Linked-data and the Internet of Things
description
Transcript of Linked-data and the Internet of Things
Linked-data and the Internet of Things
Payam BarnaghiCentre for Communication Systems
ResearchUniversity of Surrey
March 2012
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
(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
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– …
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
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 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.
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
Source: Stefan Decker (DERI NUI Galway, Ireland) , http://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.png
OSI/OSI Model and envisioned Linked Data Interoperability Layer
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
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 ResearchFaculty of Engineering and Physical SciencesUniversity of SurreyGuildford, UKEmail: [email protected]
http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/payam-foaf.rdf