The biggest challenge for the IoT is semantic interoperability. The solution is to abstract metadata from sensors and devices by creating an IoT knowledge system.

1. Making Sure
IoT Devices
Understand Each Other!
ConnectedWorld 2018
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2. 40%
Share of value
enabled
by interoperability.
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The Internet of Things: mapping the value beyond the hype
June 2015

3. Interoperability
The ability of
machines, devices, sensors,
and people
to connect and communicate
with each other
via the Internet of Things.
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4. 4
Cross-industry semantic interoperability,
Embedded Computing Design June 29, 2017
Open System
Interconnection
OSI
Levels of Conceptual
Interoperability Model
LCIM
Industrial Internet
Connectivity Framework
IICF
 Exchange raw data (bits, frames, packets, messages).
 Well understood and standardized.
 Agnostic.
 Exchange information (data in context).
 Metadata ontology to interpret meaning .
 Standardization attempts (OMG, IPSO, OCF, etc.)
too narrow for cross-industry.
 Domain expertise required.
 Exchange structured data (states, events, streams).
 Standard formats (XML, JSON, etc.).
 Core connectivity standards (DDS, OPC-UA, etc.).
 Cross-industry via standardized gateways.
Network Layer
Transport Layer
Data Link Layer
Physical Layer
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5
6
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Link
Net
work
Transport
Physical
Technical
Interoperability
Application Layer
Session Layer
Presentation Layer
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2
3
Semantic
Interoperability
Framework
Information
Syntactic
Interoperability

5. Data Semantics
77.6?
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What is the data type of sensor point?
What is the unit of measure?
What is measured?
When? And where is sensor „zn3-wwfl4“?
Cross-industry semantic interoperability,
Embedded Computing Design June 29, 2017
Device data inconsistent, non-standard naming conventions, limited descriptors!

6. Normalization?
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Cross-industry semantic interoperability,
Embedded Computing Design June 29, 2017
× Standardizing point identifiers
impossible.
× Too much metadata.
× Not extensible. Not changeable.
 Decouple point identifiers from
metadata by association.
 Use domain expertise to build
knowledge base.
 Consistent metadata terminology.
 Use metadata model for mapping.
Normalization? – Metadata Abstraction!

7. IoT Knowledge System
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Controlled Vocabulary
Well-defined meaning.
National language support.
Consistency via registration
authority.
Ontology
Classification of concepts.
Associated with metadata terms.
Semantic relationships.
Common Business Ontology
Core Standard.
Top-level for cross-industry
interoperability.
Trusted Application Ontology
Domain knowledge for lower levels.
Created by subject matter experts.
Links classification systems.
Maps between metadata terms.

8. IoT Knowledge System
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Controlled Vocabulary
Well-defined meaning.
National language support.
Consistency via registration
authority.
Ontology
Classification of concepts.
Associated with metadata terms.
Semantic relationships.
Common Business Ontology
Core Standard.
Top-level for cross-industry
interoperability.
Trusted Application Ontology
Domain knowledge for lower levels.
Created by subject matter experts.
Links classification systems.
Maps between metadata terms.

9. Coreon in the IoT Connectivity Architecture
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Connectivity Core Standards
Corporate
Data
Users
3rd Parties
Endpoints
Semantic
Gateway

10. Solution &
Implementation
Coreon SaaS
Secure
High Availability
Hosted in Germany
API
RESTful Web API
 JSON
URIs
Services
Consulting
Ontology building
Classification mapping
Multilingual
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11. Benefits
100 % IoT value
Cross-industry
interoperable, linked, worldwide
Future-proof
extensible, changeable, mapable
Controlled vocabulary
consistent, defined, global
Corporate knowledge
captured, maintained, trusted
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12. Contact
Rungestr. 20 ‐ 10179 Berlin
www.coreon.com
@CoreonApp
michael@coreon.com
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