A Semantic Environment for Data Processing in

Embedded Sensor Networks

Salvatore F. Pileggi

Communications Department, Universidad Politécnica de Valencia, Valencia, Spain

Abstract. Sensor Networks have the critical role of technological bridges be-

tween the real world and information systems, through always more consolidat-

ed and efficient solutions that enable advanced heterogeneous sensor grids.

Sensor Networks have been increasingly adopted in the context of several dis-

ciplines and applications and they are currently disseminated everywhere. The

relevance of their role is growly increasing in the everyday life. Data processing

is one of the critical and key issues for sensor networks. An advanced semantic

environment for event-driven data processing is proposed in the paper.

1 Introduction

During last years, sensors have been increasingly adopted in the context of several

disciplines and applications (military, industrial, medical, homeland security, etc.)

with the aim of collecting and distributing observations of our world in everyday life.

Sensors progressively assumed the critical role of technological bridges between

the real world and information systems [1], through always more consolidated and

efficient solutions that enable advanced heterogeneous sensor grids [16].

Sensors are currently disseminated everywhere and the relevance of their role is

growly increasing in the everyday life.

They can work as independent stand-alone objects or as part of complex networks,

performing cooperative tasks in order to reach common goals.

Current sensor networks are able to detect and identify simple phenomena or mea-

surements as well as complex events and situations.

Complex systems build their own knowledge on the base of sensor data and, even-

tually, considering other available data. Due to the specificity of the knowledge for

each system, also the process for building it is commonly considered a domain specif-

ic task that requires ad-hoc infrastructures. Semantic Technologies [5][8][14] could

allow an innovative approach for the problem.

Semantic Technologies are able to improve the machine-to-machine interaction

through an innovative model of interoperability (Semantic Interoperability [12]) that

assumes rich schemas for knowledge representation.

Semantic Interoperability integrates the common Functional Interoperability model

introducing the interpretation of means of data [12]. This model allows a new pers-

pective and an innovative approach for the systems because the “intelligence” is no

longer implemented by actors (that are similar to interpreters) but it is implicit in the

information (Ontology-driven computation [12]).

F. Pileggi S..

A Semantic Environment for Data Processing in Embedded Sensor Networks.

DOI: 10.5220/0003350100230030

In Proceedings of the International Workshop on Semantic Interoperability (IWSI-2011), pages 23-30

ISBN: 978-989-8425-43-0

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Semantic Knowledge implicitly needs rich schemas that include structured con-

cepts, related properties as well as complex relationships among them [11]. Standar-

dized methodologies for knowledge (semantic knowledge in this case) building are a

current open research issue. Mapping real knowledge on semantic schemas is, proba-

bly, the most creative task for the concrete engineering of Semantic Systems [12].

The description of the proposed semantic environment is structured in two main

parts: first the infrastructure, based on standard reasoners [8], is described and, then,

the Ontology, implemented in OWL [11], is proposed.

2 Related Work

At the moment, semantic technologies are applied in several sensor architectures in

order to reach different goals.

Common applications have the aim of providing advanced support to information

description and processing [2], data management [6], interoperable networking [5],

dynamic representation of situations and system states [7], advanced analysis of data

[9] and classification [10].

Semantic Sensor Web [4] would be a generalized concept in which semantic tech-

nologies allow interoperable interchanging of semantic data [12]. A semantic envi-

ronment for Sensor Web addresses several research issues and challenges. Probably,

the engineering of semantic knowledge is the most interesting for its central and key

role as well as for the fundamental lack of standardized methodologies [12].

Data processing is one of the most common and key issue for embedded sensor

networks; the convergence of semantic technologies could enable the development of

advanced semantic interoperable environments in which abstract knowledge is direct-

ly built on the top of sensor data with a completely transparent approach for higher

layers of systems. Furthermore, the knowledge can be defined and represented ac-

cording to several perspectives and abstraction levels.

3 An Interoperable Layer for Event-driven Sensor Data

Processing

An infrastructure for event-driven sensor data processing can be modeled according to

the schema represented in Figure 1.

The lower layer of the architecture (Data Manager) has the role of collecting (syn-

chronized) sensor data, integrating it with data available at the moment in the system.

This information is processed by an engine that implements the “intelligent” layer

of the system and has the key role of processing available data providing the system

with the related knowledge. The engine needs a representation for both data and

knowledge.

The knowledge built by the data processing engine is available for the Control Sys-

tems, understood as the layer that implements high-level applications.

In common architectures all represented layers are implemented as ad-hoc infra-

structures.

Fig. 1. Simplified vision of an interoperable layer for sensor data processing.

Semantic technologies allow the deployment of a novel semantic environment for

data processing. This environment is able to work in a context of semantic interopera-

bility because the rules for knowledge building are expressed by the Ontology. Each

concrete system can so implement its own rules in its own Ontology that can be

processed by standard reasoners.

A description of the proposed environment and a brief discussion about the related

key issues is object of the following section.

4 Outline of the System

The description of the proposed semantic environment is structured in two main parts:

first the infrastructure, based on standard reasoners [8], is described and, then, the

Ontology, implemented in OWL [11], is proposed.

4.1 The infrastructure for Data Processing

The architecture for data processing is represented in Figure 2. It is implemented over

Java Technologies. As showed, the core infrastructure is based on semantic reasoners.

A semantic reasoner, reasoning engine, rules engine, or simply a reasoner, is com-

monly defined as ‘a piece of software able to infer logical consequences from a set of

asserted facts or axioms’. The notion of a semantic reasoner generalizes that of an

inference engine, by providing a richer set of mechanisms to work with. The inference

rules are commonly specified by means of an ontology language, and often a descrip-

tion language.

Fig. 2. Schematic view of the infrastructure for data processing.

As in common infrastructures, sensor data is received by a Base Station. The Data

Manager has the role of preprocessing (if required) and synchronizing (if required)

data input for the reasoner. Simple logic environments assume not related data from

the various sensor nodes; on the contrary, complex contexts propose relationships

among sensor data and, eventually, among data from internal or external information

systems. Data Manager accept in input only data represented according to a semantic

schema.

The effective data processing is the task implemented by the reasoner that receives

in input synchronized data (from Data Manager) and the Process Ontology (described

in the next section). The Process Ontology provides the reasoner with the semantic for

the data processing. The reasoner implements three different and progressive software

layers [12]: the first one is just an abstraction of the functionalities of a standard rea-

soner (Pellet 1.3 in this case) in order to support Ontology-driven computation; the

second and the third one are domain specific extensions that respectively provide

support for Ontology-aware computation (the reasoner performs its behavior in func-

tion of detected Ontology) and for advanced functionalities (e.g. Learning).

The architecture is structured in order to support parallel computation considering

typical time constraints requirements of certain architectures. The output of each

reasoner instance is available for the Control System that actively supports any high-

level application. Output knowledge is the enabler for the event-driven engine that

performs real-time actions. Both input data and output knowledge are stored in the in

the information system and object of advanced data analysis.

4.2 A Process Ontology Model for Embedded Sensor Networks

As previously mentioned, the Process Ontology has a key role in the system because

it has to provide the semantic for data processing.

In the common means, a process ontology specifies the behavioral view on physi-

cal systems. In the general case it is quite difficult to formalize what the notion of a

dynamic process precisely entails. For example, Process Ontologies are currently used

to define and specify complex business processes [15].

Fig. 3. The Ontology schema.

In the context of this work, the Process Ontology (Figure 3) would specify the se-

mantic of the main interest concepts in a data process infrastructure as well as the

relationships among them; at the moment the schema is composed of four main con-

cepts (data source, data, event and actions) featured by an increasing degree of ab-

straction. All the mentioned concepts should be understood in the context of concrete

systems. However, it is relatively suitable the generalization of data source and data

models respectively through a resource-centric ontology (typically a Domain Ontolo-

gy [13]) and a Data Ontology. On the contrary, even if several classifications are

possible, the logically higher concepts (event and action) probably make sense only

within concrete contexts. The most complex domain/ application specific aspect is the

set of relationships among the various concepts composing the logic schema.

Considering the objective difficulty of generalization of domain/application specif-

ic aspects, the schema represented in Figure 3 is implemented in OWL (Figure 4) as a

meta-ontology that can be particularized in function of concrete applications.

Fig. 4. Implementation of the Ontology (class hierarchy) in Protégé 4 [17].

The current version of the implementation is modeled according to a bottom-up

schema for knowledge building (Figure 3). An extensible set of inferred properties

and concepts is also provided. An exhaustive description of the implementation is out

of paper scope.

The Ontology was validated using OwlSight [18] as showed in Figure 5.

5 Conclusions

Semantic Technologies propose an evolving extension of interaction and data

processing models integrating common models for knowledge representation with

formal description of semantic or meaning of information.

The most modern semantic technologies enable an innovative technologic envi-

ronment in which systems can interact among them interchanging semantic data in a

context of semantic interoperability.

Fig. 5. Validation of the Ontology using OWLSight [18].

These novel interaction models allow the engineering of advanced semantic actors

built on standardized technologies as well as an innovative vision of systems and

related applications.

Regardless by concrete technologies or languages, Semantic Knowledge building

can be considered as the most creative and critical issue for the concrete realization of

semantic environments.

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