=Paper=
{{Paper
|id=Vol-522/paper-2
|storemode=property
|title=A Survey of the Semantic Specification of Sensors
|pdfUrl=https://ceur-ws.org/Vol-522/p6.pdf
|volume=Vol-522
|dblpUrl=https://dblp.org/rec/conf/semweb/ComptonHNLS09
}}
==A Survey of the Semantic Specification of Sensors==
https://ceur-ws.org/Vol-522/p6.pdf
A Survey of the
Semantic Specification of Sensors
Michael Compton1 , Cory Henson2 , Laurent Lefort1 , Holger Neuhaus3 , and
Amit Sheth2
1
ICT Centre, CSIRO, Canberra
2
Kno.e.sis Center, Wright State University
3
Tasmanian ICT Centre, CSIRO, Hobart
firstname.lastname@csiro.au,
{cory,amit}@knoesis.org
Abstract. Semantic sensor networks use declarative descriptions of sen-
sors promote reuse and integration, and to help solve the difficulties of
installing, querying and maintaining complex, heterogeneous sensor net-
works. This paper reviews the state of the art for the semantic specifi-
cation of sensors, one of the fundamental technologies in the semantic
sensor network vision. Twelve sensor ontologies are reviewed and anal-
ysed for the range and expressive power of their concepts. The reasoning
and search technology developed in conjunction with these ontologies is
also reviewed, as is technology for annotating OGC standards with links
to ontologies. Sensor concepts that cannot be expressed accurately by
current sensor ontologies are also discussed.
1 Introduction
The Semantic Web promises a Web of annotated and linked data, a Web pop-
ulated by autonomous and semi-autonomous software agents, agents that inter-
pret, reason about and act on the annotations, links and data [12, 49]. Semantic
Web technologies have the potential to benefit domains where issues such as vol-
ume, complexity and heterogeneity can overcome traditional techniques. Sensor
networks are one such area where scale, complexity and the need to integrate
across heterogeneous standards, sensors and systems all indicate the applica-
tion of semantics. While the Open Geospatial Consortium’s (OGC) Sensor Web
Enablement (SWE) suite of standards provide a syntactic model for sensors,
issues such as integration and interpretation of information encoded using the
standards have not been resolved.
Sensors and Sensor Networks: Digital sensors have begun to pervade much
of the modern world: for example, phones, computers and fridges are now equipped
with various sensors, as are roadways, traffic lights, buildings and some otherwise
natural landscapes. Increasingly, sensor networks, that is, networks of connected
sensors and associated devices, are being used in such diverse applications as
environmental monitoring (for example, in ecological monitoring, agriculture,
Proc. Semantic Sensor Networks 2009, page 17
and wildfire and flood detection), security and surveillance (for example, in traf-
fic, building, city, and airport monitoring and anti-terrorism), and health (for
example, in-home monitoring).
Sugihara and Gupta’s [53] and Yick et al.’s [56] reviews demonstrate the
broad scope of sensor networks, the devices they can contain and how they are
programmed. Sensor networks, which are formed from communicating nodes (de-
vices with attached sensors), range from single-purpose sensing units through to
large networks of heterogeneous devices and, with associated services, may offer
live and historical data, analysis, interpretation and prediction. Sensors range
from single-feature sensors to more complicated systems, such as weather sta-
tions and satellites. The sensors may be powered or harvest power from their
environment and may internally, or in concert with other sensors, process, ag-
gregate and interpret observations. Generally, a network is organised such that
data flows from low-powered devices to higher-powered devices for further ag-
gregation and processing. The identifiable entity a sensor is attached to is called
a platform. Though each unit potentially collects and transmits a small amount
of data, sensor networks typically deal with large volumes of data.
Sensors are said to observe a physical quality (temperature, depth, etc.) of a
feature (a lake) and report observations (the term property is used for qualities
in SWE standards). Specifications of sensors’ responses to stimuli under various
conditions are called response models. Sensor refers to a range of instruments,
including transducers, sensor devices and computations: for example, wind chill,
calculated from wind speed and ambient temperature, could be sensed by an in
situ device or computed from co-located measurements. A sensor is defined as
a source that produces a value representing a quality of a feature. Sensors and
scientific or other computational models form a continuum of sensing that is not
easy to partition; there is some aspect of prediction or inference that is perhaps
stronger in a model, but is, in any case, still present in any transducer or sensing
device. Hence, sensor in this review refers to physical devices that measure and
computations that measure: though, much of the material reviewed does view
sensors as devices.
While standardisation solves some issues of device incompatibility, and there
are a number of standards for sensor networks [18], it is typically more successful
in removing interface heterogeneity than solving data and concept incompati-
bilities. The OGC’s SWE suite of standards [13], including SensorML [14] and
Observations and Measurements (O&M) [20, 21], for example, standardise inter-
faces for services and description languages for sensors and their processes. Quite
deliberately, the SWE standards to not provide for interoperability beyond de-
scribing a standard set of functions or a standard syntax: domain semantics, for
example, have been left for the relevant communities. This prudent for, and a key
feature of, a suite of domain independent standards; however, it does mean that,
without external agreement, SWE cannot provide more than syntactic interoper-
ability. Using vocabularies of concepts, relationships between those concepts and
various reasoning techniques, semantics can, with largely domain independent
techniques, provide more than syntactic interoperability.
Proc. Semantic Sensor Networks 2009, page 18
Semantics: The semantic approach to information systems design uses declara-
tive descriptions of information and processing units, allowing (semi-)automatic
satisfaction of declaratively described requirements. Declarative descriptions en-
able both domain-independent and domain-specific reasoning of various forms
(logic-based or otherwise) to be applied in processes such as entity identification,
search, and query and workflow generation.
Metadata serves a spectrum of data, and service, enrichment functions from
documentation, to explicitly and implicitly linking data and services, to compo-
sition.
documentation → linking → composition
Semantics enables reasoning, including search, logical reasoning and domain rea-
soning, throughout this spectrum. Reasoning can of course be recursive, deriving
new knowledge from previously inferred knowledge.
This review views semantic descriptions as OWL ontologies — for which pur-
pose, both the original W3C OWL recommendation [4], based on the SHOIN
Description Logic (DL), and the almost finalised OWL 2 [8], based on SROIQ,
are included. OWL serves a dual role in semantics: it is part mark-up of in-
formation and part logic for reasoning. Ding et al. [23] argue that an ontology
language for semantics requires a model for defining entities and relationships,
a syntax in which to write down the entities and relationships and a semantics
for inference and constraints. However, as Sheth et al. [51] point out, reasoning
need not be limited to DL reasoning and any number of inference mechanisms
can be applied to semantic descriptions.
A semantic sensor network requires declarative specifications of sensing de-
vices, the network, services, and the domain and its relation to the observations
and measurements of the sensors and services. Processing tools, logical and oth-
erwise, can then be used to answer queries, infer further information, search
for and identify particular resources or generate workflows, all of which might
require reasoning and inference in analysing the specifications, links between en-
tities and data, allowing users to develop, use and adapt sensor networks, while
abstracting away the the low-level details and difficulties of the network and its
multiple devices.
Review Topics and Outline: This review evaluates the state of the art in
OWL semantics for describing and reasoning about sensors.
Section 2 further defines semantic sensor networks. It outlines the capabilities
and architecture of a semantic sensor network.
Section 3 reviews twelve ontologies for sensors — including published and
unpublished material: as this is a technology review, not a publication review,
unpublished, publicly available material is as relevant as peer-reviewed articles.
Section 3.2 analyses the range of concepts that each ontology can describe, and
Section 3.3 complements this by discussing the relative expressive power and
completeness of the concepts.
Proc. Semantic Sensor Networks 2009, page 19
Section 4 reviews the technological setting of the twelve ontologies (and other
relevant published material on semantic sensor networks). It shows the capability
that current semantic sensor specifications enable.
Section 4.1 reviews methods for relating SWE documents to semantic de-
scriptions.
Section 5 concludes the paper, evaluating the state of the art against the
semantic sensor networks vision and outlining required future work.
2 Semantic Sensor Networks
A semantic sensor network uses declarative descriptions of sensors, networks
and domain concepts to aid in searching, querying and managing the network
and data. A semantic sensor web, on the other hand, is an OGC-style sensor
web enriched with semantic annotation, querying and inference [50]. Semantic
sensor webs rely on OGC standards and focus on issues external to the network,
although the use of semantics inside the network isn’t precluded, while semantic
sensor networks may include semantic sensor webs, semantic sensor networks
that aren’t reliant on OGC standards and allow the use of semantics to manage
the network as well as its resulting data.
Architectures for semantic sensor networks [37, 42, 34, 57, 40] use multiple
layers of semantics and technology to provide infrastructure and services. The
three layers of the architecture in this review (Figure 1) data, processing and
application, respectively support network-internal processing, inference and in-
tegration, and services. Knowledge inferred at the processing layer is made avail-
able to the application layer and may also be used to manage the network. The
stack of semantic specifications is based on node-level semantics that includes
sensor (also device and node) and observation semantics, both of which rely
on domain semantics for describing the link between the abstract and techni-
cal properties of the sensors and observations and their real-world interactions
and placements. Network-level semantics allows the description of network wide
properties, while semantics at the integration level allows for mappings between
distinct, but related, concepts to be established and also for the concepts needed
for composition, inference and, for example, scientific models and prediction.
Semantics in the architecture takes the form of vocabularies of concepts and
relations defined in OWL, first-order mappings for integration, and logic pro-
gramming rules (and other forms of inference) for defining further reasoning
power. These technologies allow a semantic sensor network to integrate mul-
tiple sensor networks, other data sources and services in ways that can cross
organisational and domain boundaries [17].
The following list (compiled from material in the Marine Metadata Interop-
erability (MMI) Device use cases,4 Sheth et al. [50], Ni et al. [42] and Huang
and Javed [34]) demonstrates potential capabilities of semantic sensor networks.
1. Classify sensors according to functionality, output, or measurement method.
4
http://marinemetadata.org/community/teams/ontdevices/usecases
Proc. Semantic Sensor Networks 2009, page 20
Fig. 1. Semantic Sensor Network Architecture. BS = Base Station and GW = GateWay
— sensor networks are generally organised around gateways that serve as aggregation
and routing points for sub-networks and base stations that collect whole-network data.
Requires machine interpretable specifications of sensors, their output types
and the domains in which they operate.
2. Find sensors that can perform a particular measurement, or can supply a
particular measurement in a particular format.
Requires the same specifications as 1 above. However, a system could do
more than search existing sensors; it could compose existing sensors and
data streams to create virtual sensors. Data format incompatibilities could
also be removed by composing suitable transformation functions.
3. Collate data spatially, temporally, or by accuracy.
Requires specifications of sensors that include locations, accuracy and mod-
elling of observation data.
4. Infer domain knowledge from low-level data.
Inference requires a reasoning mechanism, domain and sensor specifications
and annotated data.
5. Produce an event when a particular condition is reached within a period.
Requires the specifications in the previous use cases, as well as query process-
ing, energy management and configuration management, and sensor speci-
Proc. Semantic Sensor Networks 2009, page 21
fications that include energy, sensor operating conditions and lifetimes. Re-
lated capabilities could include finding sensors to satisfy particular tasks,
and using reasoning to help plan a deployment.
3 Sensor Ontologies
First, the twelve ontologies (Table 1) studied in depth in this review are intro-
duced (§3.1). Then, the concepts that each ontology can describe are outlined
(§3.2). Since indicating that ontologies have concepts for particular aspects of
sensors does not indicate the relative expressive power or quality of those con-
cepts, this section concludes by discussing qualitative aspects of the ontologies
(§3.3).
3.1 Ontologies
Avancha, Patel and Joshi [9] describe an ontology for adaptive sensor networks,
where nodes react to available power and environmental factors, calibrating for
accuracy and determining suitable operating states. Matheus et al. [38] include
sensor types in an ontology developed for recording provenance, or pedigree,
information in naval operations.
The OntoSensor [48, 47] ontology was intended as a general knowledge base
of sensors for query and inference, based on SensorML it includes concepts from
IEEE SUMO and ISO 19115. The OntoSensor ontology includes concept and
individual definitions of CrossBow sensors.5 Kim et al. [35] extend OntoSensor
for Web services, though the ontology or full details are not available.
Eid et al. [25, 26] propose a two-tier framework for a sensor ontology. In their
framework the sensor hierarchy, data and extension ontologies (lower tier) all
reference SUMO (upper tier).
Calder et al. [16], as part of the Coastal Environmental Sensing Networks
(CESN) project6 for sensor networks for coastal observing, have built an ontology
of sensor types and a DL and logic programming rules reasoner for making
inferences about data and anomalies in measurements. The CESN ontology has
ten concept definitions for sensor instances and six individuals.
The SWAMO [55] ontology for intelligent software agents describes physical
devices and process models and tasks. The ontology was designed to compatible
with SensorML.
5
http://www.xbow.com/
6
http://www.cesn.org
7
http://www.memphis.edu/eece/cas/onto_sensor/OntoSensor.txt
8
http://www.cesn.org/resources/cesn.owl
9
http://www.dvs.tu-darmstadt.de/staff/aherzog/a3me/a3me.owl
10
http://www.csd.abdn.ac.uk/research/ita/sam/downloads/ontology/ISTAR.owl
11
http://mmisw.org/ont/mmi/20090519T125341/general
12
http://mmisw.org/ont/mmi/device
13
http://www.w3.org/2005/Incubator/ssn/wiki/images/4/42/
SensorOntology20090320.owl.xml
Proc. Semantic Sensor Networks 2009, page 22
reference date active purpose
Avancha et al. [9] 2004 7 adaptive sensor networks
Matheus et al. [38] 2005 7 pedigree (provenance)
OntoSensor [48, 47]7 2006 7 knowledge base and inference
Eid et al. [25, 26] 2007 ? searching heterogeneous sensor network data
Kim et al. [35] 2008 ? services
CESN [16]8 2008 3 inferring domain knowledge from data
SWAMO [55] 2008 3 intelligent agents
A3ME [30, 31]9 2008 3 resource constrained devices
ISTAR [44, 27]10 2009 3 task assignment
OOSTethys [2]11 2009 3 integrating standards-compliant Web services
MMI [1]12 2009 3 interoperability
CSIRO [41]13 2009 3 data integration, search, classification and workflows
Table 1. Ontologies studied in this review: references, year of last known update
or publication, active if known, main stated purpose, and url if ontology is publicly
available.
The A3ME [31, 30] ontology of devices and their capability types was devel-
oped to classify devices and their capabilities in a heterogeneous network, with
a focus on making the ontology usable on resource constrained devices.
The ISTAR [44, 27] ontology was developed as part of a system to automat-
ically select sensors for tasks based on their fitness for the task description. The
system can select suitable sensors, aid in deployment, decide at runtime on the
sensors to use from those selected as candidates and configure deployed sensors.
The OOSTethys community14 are developing open-source resources to help
install, integrate and update standards-compliant Web services for oceanographic
observing, with a particular emphasis on OGC standards.15 The sensor ontology
focuses on system structure and the proceedure and result of an observation.
The Marine Metadata Interoperability (MMI) Device Ontologies Working
Group16 is developing an ontology of oceanographic devices, sensors and sam-
plers.
The CSIRO sensor ontology [41, 19] is a generic ontology for describing sen-
sors and deployments. It is intended to be used in data integration, search,
classification and workflows. There are two example sensor definitions available
for the CSIRO ontology.
Hu, Wu and Guo [33] develop two layers of ontology with the intention of
using rules to deduce high-level, contextual information from low-level data, but
do not provide enough detail to be included in the analysis here. Horan [32]
uses the OWL-S [43] Web services ontology as a basis for a sensor ontology,
but does not provide enough detail for inclusion. As it is based on services,
processes, inputs and outputs, and grounding (which is interpretable as access,
14
http://www.oostethys.org/
15
http://www.oostethys.org/ogc-oceans-interoperability-experiment
16
http://marinemetadata.org/community/teams/ontdevices
Proc. Semantic Sensor Networks 2009, page 23
communication and physical information) OWL-S seems an appropriate basis
for a sensor ontology; however, it would need to be extended with sensor spe-
cific concepts — many of OWL-S’s capabilities are, in any case, covered by the
CSIRO, OntoSesnor, MMI, OOTethys and SWAMO ontologies.
3.2 Concepts
Table 2 shows the aspects of sensors that the ontologies can describe. A tick
indicates the capability to describe the stated aspect in some form. The absence
of a tick indicates either no ability to describe this aspect, or insufficient infor-
mation. Absence of some aspect from the table indicates that none of the studied
ontologies can describe those concepts.
The Avancha, Eid and Kim ontologies focus mainly on data and measure-
ments, with little capacity to describe sensors, systems or how measurements
are taken. The CESN ontology, and to some extent Matheus’s ontology as well,
lie at another extreme, being almost entirely a description of sensor types.
The SWAMO, MMI and OOSTethys ontologies extend the analysis along a
third dimension, from measurements and sensor types to systems. Each includes
concepts for describing measurements, systems, the components of systems and
how those components are organised — the structure of systems. They can be
seen, in some sense, as ontologies for describing the structure and process of mea-
surement taking systems. Both MMI and OOSTethys are work-in-progress and
it’s likely that their scope will be extended; the MMI Device Ontologies Working
Group, for example, intend to add concepts ranging from physical properties and
limits of the sensor to communication information and software.17
The A3ME ontology covers a broad range of concepts, but in a simple way
intended for low-power devices that do not have complex reasoning capabilities.
The CSIRO and OntoSensor ontologies are each being able to describe most
of the spectrum of sensor concepts and thus cover a wider range of concepts than
the other ontologies. The OntoSensor ontology includes more on data and sensor
types than the CSIRO ontology. The CSIRO ontology can, however, describe
composition and structure, while OntoSensor can only describe part-of relations
— the difference between an assembly plan and a parts list. These expressivity
differences are the subject of the next section.
3.3 Expressive Power
This section discusses the relative expressive power of the ontologies for a num-
ber of important points. The OntoSensor, SWAMO, OOSTethys, CSIRO and
MMI ontologies, for example, can each describe the platform a sensor is at-
tached. OntoSensor and OOSTethys, through the MMI platform ontology [11],
can describe the components of platforms. The SWAMO, CSIRO, OOSTethys,
ISTAR and MMI ontologies can say a sensor is attached to something (a plat-
form), OntoSensor can list the parts of the platform if they are independently
interesting.
17
http://marinemetadata.org/community/teams/ontdevices/facetoutline
Proc. Semantic Sensor Networks 2009, page 24
sensor physical observation domain
identity & manufacturing
dimension, weight, etc.
contacting & software
units of measurement
operating conditions
field of view/sensing
data/observation
sampled medium
action & process
sensor hierarchy
response model
feature/quality
power supply
configuration
components
deployment
frequency
accuracy
platform
location
history
time
ontology base
concepts
Avancha sensor 3 33 3 33 3 3 3 3 3
Matheus system & 3 3 3 3 3 3
sensor
OntoSensor component 3 3 3 3 3 33 3 3 3 33 3 3 3 3 3
& sensor
Eid sensor 3 3 33 3 33 3 3
Kim sensor 3 3 3 33 3 3
CESN sensor 3 3 3 3 3 3
SWAMO agent, 3 3 3 3 3 3 3 3 3 3
process &
sensor
A3ME device & 3 3 3 3 3
capability
ISTAR system & 3 3 3 3 3 3
sensor
OOSTethys component, 3 3 3 3 3 3
system &
process
MMI sensor 3 3 3 3 3 3 3 3 33 3 3 3
(system) &
process
CSIRO sensor 3 3 3 3 3 3 3 3 33 3 3 3 33 3 3 3 3
& process
Table 2. Sensor Concepts
Proc. Semantic Sensor Networks 2009, page 25
The same five ontologies can describe the components of a sensor system
and its processes. OntoSensor, MMI and OOTethys describe part-of relations.
SWAMO can describe part-of relations for systems and a form of process chain-
ing. While the CSIRO ontology can describe more sophisticated forms of struc-
tural and sequencing composition, with, for example, sequence, conditional and
repetition for process composition. These sophisticated forms of composition
are important in describing sensors, as SensorML recognises. Without structural
composition it is not possible to describe sensors accurately, nor is it possible to
search for and automatically compose and execute virtual sensors.
In the OntoSensor and CSIRO ontologies, sensors and processes are in dif-
ferent parts of the concept hierarchy, whereas the OOTethys and MMI ontolo-
gies are organised such that a process is-a system — and to such an extent in
OOTethys that a sensor is-a system and a system is-a process. The organisation
in the OntoSensor and CSIRO ontologies allows sensors as sub-processes and
vice versa, but the explicit hierarchical organisation of the MMI and OOTethys
ontologies may allow some interesting modelling options.
The OntoSensor, Matheus, CESN and CSIRO ontologies each provide some
capacity for organising sensors into a hierarchy of sensing concepts, of which
OntoSensor has the most concepts and sub-concepts. The OntoSensor ontology
also has the greatest expressive capacity for data.
Observations and data, which are needed in describing capabilities of sensors,
require care in modelling, for example, accuracy is often condition dependent.
The Vaisala WM30 wind sensor,18 for example, has an accuracy of ±0.3m/s
for wind speeds below 10m/s, accuracy of ±2% for wind speeds up to 60m/s
and isn’t rated for wind speeds over 60m/s. These finer aspects of the response
model can be represented in the CSIRO ontology, and to some extent in the
SWAMO and OntoSensor ontologies. However, none of the ontologies can fully
describe response models, configurations, history, or operating conditions to the
level required to satisfy all the capabilities in Section 2.
4 Technologies
The section discusses how the technology developed alongside the sensor on-
tologies enables parts of the SSN architecture outlined in Section 2. There are
three generic reasoning mechanisms that support the technology discussed in this
section: OWL reasoning (DL inference), logic programming rules and SPARQL
queries.
By virtue of being metadata expressed in OWL, each of the ontologies is a
language for cataloguing sensors, with various levels of completeness and expres-
sive power (§ 3.2 and § 3.3), and thus come with DL inference for validation,
categorisation and some search capability.
SPARQL [7] gives greater search potential than DL querying, and can be
combined with DL inference [52]. Kim et al. [35] and Eid et al. [26] give examples
of using SPARQL to query a sensor ontology.
18
http://www.vaisala.com/files/WM30_Brochure_in_English.pdf
Proc. Semantic Sensor Networks 2009, page 26
Logic programming rules give a further inference resource for classifying in-
stances or adding new instances to an ontology. Logic programming, in con-
junction with DL inference, can be used to derive high-level information (say,
inference about weather conditions) from low-level data (temperature and wind
speed). It is used by Calder, Morris and Peri [16] to derive further inferences
about data, in ISTAR to derive further capabilities of sensors [44, 27, 22], and by
a number of other related technologies [54, 15, 57, 10, 34, 33]. Henson et al. [29]
annotate SWE services to reason over sensor data and query high-level knowl-
edge of the environment as well as low-level sensor data.
OWL reasoning and logic programming is used with the ISTAR ontology to
suggest sensors that match parts of tasks and a set covering algorithm is used
to find simple combinations of these that could form a complete solution to the
information needs of the task [22, 44, 27]. The CSIRO ontology can be used for
more complex automated composition and potentially similar technology to that
used for Web service composition [19].
4.1 Semantic Annotation
Semantic annotations link data to more expressive ontological representations
through model references [5]. As large amounts of sensor data are being made
available on the web, semantic descriptions of sensors and sensor data provide a
means to make such data discoverable, accessible, and queryable, and semantic
annotation of sensor data provides a means of relating the data to the semantic
description. Assuming sensor data is encoded in SWE format, there are currently
two approaches for annotation: XLink [3] and RDFa [6].
XLink, the XML Linking Language, is an XML markup language for creating
hyperlinks in XML documents. The XLink recommendation outlines methods of
describing links between resources in XML documents. XLink attributes can be
added to SensorML and O&M documents (see Figure 2) to provide semantic
annotations for the sensor data [29, 39].
Fig. 2. Semantic annotation of O&M with XLink
RDFa, Resource Description Framework-in-attributes, enables the layering
of RDF information on any XHTML or XML document. RDFa provides a set of
attributes that can represent semantic metadata within an XML language and
a simple mapping to RDF triples. These attributes can be added to SensorML
and O&M documents to provide semantic annotations for the sensor data [50,
10], but require additional syntax.
Proc. Semantic Sensor Networks 2009, page 27
XLink is already used in SWE documents, thus, no syntactic or structural
changes are required. This explains the relative success of XLink-based ap-
proaches in earlier attempts to add semantic annotations to SWE documents.
Recognizing which XLink attributes correspond to semantic annotations and
which correspond to permissible SWE usages could become difficult.
Approaches based on RDFa look more promising at the level of SWE docu-
ments since it would be easier to process the annotations independently of the
rest of the document. Further work is required to check that the introduction
of RDFa would not bring major changes for the implementers of the SWE stan-
dards and also to investigate how RDFa-enabled SWE services could be further
integrated with other RDFa-based Web mashups.
SWE also provides a definition attribute that provides a link to a registry
definition, which may also link to an ontological description [29, 39]. In addi-
tion, SWING [24], Semantic Web-Service Interoperability for Geospatial Deci-
sion Making, describes sensor annotation of OGC documents at three distinct
levels: (1) at the document level using keyword metadata, (2) at the schema level
using SAWSDL [5], and (3) at the data level using by semantically annotating
SWE documents as described above.
5 Conclusion
This paper has reviewed the state of the art in semantic descriptions of sensors:
twelve OWL ontologies were reviewed, with a focus on sensor ontologies as a key
enabling component of semantic sensor networks.
A combination of OntoSensor and the CSIRO ontology represents the current
limit of expressive capability for semantic sensors. However, questions remain
about the correct structure and scope of a sensor ontology, including how best
to express composition of processes and systems, how to express response model
details such as accuracy and how to delineate between and integrate sensors,
services and scientific (and other predictive) models. Units of measurement, lo-
cation and time, for example, are perhaps best deferred to authorities. Until such
authorities and ontologies exist, however, these aspects must be handled in con-
junction with a sensor ontology; for example, building on either OWL-Time19
or Henson et al.’s [28] model for time series information, which is not currently
covered adequately in sensor ontologies.
No current ontology, nor a combination of the available ontologies, is able
to express all the properties required for the capabilities in Section 2. However,
the current state of the art can enable classification, and linking of data and
sensors, and the technology exists to construct virtual sensors as compositions of
existing components. In short, sensor ontologies have enabled a range of semantic
technologies for semantic sensor networks, but the state of the art is some way
from enabling the full range of features envisaged for semantic sensor networks.
DL inference and logic programming rules are the main forms of inference
the have been used with semantics for sensors [16, 44, 27, 22, 54, 15, 57, 10, 34,
19
http://www.w3.org/TR/owl-time/
Proc. Semantic Sensor Networks 2009, page 28
33, 29]. However, as advocated by Sheth, Ramakrishnan and Thomas [51], the
importance of domain reasoning, abductive, fuzzy and probabilistic reasoning is
beginning to be realised. Search using DL and SPARQL has been applied for
sensor descriptions. More advanced Semantic Web technologies such as mixtures
of DL, structural similarity and information retrieval techniques, as in Klusch
et al. [36], have not yet been applied to sensors.
If large amounts of data can be annotated using the techniques outlined
in Section 4.1, either post processed or tagged at point of observation, then
semantic reasoning and linking can be applied to a wider range of data than
that emanating from semantic sensor webs and networks.
Sensors and observations are complementary and for some aspects intersect-
ing. This review has covered sensors and measurements from a sensor perspec-
tive; however, the observation perspective is important and could be reviewed
as a complement to this review. Among other O&M ontologies, Probst [45, 46]
gives an ontological grounding for O&M aligned to the DOLCE upper ontology.
The W3C Semantic Sensor Networks Incubator Group (SSN-XG),20 which
includes developers from the CSIRO, MMI and OOTethys ontologies, aims to
build a general and expressive ontology for sensors, addressing the coverage,
structural and expressivity issues discussed in this review.
Acknowledgements: Part of this research was conducted as part of the CSIRO
Water for a Healthy Country National Research Flagship and the Sensor Network
Technologies Theme.
The Tasmanian ICT Centre is jointly funded by the Australian Government
through the Intelligent Island Program and CSIRO. The Intelligent Island Pro-
gram is administered by the Tasmanian Department of Economic Development,
Tourism and the Arts.
Part of this research was supported in part by The Dayton Area Graduate
Studies Institute (DAGSI), AFRL/DAGSI Research Topic SN08-8:“Architectures
for Secure Semantic Sensor Networks for Multi-Layered Sensing.”
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