=Paper=
{{Paper
|id=None
|storemode=property
|title=Bridging the Gap between Semantic Web and Networked Sensors: A Position Paper
|pdfUrl=https://ceur-ws.org/Vol-668/paper1.pdf
|volume=Vol-668
|dblpUrl=https://dblp.org/rec/conf/semweb/SuR10
}}
==Bridging the Gap between Semantic Web and Networked Sensors: A Position Paper==
https://ceur-ws.org/Vol-668/paper1.pdf
Bridging the Gap between Semantic Web and
Networked Sensors: A Position Paper
Xiang Su and Jukka Riekki
Intelligent Systems Group and Infotech Oulu,
FIN-90014, University of Oulu, Finland
{Xiang.Su,Jukka.Riekki}@ee.oulu.fi
Abstract. In this position paper, we present our work towards design-
ing a Semantic Web languages-compatible representation for networked
sensors. The representation, Entity Notation, is proposed to connect sen-
sors to the Semantic Web. Entity Notation can express RDF and OWL
ontology models in a uniform format. Meanwhile, it offers a lightweight
alternative for sensors with limited computation and communication ca-
pabilities. We present motivation and design issues of Entity Notation in
this paper.
Key words: Entity Notation, Semantic Web Representations, Embed-
ded Sensing
1 Introduction
Sensors and sensor networks will be everywhere, from environment monitoring to
planet exploration. Increasing maturity of sensors and sensing technology opens
up questions about how to effectively operate with massive amounts of data
produced by current large scale deployments. What kind of solutions should be
developed on top of these sensor networks to make the best use of all the data?
Semantic Web approaches this challenge from the opposite direction and
offers solutions for a heterogeneous, large scale inter-operating networks. When
sensors meet Semantic Web, there will be benefits for both sides. For the Seman-
tic Web, sensors and sensor networks bring a whole new peripheral of massive
data from real time measurements. For sensing systems, Semantic Web enables
resource sharing, resource reallocation, and advanced functionality like inference
over events in space and time within a particular context.
One main challenge for connecting networked sensors to the Semantic Web
is that most sensors produce raw data in their own formats. Hence, each sensor
model needs its own solution when sensors are connected to Semantic Web. We
tackle the gap between Semantic Web and networked sensors at the data inter-
change level. As RDF [1] and ontology [2] models are based on triplet represen-
tations, we design a uniform format, Entity Notation (EN), based on triplets for
expressing RDF models and OWL ontologies. Moreover, we suggest a compact
format for transmission when resources of sensor networks are constrained. The
compact format shortens our representation based on techniques of templates
2 Xiang Su and Jukka Riekki
and prefixes. Entity Notation is unique in its combination of expressiveness and
compactness. This combination makes it ideal for connecting networked sensors
to the Semantic Web.
We start this paper by introducing related data representations in section 2.
Then, we present our requirements and design details in section 3. Finally, we
discuss our future work in section 4.
2 Related Work
Numerous data representations have been defined in different sensing systems,
like Generic Sensor Format [3], Unisens [4] and Unified Transportation Sensor
Data Format [5]. Sensor model specific data formats introduce dependencies into
the system, and even set up a barrier to web applications. Binary XML emerges
as a new trend to support convenient solutions for transferring raw data to XML
representation. There are many competitive binary XML formats, like X.694 [6],
Efficient XML Interchange Format (EXI) [7], and Fast Infoset [8]. Data in these
formats can be transformed into XML straightforwardly, and some of them can
achieve good compression ratio. However, XML representation has no embedded
semantics, and the gap between syntax and semantics still exists.
On the other hand, there are some compact serializations for representing
knowledge, like N3 [9] and Turtle [10], which can express RDF in a straightfor-
ward manner. They are flexible languages with strong expressive capability. But
unfortunately, they normally contain shorthand syntaxes to shorten the files.
This is not a suitable solution for resource-constrained sensors, because they do
not have enough processing power to process these shorthand syntaxes.
Another effort to increase interoperability is to annotate sensor data with se-
mantic metadata. Metadata languages, like RDFa, are utilized to provide context
information for raw data [11]. However, as the expressivity of annotation formats
is limited, they cannot capture the full semantics of sensor data. In a nutshell,
more work is needed in developing representation that can interoperate Semantic
Web knowledge with sensing data straightforwardly and unambiguously.
3 Designing Entity Notation
Our ultimate goal is to design a communication-convenient serialization for Se-
mantic Web languages. This serialization will ease the transferring of knowledge
models and advance interoperability between Semantic Web and networked sen-
sors.
We examine W3C Semantic Web standards, and consider RDF and OWL
ontology as essential models for realizing knowledge interoperability. Intelligent
Semantic Web applications normally build on data represented by RDF and
knowledge based on ontologies. Then inference tools can be deployed on this
knowledge base to enable intelligent functions. At current stage, EN can serialize
RDF models and OWL DL ontology in a uniform format, and we will extend
this format for other standard languages.
Bridging the Gap between Semantic Web and Networked Sensors 3
3.1 Design Requirements
We have specified the following main design requirements for EN. First, EN must
be expressive enough for representing RDF and OWL DL ontology models in a
uniform format. It should be possible to transform any description of RDF and
OWL DL knowledge into EN packets, and vice versa. Fulfilling this goal enables
transforming RDF/XML and OWL into EN packets. This is essential, as XML
is one of the most widely used serialization, and OWL utilizes XML syntax too.
Many available reasoning tools are based on RDF/XML and OWL. Second, EN
must support distributed knowledge production. Any snippet of RDF and on-
tology knowledge can be defined by any device and Semantic Web applications,
and these snippets can be discovered, transferred and integrated into larger on-
tologies. This will be a unique feature that to the bests of our knowledge has
not been reported before. Third, EN must be suitable for bandwidth limited
and ultra-low power communication links of sensor networks. For example, an
unsophisticated embedded sensor should be able to compose EN packets using
minimal computation power and deliver packets to Semantic Web applications
in an energy efficient manner. Fourth, EN must be able to carry any kind of
information between sensors and Semantic Web applications. Fifth, the infor-
mation carried in EN packets needs to be identified in a unique fashion. This is
an important feature for an Internet scale system when all the possible usages
of the information cannot be specified in advance.
3.2 General EN Packet Formats
The basis of RDF models is a sequence of triplets (Subject, Predicate, Object),
and ontologies also contain simple definitions and triples, which describe re-
lations among concepts. Complex relations are built on simple definitions and
triplets. Besides, all information is identified in a unique fashion by URIs, except
literals.
To fulfill the expressibility and lightweight requirements, we define two EN
formats: the complete format, and the short format. Complete format has enough
expressibility, as it follows the triple notation. Moreover, we adopt almost all
terms from OWL in the complete format. It’s straightforward to serialize RDF
and ontologies into complete EN packets and vice versa. Lightweight short pack-
ets can support resource-constrained devices and slow communication links. Fur-
thermore, EN supports composition and decomposition of knowledge, and thus
enables incremental knowledge definition and communication. We define for RDF
and ontology knowledge a uniform EN format that does not constrain the type
of information. Finally, EN uses UUIDs (Universally Unique Identifiers) [12] and
URIs for unique identification.
Generally, an EN packet depicts an entity and its relationships with values
and other entities. An entity is some identifiable whole, concrete or abstract.
Entities in the RDF level knowledge tend to be concrete, like sensors, robots and
measurements. Entities in the ontology level are abstract concepts and relations
in the ontology. An entity description is of the form:
4 Xiang Su and Jukka Riekki
EntityType EntityID
PropertyName PropertyValue
...
PropertyName PropertyValue
The subject can be same for multiple triplets, and this happens for most
entities in actual knowledge models. Hence, we identify one entity in an entity
description, and then any number of triplets can be defined about it. Besides,
we include type information for EntityID, because this is important information
to identify its subsumption.
Square brackets and angle brackets are utilized to identify the level of knowl-
edge an EN packet should mapped to. When an entity description is wrapped in
square brackets ([ and ]), this EN packet should be transferred to RDF models.
When an entity description is wrapped in angle brackets (< and >), this EN
packet should be transferred to OWL ontologies.
When an EN packet is mapped to RDF statements, EntityID maps to the
subject of a triplet, PropertyNames map to predicates, PropertyValues map
to Objects, and EntityType maps to a class this entity is subsumed. When it
should be mapped to ontology snippets, EntityID maps to an element name,
EntityType indicates the type of the element, and PropertyNames are mapped
to relationships or EntityCharacteristics. Relationships between entities, like
rdfs:subClassOf, and rdfs:domain, can be mapped to OWL constructors. Entity-
Characteristics can be mapped to the term rdf:type, while CharacteristicsName
can be mapped directly to OWL types.
For resource-constrained sensors, complete EN packets are still verbose as
full URIs are used. We do not utilize complex compression algorithms, because
this would introduce additional resource-consumption for sensors. Instead, we
suggest a short packet format for decreasing the need for computations and
communication bandwidth.
The short EN format uses templates and prefixes to shorten packets. The
basic idea is that a template contains a description of the constant part of a
sequence of EN packets and placeholders for the variable items. The packet sent
over the communication links needs to contain only a template identifier and the
variable items. A sequence of complete EN packets can then be assembled by
replacing the template’s placeholders with the values contained in a short packet.
Prefixes are used to shorten URI references. The receiver, like a gateway, needs to
possess the corresponding template in order to assemble complete packets but a
resource-constrained sensor needs only the template identifier. Prefixes are used
to shorten URI references. Similar to complete packets, square or angle brackets
are utilized to wrap following descriptions depending on the level of knowledge:
UUID PropertyValue ... PropertyValue
UUID is an identifier that is guaranteed to be unique across space and time.
In its canonical form, a UUID is 128 bits long. We utilize UUIDs to identify
templates. When a short packet format includes EntityIDs the format is:
Bridging the Gap between Semantic Web and Networked Sensors 5
UUID
EntityID PropertyValue ... PropertyValue
......
EntityID PropertyValue ... PropertyValue
3.3 The Role of EN in Connecting Semantic Web and Networked
Sensors
Figure 1 presents the role of EN in bridging the gap of Semantic Web and net-
worked sensors. Embedded sensors can compose complete or short EN packets
depending on their capabilities, but they all can transformed into Semantic Web
representation at gateways. Knowledge base and Semantic Web applications can
access sensor produced data and knowledge straightforwardly. Moreover, some
devices in sensor networks can actuate commands what Semantic Web applica-
tions produce.
Fig. 1. The role of EN in bridging the gap of semantic web and networked sensors
4 Discussion
In this short paper, we presented motivation of designing a novel representation
for connecting networked sensors to Semantic Web. Then, we introduced design
requirements, general formats of EN, and EN’s role in connecting networked
sensors to Semantic Web. We have implemented EN in a simulator, wearable
sensors, and embedded GPS sensors of Nokia N95 cell phones. Implementation
details are not included in this paper.
EN is unique in its combination of expressiveness and compactness. Utiliza-
tion of UUIDs and URIs enables EN a scalable solution for Semantic Web. It is
recommended to adopt full 128 bits UUIDs for web-scale system. The shorter
alternatives, like 16 bit, 32 bit UUIDs, guarantee uniqueness in smaller scale
systems. UUIDs offer a convenient solution for mapping shorter UUIDs to full
ones, which facilities the integration of small scale systems to large, even Internet
scale systems.
6 Xiang Su and Jukka Riekki
The current EN version does not support nesting although nesting is widely
supported by data representations and it compresses descriptions. Instead of
nesting we divide descriptions into several small EN packets and use URI ref-
erences and blank node identifiers to determine relations between them. Small
packets facilitate optimizing the communication. For example, packets can have
different routing paths or they can be sent over a longer period of time. We will
consider nesting in near future; we will analyze the computational load it sets
for constrained devices and decide based on this analysis whether EN should
support nesting.
This is a early work, and we will continue it with several aspects. We will
specify formal syntax and semantics for our serializtion, and perform more ex-
periments based on standard dataset to evaluate it. Moreover, we will study
how to represent SWRL and RIF rules in EN. This is another important step
for building a uniform format for transferring representations of Semantic Web.
Finally, we will explore possibilities of applying EN and Semantic Web technolo-
gies in sensor networks, especially how to infer knowledge from sensory data in
ubiquitous environments.
Acknowledgments. This work was funded by Infotech Oulu and the MOTIVE
research project program of the Academy of Finland.
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