Semantic%20Web%20Services%20for%20Smart%20Devices%20based%20on%20Mobile%20Agents

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Semantic Web Servicesfor Smart Devices based on
Mobile Agents
Expert
Resource
Service

• Vagan Terziyan
• Industrial Ontologies Group
• University of Jyväskylä

http//www.cs.jyu.fi/ai/OntoGroup/index.html
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Content

• Resources in Semantic Web and Beyond
• Global Understanding Environment
• Resource Adaptation
• Remote Diagnostics of Resources
• Resource Maintenance and Networking
• Mobile Service Components (Agents)

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Semantic Web in Networked BusinessEnvironment
Networked Business Environment requires new
advanced ways of data and knowledge
management Industrial Maintenance domain is a
good application case for the concept of the
Networked Business Environment Networked
Maintenance Environment will bring all benefits
of the knowledge management, delivering
value-added services and integration of businesses
In a networked business environment Metso will
be a business hub controlling the flow of
information in the network of installed Metso
devices and solutions, and Metsos customers and
partners.
Semantic Web technology provides standards for
metadata and ontology development such as
semantic annotations (Resource Description
Framework) and knowledge representation (Web
Ontology Language). It facilitates
interoperability of heterogeneous components,
authoring reusable data and intelligent,
automated processing of data. Semantic Web is
an enabling technology for the future Networked
Business Environment
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MAIN RESEARCH OBJECTIVE

• to combine the emerging Semantic Web, Web
  Services, Peer-to-Peer, Machine Learning and
  Agent technologies for the development of a
  global and smart maintenance management
  environment, to provide Web-based support for the
  predictive maintenance of industrial devices by
  utilizing heterogeneous and interoperable Web
  resources, services and human experts

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Industrial Resources

• Classes of resources in maintenance systems
• Devices - increasingly complex machines,
  equipment, etc., that require costs-demanding
  support
• Processing Units (Services) embedded, local and
  remote systems, for automated intelligent
  monitoring, diagnostics and control over devices
• Humans (Experts) qualified users of the system,
  operators, maintenance experts, a limited
  resource that should be reused

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Smart Maintenance Environment
Experts
Devices with on-line data
exchange
data
Maintenance
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Self-maintenance

• Do not expect that someone cares about you, take
  care yourself even if you are just an industrial
  device !
• You should be proactive enough to realize that
  you exist and want to be in a good shape
• You should be sensitive enough to feel your own
  state and condition
• You should be smart enough to understand that
  you need some maintenance.

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Resource Agents
1. I feel bad, temperature 40, pain in stomach,
Who can advise what to do ?
3. Hey, I have some pills for you
2. Yeah, your condition is not good. You need
urgent help
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Research Challenges

• Resource Adaptation and Interoperability
  (Semantic Web)
• Unify data representation for heterogeneous
  environment
• Provide basis for communication
• Resource Proactivity and Mobility (Agent
  Technology)
• Design of framework for delivering
  self-maintained resources to industrial systems
• Resource Interaction (Peer-to-Peer, Web Services
  technologies)
• Design of goal-driven co-operating resources
• Resource-to-Resource communication models in
  distributed environment (in the context of
  industrial maintenance)
• Design of communication infrastructure

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GUN Concept
Global Understanding eNvironment
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RESOURCE ADAPTATION

• First Slice of Gun Architecture

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Goals
Define Semantic Web-based framework for
unification of maintenance data and
interoperability in maintenance system

• Research and Development
• Resource State/Condition Description Framework
  (RSCDF) based on Semantic Web and extension of
  RDF (Resource Description Framework)
• RSCDF adapters (wrappers)
• for devices, services and experts
• - browsable devices
• - application-expert interface
• - RSCDF-enabled services

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Generic Semantic Adapter
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Generic Semantic Adapter
Semantic wrapping of resource actions
translation of external messages into
resource-native formats
Generic Adapter
configuration
Semantic Layer
Resource-specific messaging
Messaging Layer
Communication-specific connector of a resource
Connectivity Layer
GUN environment
GUN-resource
The integration requires development of the
Generic Resource Adapter, which will provide
basic tools for adaptation of the resource to
Semantic Environment. It should have open modular
architecture, extendable for support of variety
low- and high-level protocols of the resources
and semantic translation modules specific for
every resource (e.g. human, device, database).
Generic Resource Adapter must be configurable
for individual resource. Configuration includes
setting up of communication specific parameters,
choosing messaging mechanism, establishing
messaging rules for the resource and providing a
semantic description of the resource interface.
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Semantic adapter for Devices
If to consider field devices as data sources,
then information to be annotated is data from
sensors, control parameters and other data that
presents relevant state of the device for the
maintenance process. Special piece of
device-specific software (Semantic Adapter) is
used for translation of raw diagnostic data into
standardized maintenance data based on shared
ontology.
Adapter
Shared ontology
Device-specific calls
Semantic message
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Semantic adapters for Services
The purpose of Service Semantic Adapter is to
make service component semantic web enabled,
allowing communication with service on semantic
level regardless of the incompatibility on
protocol levels, both low-level (data
communication protocol) and high-level (messaging
rules, message syntax, data encoding, etc.).
Adapter
Shared ontology
Service-specific calls
Semantic message
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Semantic Adapters for Human-experts
Human in the system is an initiator and
coordinator of the resource maintenance process.
The significant challenge is development of
effective and handy tools for human interaction
with Semantic Web-based environment. Human will
interact with the environment via special
communication and semantic adapter.
GUN-resource
Semantic message that will be visualized
Shared ontology
Action translated into semantic message
User interface
Human
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REMOTE DIAGNOSTICS

• Second Slice of Gun Architecture

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Goals
Development of agent-based resource management
framework and enabling meaningful resource
interaction

• Adding agents to resources
• Making resource proactive
• Enabling communication
• with resource
• Implementation of agent-communication scenarios
• service learning
• remote diagnostics

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Device Expert interactions
Expert

• Accepts semantic description of device state and
  can respond with classification label (semantic
  description of diagnosis)
• Can make semantic query to request device-state
  data (also labeled history data), get response
  from Device and provide own label for observed
  device state

Device
History data
Expert
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Device Service, learning
Device
History data
Service
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Device Service, servicing
Device
History data
Service
Diagnostic model
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System structure
Expert
Simple remote diagnostic model with
semantic-based communication, expert and
diagnostic service with learning capabilities.
Labelled data
Watching and querying diagnostic data
Querying diagnostic results
Device
Service
Labelled data
History data
Querying data for learning
Learning sample and Querying diagnostic results
Diagnostic model
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MAINTENANCE NETWORKING

• Third Slice of Gun Architecture

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Goals
Development of networked maintenance environment

• P2P agent-communication system
• Resource Discovery
• Maintenance Data Knowledge Integration
• Certification and credibility assessment of
  services
• Resource Goal/Behaviour Description Framework
• Semantic modelling of a resource proactive
  behaviour
• Exchanging integrating models of resource
  (maintenance) behaviour

GB
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Networking
Expert
Device
Service
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P2P networking
- network of hubs
- highly scalable
- fault-tolerable

• supports dynamic changes
• of network structure

• does not need
• administration

• Why to interact?
• Resource summarizes opinions from multiple
  services
• Services learns from multiple teachers
• One service for multiple similar clients
• Resources exchange lists of services
• Services exchange lists of clients.

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Notice boards
Component advertisement solution
Client 3
Client 2
Allows search for new partners
Source of new entry points into P2P network
Client 1
Allows automated search based on semantic
profiles
Service 3
Service 1
Service 2
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Discovery sample scenario

• Number of queried peers is restricted due to
• superhub based structure
• query forwarding mechanism based on
• analysis of semantic profile

Resource
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Devices multiple services
Device will support service composition in form
of ensembles using own models of service quality
estimation. Service composition is made with goal
of increasing diagnostic performance.
Device
Labelled data
Service
Service
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Services multiple devices
Service
Diagnostic model
1
Diagnostic model
n
Device
Device
Device
Labelled data
Device
Device
Device
Labelled data
Labelled data
Labelled data
Labelled data
Labelled data
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Results of Networking

• Decentralized environment that integrates
• many devices,
• many services,
• many human experts
• and supports

Establishment of new peer-to-peer links through
NoticeBoards, advertisement mechanism
Exchange of contact lists between neigbor peers
Semantic based discovery of necessary network
components
Interaction One service many devices
Interaction One device many services
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Device-to-Device opinion exchange
Device will be able to derive service quality
estimates basing on analysis of opinions of
other devices and trust to them.
Service 1
Service 2
Service quality evaluations
?
Device
?
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trust 100
Device 1
trust 2
1
Device 2
4
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Service-to- Service model exchange and
integration
Diagnostic models integration entails creation of
a more complex model extension or a service with
new diagnostic model
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Certification
Sure, there are security threats as in any
open environment. Security is to be ensured using
existing solutions for Internet environment.
Existence of certification authorities is
required in the network. Certificates gained by
services and trust to the certificate issuer are
factors that influence optimal service selection.
The quality of service is evaluated by users as
well.
Service 1
Service 2
Service 3
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3
4
Device
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1
trust
2
Own evaluations
Certifying party
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Maintenance executive services
Support for maintenance services that can
influence on device state and perform
maintenance actions upon it (automated control
system, maintenance personnel). They complete the
minimal working set of maintenance system
components.
Service
diagnosis
data
Control
Device
control
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Maintenance Networking Environment
Semantic Web environment
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Internal and External Service Platforms
Maintenance Platform Environment to run
Maintenance Services, contains a set of
expert-agents both in maintenance and
diagnostics. Agents are service components

• Service Platform
• Environment where service components perform
• Condition monitoring
• Maintenance activities

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Mobility of Service Components
Embedded Platform
Based on the online diagnostics, a service agent,
selected for the specific emergency situation,
moves to the embedded platform to help the host
agent to manage it and to carry out the
predictive maintenance activities
Host Agent
Maintenance Service
Service Agents
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Conclusion Summary of Concepts and Requirements
Adaptation of resources (devices, services,
experts) to the Environment
Support for services that are able to learrn
Discovery of necessary network components using
their profiles
GB
Interaction One device many services
Proactive and Mobile Resources