Transcript Linked-data and the Internet of Things

Linked-data and the Internet of
Things
Payam Barnaghi
Centre for Communication Systems
Research
University of Surrey
March 2012
Future Internet
• Extension
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More nodes, more connections
Any TIME, Any PLACE, Any THING
M2M, IoT
Millions of interconnected devices
• Expansion
– Higher bandwidth
– Spectrum optimisation
• Enhancement
– Smart networks
– Data centric and Content Oriented Networking
– Context-aware networking
(Future) Web
• Early generation Web focused on
Presentation.
– HTML (rendering the pages)
– Dynamic pages (often database to html transformation)
– Non-structured
• Semantic Web
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Structured data
Semantic annotation
Machine interpretable
Reasoning and AI enhancements
• Web of Data
– Interconnecting data resources
– Semantic data (i.e. RDF) linked to other data
– Large interconnected data sets
Future Internet and Future Web
• More Data centric
– Data as
• Content
• Context
• Service oriented developments, Cloud infrastructure
• More resources, more nodes, more constraints on traffic,
energy efficiency, heterogeneity,…
• Issues:
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Interoperability
Trust, Privacy and Security
Resource discovery
Automated processes
Autonomous communications
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Current Status
• The current data communications often rely on binary or syntactic
data models which lack of providing machine interpretable
meanings to the data.
– Binary representation or in some cases XML-based data
– Often no general agreement in annotating the data
• Requires an pre-agreement on communication parties to be
able to process and interpret the data
– Limited reasoning based on the content and context of the
node or communication
– Limited interoperability in data level
– Data integration and fusion issues
Challenges
• Numbers of devices and different users and interactions required.
– Challenge: Scalability
• Heterogeneity of enabling devices and platforms
– Challenge: Interoperability
• Low power sensors, wireless transceivers, communication, and
networking for M2M
– Challenge: Efficiency in communications
• Huge volumes of data emerging from the physical world, M2M and
new communications
– Challenge: Processing and mining the data, Providing secure access
and preserving and controlling privacy.
• Timeliness of data
– Challenge: Freshness of the data and supporting temporal
requirements in accessing the data
• Ubiquity
– Challenge: addressing mobility, ad-hoc access and service continuity
• Global access and discovery
– Challenge: Naming, Resolution and discovery
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What is expected in service/application
level?
• Unified access to data
– unified descriptions and at the same time an open frameworks
• Deriving additional knowledge (data mining)
• Reasoning support and association to other entities and resources
• Self-descriptive data an re-usable knowledge
• In general: Large-scale platforms to support discovery and access
to the resources, to enable autonomous interactions with the
resources, to provide self-descriptive data and association
mechanisms to reason the emerging data and to integrate it into
the existing applications and services.
Using semantically enriched data
• The core technological building blocks are now in place and
(widely) available: ontology languages, resource description
frameworks, flexible storage and querying facilities, reasoning
engines, etc.
• There are existing standards such as those provided by OGC
and W3C’s SSN Ontology.
• However, often there is no direct association to the domain
knowledge
– What a sensor measures, where it is, etc.
– Association of an observation and/or measurement data to a
feature of interest.
– We often need : to have access to domain knowledge and
relate semantically enriched descriptions to other entities
and/or existing data (on the Web).
The role of metadata
• Semantic tagging and machine-interpretable descriptions
• Re-usable ontologies (interoperable data and knowledge
sharing)
• Resource description frameworks
– Semantic models to describe sensors, nodes, content, etc.
• Structured data, structured query
• Using metadata and semantic annotation solves some of
the problems; however, interconnected and linked
metadata is better than stand-alone metadata!
How to create linked-data?
• The principles in designing the linked data are defined as:
– using URI’s as names for things;
• Everything is addressed using unique URI’s.
– using HTTP URI’s to enable people to look up those names;
• All the URI’s are accessible via HTTP interfaces.
– provide useful RDF information related to URI’s that are looked
up by machine or people;
• The URI’s refer to “objects” that are described by machineinterpretable data.
– including RDF statements that link to other URI’s to enable
discovery of other related concepts of the Web of Data;
• The URI’s are linked to other URI’s.
Linked data contributions to M2M and
information communication
- Using URI’s as names for things;
- URI’s for naming M2M resources and data (and also streaming
data);
- Using HTTP URI’s to enable people to look up those names;
- Web-level access to low level sensor data and real world
resource descriptions (gateway and middleware solutions);
- Providing useful RDF information related to URI’s that are
looked up by machine or people;
- publishing semantically enriched resource and data
descriptions in the form of linked RDF data;
- Including RDF statements that link to other URI’s to enable
discovery of other related things of the web of data;
- linking and associating the real world data to the existing data
on the Web;
Linked-data to support data interoperability
OSI/OSI Model and envisioned Linked
Data Interoperability Layer
Source: Stefan Decker (DERI NUI Galway, Ireland) , http://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.png
Linked-data for…
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Web data, network, and application data
(Web) Services and service platforms
IoT and THING descriptions
Resource descriptions
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Network resources
Entities of Interests/Resource/Service
Content
Context
• This will enable
– Intelligent decision making
• Network communications
• Information networking
Linked-data for… (cont’d)
- However, it still is a form of Knowledge and Data
Engineering;
- We still need more intelligent systems, reasoning
mechanisms, and effective information
processing and decision making mechanisms to
support M2M and Future Internet data
communications.
- It helps AI methods, but does not replace
them…
Payam Barnaghi
Centre for Communication Systems Research
Faculty of Engineering and Physical Sciences
University of Surrey
Guildford, UK
Email: [email protected]
http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/payam-foaf.rdf