This document discusses semantic sensor service networks and proposes an approach using semantic technologies. It presents a layered model with modules for sensors, observations, systems and services. Existing ontology models are reviewed and a lightweight ontology for IoT services is proposed, focusing on modularity, compatibility and efficiency. Linked data principles are leveraged for sensor discovery. A demonstrator is presented to show sensor discovery using semantic descriptions and linked sensor data. The work aims to address key issues of sensor service connectivity, discovery and composition in semantic sensor service networks.

1. Semantic Sensor Service Networks
Wei Wang, Payam Barnaghi, Gilbert Cassar, Frieder Ganz, Pirabakaran
Navaratnam
Centre for Communication Systems Research
University of Surrey
Guildford, Surrey
United Kingdom
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2. Sensors, Sensor Networks and Internet of
“Things”
 Physical world objects
 e.g. A room, a car, A person;
 Feature of Interest
 e.g. Temperature of the room, Location of the car,
heart-rate of the person;
 Sensors
 e.g. Temperature sensor, GPS, pulse sensor
 Embedded devices
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3. Semantics and Sensor Networks
Image credits:
[1] Protocols and Architectures for Wireless Sensor Networks, Holger Karl, Andreas Willig, Wiley, 2005 3
[2] Cisco - Interne of Things

4. Distributed WSN
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5. The Internet of Things
 A primary goal of interconnecting devices and
collecting/processing data from them is to create
situation awareness and enable applications,
machines, and human users to better understand their
surrounding environments.
 The understanding of a situation, or context,
potentially enables services and applications to make
intelligent decisions and to respond to the dynamics of
their environments.
 A key enabler is providing Services that represent
sensors/resources and integrating them into the
cyber-space.
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6. Semantics, sensors and services
 Semantics are machine-interpretable metadata (for mark-up),
logical inference mechanisms, query mechanism, linked data
solutions
 For semantic sensor services this means:
 ontologies for: devices (e.g. sensors), observation and
measurement data (e.g. sensor readings), domain concepts (e.g.
unit of measurement, location), service descriptions (e.g. IoT
services) and other data sources (e.g. those available on linked
open data)
 Semantic annotation should also supports data represented
using existing forms
 Reasoning /processing to infer relationships or hierarchies
between different resources, data
 Semantics (/ontologies) as meta-data (to describe the
services/resources) / knowledge bases (domain knowledge).
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7. A layered model
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8. Existing models for resources and data
 W3C Semantic Sensor Network Incubator
Group’s S N ontology (mainly for sensors and
S
sensor networks, observation and
measurement, and platforms and systems)
 Quantity Kinds and Units
 Used together with the SSN ontology
 based on QUDV model OMG SysML(TM)
 Working group of the SysML 1.2 Revision Task
Force (RTF) and W3C Semantic Sensor Network
Incubator Group
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9. SSN Ontology Modules
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10. Existing models for services
 OWL-S and WSMO are heavy weight models: practical
use?
 Minimal service model
 Deprecated
 Procedure-Oriented Service Model (POSM) and Resource-
Oriented Service Model (ROSM): two different models for
different service technologies
 Defines Operations and Messages
 No profile, no grounding
 SAWSDL: mixture of XML, XML schema, RDF and OWL
 hRESTS and SA-REST: mixture of HTML and reference
to a semantic model; sensor services are not anticipated
to have HTML
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11. Semantic modelling
 Lightweight: experiences show that a lightweight
ontology model that well balances expressiveness and
inference complexity is more likely to be widely adopted
and reused; also large number of IoT resources and
huge amount of data need efficient processing
 Compatibility: an ontology needs to be consistent with
those well designed, existing ontologies to ensure
compatibility wherever possible.
 Modularity: modular approach to facilitate ontology
evolution, extension and integration with external
ontologies.
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12. IoT.est service profile highlight
 ServiceType class represents the service technologies:
RESTful and SOAP/WSDL services.
 serviceQos and serviceQoI are defined as subproperty of
serviceParameter; they link to concepts in the QoS/QoI
ontology.
 serviceArea: the area where the service is provided;
different from the sensor observation area
 Links to the IoT resources through “exposedB property
y”
 Future extension:
 serviceNetwork, servicePlatform and serviceDeployment
 Service lifecycle, SLA…
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13. A snapshot of the model
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14. Service Search and Discovery
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15. Linked data principles
 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;
 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.
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16. Linked data layer for not only IoT…
Diagram from Stefan Decker, http://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.png; linked data diagram: http://richard.cyganiak.de/2007/10/lod/
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17. A Sample demonstrator
http://ccsriottb3.ee.surrey.ac.uk:8080/IOTA/
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18. Sensor discovery using linked sensor
data
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19. Conclusions
 Sensor service connectivity, discovery and
composition are some of the most key issues
in semantic sensor service networks.
 SOA based design can support seamless
integration to existing applications on cyber-
space.
 While the direct access method uses the
standard HTTP protocols for service
communications, the intermediate access
method is designed on the top of the
Constrained Application Protocol (CoAp) and
6LowPan for devices operating in constrained
environments. 19

20. Questions?
 Thank you.
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