time
Interval
Interfaces
Inputs
Year
Data
Semantics
XML Schema
Data type hierarchy
Time
Domain
Time-Point
{year, month, day}
Outputs
Name
Ontologies
Date
Time
Date
{hour, minute, second}
Event
Calendar-Date
Duration
{absolute_time}
{dayOftheWeek, monthOftheYear}
Scientific-Event
= Local ontology
City
{millisecond}
Coordinates {x, y}
Get Conference
Information
QoS
QoS Ontology
Ontology
Area {name}
City
QoS
Semantics
Forrest
Functional
Semantics
WSDL
Quality
Information Function
<portType name=“ConferenceInformation">
<operation name="getInformation">
<input message="tns:Data" />
<output message="tns:ConferenceInformation" />
</operation>
Min
Conference Information Functions
Get Information
Get Date
Semantic Pervasive Computing, Distributed Systems Course, June 2006
38
Task Computing Environment
• Fujitsu Laboratories of America and
the MINDSWAP research group at the
University of Maryland Implementation
of a smart conference room.
• Expose the functionality in rich
pervasive environments (device
functionality or third-party
functionality) as Semantic Web
services, which in turn the user can
discover and arbitrarily compose.
• STEER client.
Semantic Pervasive Computing, Distributed Systems Course, June 2006
39
Semantic Pervasive Spaces
Semantic Pervasive Computing, Distributed Systems Course, June 2006
40
Triple Space Computing
• Proposed By Digital Enterprise
Research Institute (DERI) at W3C
Workshop on the Ubiquitous Web,
Tokyo, 2005.
• Provides a web that is optimized for
machines, thus simplifying the sharing
of data and the coordination of
devices and services in dynamic and
heterogeneous systems.
• Services will have to understand each
other and exchange information over
the Ubiquitous Web to allow human
users access to the world's information
spaces whenever and wherever they
find themselves.
Semantic Pervasive Computing, Distributed Systems Course, June 2006
41
TSC Architecture
Semantic Pervasive Computing, Distributed Systems Course, June 2006
42
TSC Access Model
•
Write(URI ts, Graph triples):URI
•
Read(URI ts, Template template):Graph
•
Read(URI ts, URI graph):Graph
•
Take(URI ts, Template template):Graph
•
Take(URI ts, URI graph):Graph
–
–
–
–
–
Write an RDF graph containing one or more triples
to the Triple Space ts. returns: the URI that
identifies the written graph.
Read a set of triples matching the given template.
returns: the RDF triples that were matched by the
template.
Read the RDF graph identified by the provided
URI. returns: the RDF graph that is identified by the
URI.
Take (read and remove) one RDF graph
matching the given template. returns: one of the
graphs that have matching triples.
Take the RDF graph identified by the provided
URI. returns: the graph that is identified by the URI.
Semantic Pervasive Computing, Distributed Systems Course, June 2006
43
Open Questions
• Triple Spaces provides us with
– A web of devices
– Data and interaction heterogeneity,
– Decreased communication overhead
(no Context gathering overhead)
• A service language
– Like WSDL for Web Services
– DAML-S, OWL-S
• Triple spaces + OWL-S = Semantic
Pervasive Spaces?
Semantic Pervasive Computing, Distributed Systems Course, June 2006
44
Conclusion
• Semantic Web goals propose
potential cases of usages in pervasive
computing domain.
• Semantic Web is not covered our lives
as current Web does yet.
• We need a standardized solution
– As CORBA, EJB or others did to
Distributed Computing years ago.
– Some implementation should be
proven.
– How many standards and consortiums
should be gathered?
Semantic Pervasive Computing, Distributed Systems Course, June 2006
45
References
•
M. Satyanarayanan. Pervasive Computing: Vision and Challenges. School of
Computer Science Carnegie Mellon University, IEEE Personal
Communications, 2001.
•
Ora Lassila, Applying Semantic Web in Mobile and Ubiquitous Computing:
Will Policy-Awareness Help?, Nokia Research Center.
•
Ryusuke Masuoka and Yannis Labrou, Fujitsu Laboratories of America, Bijan
Parsia and Evren Sirin, MIND Lab, University of Maryland, Ontology-Enabled
Pervasive Computing Applications, IEEE INTELLIGENT SYSTEMS, 2003.
•
Jorge Cardoso, Amit Sheth, Semantic Web Processes, 4rd International
Conference on Web Information Systems Engineering, WISE 2003.
•
Heraklion, Crete, Ubiquitous Services Cluster - UbiServ, DERI Offsite, May 2005.
•
Reto Krummenacher, Thomas Strang, Dieter Fensel. TRIPLE SPACES FOR AN
UBIQUITOUS WEB OF SERVICES, Position Paper: W3C Workshop on the
Ubiquitous Web, Tokyo, Japan, March 9-10, 2005.
•
Ippokratis Pandis, et.al. An Ontology-based Framework for Dynamic
Resource Management in Ubiquitous Computing Environments, 2005.
•
Sheila A. McIlraith, Tran Cao Son, and Honglei Zeng, Semantic Web Services,
Stanford University.
•
Ovidiu Chira, The Semantic Web, IDIMS Report, February 2003.
Semantic Pervasive Computing, Distributed Systems Course, June 2006
46
References (2)
•
Davy Preuveneers, et.al. Towards an extensible context ontology for Ambient
Intelligence, 2004.
•
Tao Gu, et.al. An Ontology-based Context Model in Intelligent Environments,
2004.
•
Harry Chen, et.al. SOUPA: Standard Ontology for Ubiquitous and Pervasive
Applications.
•
Harry Chen, et. al. Using OWL in a Pervasive Computing Broker
•
Harry Chen, et.al. An Ontology for Context Aware Pervasive Computing
Environments.
Semantic Pervasive Computing, Distributed Systems Course, June 2006
47