This document discusses semantic interoperability challenges for Internet of Things (IoT) services and resources. It proposes using semantic models and metadata tagging to provide structured, machine-interpretable representations of IoT concepts like resources, entities, and services. This would allow automated processing and integration of diverse IoT data and services. However, challenges remain in aligning different models and achieving real interoperability across frameworks. Practical solutions may involve linking related models and proposing common abstract descriptions.

1. Semantic Interoperability
for IoT Services and Resources
Klaus Moessner
Centre for Communication Systems Research
University of Surrey
October 2011

2. Internet of Things
• Internet of Things (IoT) vision:
– large number of physical world objects
interacting with each other and the ambient
environment
– data/services from these objects to provide
‘real world’ services
• provide information
• allow interaction

3. Requirements
• structured representation of identified IoT
concepts
• homogeneous access mechanism to
heterogeneous objects with diverse
capabilities
• automated machine interpretability of the
possible interactions and horizontal
integration with existing applications

4. How to model Things?
• resource model
– Gateway, sensors, processing resources
• entity model
– Physical world objects
• Features of interest for each entity
• service model
– IoT services and interfaces

5. IoT Services
• services are provided by a plethora of
heterogeneous objects that are often directly
related to the physical world
• data and/or functionalities offered by such
services provide information about the
physical world and allow interaction with it

6. Data and services in IoT ‐ Challenges
• large‐scale networks, huge volumes of data, dynamic and
sometimes unreliable data sources
• dynamicity, transient data and subject to quality changes
• scalability of the solutions
• express‐ability and extensibility of semantics and meta‐data
• heterogeneity ‐ more devices are contented, more diversity
• more autonomous processes (integration, aggregation,
filtering, ...) are required
• management of the resources

7. Role of metadata
• semantic tagging
• machine‐interpretable data annotation and
resource descriptions
• re‐usable ontologies
• resource description framework(s)
• structured data, structured query

8. In IoT semantic models are
useful for:
• description of various concepts and attributes in the
IoT framework
• providing machine interpretable and structured
representation of resources/entities/services and
domain knowledge
• supporting automated processing and decision
making over various interactions and integration
with existing and new applications

9. Metadata and Semantics
• to describe:
– Content
– Context
– Resources
– Entities and features of interest
• to create:
– Perception
– Context and situation awareness
• to support:
– Automated processes for management of resources and decision
making

10. Semantic interoperability for
• resource description
– SENSEI resource description, IoT‐A resource model, W3C
SSN Ontology,…
• entity description and domain knowledge
– SENSEI entity description, IoT‐A entity model, linked data
models and linked open data fro domain knowledge,…
• IoT services
– In IoT.est we plan to use semantic web services (extension
to OWL‐S) that we have developed in the context of the
IoT‐A project.

11. 11
W3C Incubator Group, SSN Ontology
makes observations
of this type
where it is
What it
measures
units
SSN-XG ontologies
SSN-XG annotations

12. Service model
Entity Model Ontology
W3C SSN OntologyResource Model Ontology
OWL‐S
Service Profile
Ontology
P(em:DomainAttribute)
OWL‐S Service Grounding Ontology
Service Model Ontology
hasInput
profile:Profile
iot:ResourceAccessAtomicProcessGrounding
em:DomainAttribute
em:DomainAttribute
P(em:DomainAttribute)
hasEffect
hasPrecondition
hasOutput
Service
presents
supports
rm:AccessInterface
hasAccessInterface
ssn:Property ssn:Property ssn:Condition ssn:Condition
hasInputType hasOutputType
hasPreconditionTypehasEffectType
iot:ObservationArea
iot:ObservationSchedule
hasObservationArea
hasObservationSchedule
hasServiceType
ServiceType
OWL‐S USDL
WSML xxx
<<instanceOf>>

13. Service instance: an example
Entity Model Ontology
W3C SSN Ontology
Resource Model Ontology
OWL‐S Service Profile Ontology
OWL‐S Service Grounding Ontology
Service Model Ontology
U38_TempSensorService_Profile
U38_TempSensorServiceProcessGrounding
RoomU38.hasA (AmbientTempAttribute)
hasOutput
U38_TempSensor_Service
presents
supports
AccessInterface_U38_temp_sensor
hasAccessInterface
U38_TempSensorService_Property
hasOutputType
U38_ObservationArea
U38_ObservationSchedule
hasObservationArea hasObservationSchedule
hasServiceTyp
e
OWL‐S
SENSEI Observation and Measurement Ontology
Temperature
subClassOf

14. What are the challenges?
• the models provide the basic description
frameworks, but alignment between different
models and frameworks are required.
• semantics are the starting point, reasoning and
interpretation of data is required for automated
processes.
• real interoperability happens when data/services
from different frameworks and providers can be
interchanged and used with minimised intervention.

15. What are the practical steps?
• linked data approach is a promising way for
integrating data from different sources and
interlinking semantic descriptions
• alignment between different description
models for IoT Services/Resources/Entities
• proposing reference and abstract models for
semantic descriptions in IoT (e.g. similar to
the W3C SSN approach)

16. References and links
• Suparna De, et al, Service modelling for the Internet
of Things
– http://www.iot‐a.eu/public/public‐documents/documents‐1/1/2/service‐
modelling‐for‐the‐internet‐of‐things/at_download/file
• W3C SSN
– http://www.w3.org/2005/Incubator/ssn/XGR‐ssn‐20110628/
• Claudia Villalonga, A Resource Model for the Real
World Internet
– http://www.springerlink.com/content/ejxm56331g8w7rk2/fulltext.pdf

17. Thank you!