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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 More nodes, more connections Any TIME, Any PLACE, Any THING M2M, IoT Millions of interconnected devices Expansion

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Linked-data and the Internet of Things

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  1. Linked-data and the Internet of Things Payam Barnaghi Centre for Communication Systems Research University of Surrey March 2012

  2. Future Internet • Extension • 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

  3. (Future) Web • Early generation Web focused on Presentation. • HTML (rendering the pages) • Dynamic pages (often database to html transformation) • Non-structured • Semantic Web • 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

  4. 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: • Interoperability • Trust, Privacy and Security • Resource discovery • Automated processes • Autonomous communications • …

  5. 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

  6. 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 6

  7. 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.

  8. 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).

  9. 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!

  10. 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 machine-interpretable 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.

  11. 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;

  12. 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

  13. Linked-data for… • Web data, network, and application data • (Web) Services and service platforms • IoT and THING descriptions • Resource descriptions • Network resources • Entities of Interests/Resource/Service • Content • Context • This will enable • Intelligent decision making • Network communications • Information networking

  14. Linked-data for… (cont’d) • However, it still is a form ofKnowledge and Data Engineering; • We still need moreintelligent systems, reasoning mechanisms, and effectiveinformation processing and decision making mechanisms to support M2M and Future Internet data communications. • It helps AI methods, but does not replace them…

  15. Payam Barnaghi Centre for Communication Systems Research Faculty of Engineering and Physical Sciences University of Surrey Guildford, UKEmail: p.barnaghi@surrey.ac.uk http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/payam-foaf.rdf

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