Database Reference
In-Depth Information
annotation can be either spatial, temporal, or may be semantic in na-
ture. Interesting discussions of research issues which arise in the context
of the semantic and database management issues of the sensor web may
be found in [96, 17].
The semantic web encodes meta-data about the data collected by
sensors, in order to make it effectively searchable and usable by the
underlying services. This comprises the following primary components:
The data is encoded with self-describing XML identifiers. This
also enables a standard XML parser to parse the data.
The identifiers are expressed using the
Resource Description Frame-
work (RDF).
RDF encodes the meaning in sets of triples, with each
triple being a subject, verb, and object of an element. Each ele-
ment defines a Uniform Resource Identifier on the Web.
Ontologies can express relationships between identifiers. For ex-
ample, one accelerometer sensor, can express the speed in miles
per hour, whereas another will express the speed in terms of Kilo-
meters per hour. The ontologies can represent the relationships
among these sensors in order to be able to make the appropriate
conversion.
We will describe each of these components in the description below.
While the availability of real-time sensor data on a large scale in do-
mains ranging from trac monitoring to weather forecasting to home-
land security to entertainment to disaster response is a reality today,
major benefits of such sensor data can only be realized if and only if
we have the infrastructure and mechanisms to synthesize, interpret, and
apply this data intelligently via automated means. The Semantic Web
vision [73] was to make the World Wide Web more intelligent by lay-
ering the networked Web content with semantics. The idea was that
a semantic layer would enable the realization of automated agents and
applications that “understand” or “comprehend” Web content for spe-
cific tasks and applications. Similarly the Semantic Sensor Web puts
the layer of intelligence and semantics on top of the deluge of data com-
ing from sensors. In simple terms, it is the Semantic Sensor Web that
allows automated applications to understand, interpret and reason with
basic but critical semantic notions such as “nearby”, “far”, “soon”, “im-
mediately”, “dangerously high”, “safe”, “blocked”, or “smooth”, when
talking about data coming from sensors, and the associated geo-spatial
and spatio-temporal reasoning that must accompany it. In summary, it
enables true semantic interoperability and integration over sensor data.
In this section, we describe multiple aspects of Semantic Sensor Web
