=Paper=
{{Paper
|id=Vol-210/paper-6
|storemode=property
|title=A Context Information Manager for Pervasive Environments
|pdfUrl=https://ceur-ws.org/Vol-210/paper6.pdf
|volume=Vol-210
|dblpUrl=https://dblp.org/rec/conf/ecai/EuzenatPR06
}}
==A Context Information Manager for Pervasive Environments==
https://ceur-ws.org/Vol-210/paper6.pdf
A Context Information Manager
for Pervasive Computing Environments
Jérôme Euzenat1, Jérôme Pierson2, Fano Ramparany2
Abstract. In a pervasive computing environment, heterogeneous Although, several scientific domains have considered the
devices need to communicate in order to provide services adapted notion of context, the standpoints from which this notion is
to the situation of users. So, they need to assess this situation as considered are different: in pervasive computing, the context of an
their context. We have developed an extensible context model application in terms of its physical parameters has been especially
using semantic web technologies and a context information considered ; in human-computer communication, the context is
management component that enable the interaction between most often the user task and the history of its dialogue with the
context information producer devices and context information computer [4]; in artificial intelligence, the context is rather
consumer devices and as well as their insertion in an open considered as the conditions of validity of an assertion [14].
environment.
2.1 Context in pervasive computing
1 INTRODUCTION1
In pervasive computing, the physical context is of the utmost
In a pervasive computing environment, various basic services can importance. In general, it is acquired through sensor data. These
be provided by smart devices (e.g., sensors, actuators, human- data are further elaborated into context characterization adapted to
computer interface). More advanced services can be provided when their use (for instance « high temperature » for some air
they act together and cooperate, but smarter services can only be conditioning controller). With regard to the sensor data (a
achieved if the devices could adapt their behaviour to the user, temperature), the information has been weakened (i.e., made less
his/her preference and his/her task, than if users have to find the precise) but is more adapted.
specific service they want among all the smart devices. The various definitions of context in pervasive computing are
This idea requires the perception of the environment in which very often related to an application or a particular domain [6, 15].
devices and users interact. There are pieces of information that can The drawback of this characterization is its reliance on the task:
be considered common to all services. In particular, spatial and « high temperature » is not an absolute characterization. It depends
temporal location as well as information related to the physical on the use of the room (a sauna or a sleeping room). More than
environment in which services are made available [1, 2]. These context, pervasive computing tends to manipulate a
elements are part of the context in which applications operate. We characterization of the context in the perspective of an application.
are here concerned with context-aware applications, i.e., As a consequence, it is difficult to dynamically implement st non
applications whose behaviour is determined to some extent by the expected applications with the characterization of context made for
Privacy/Tru
context. another one. Web
Our goal is to design a context management system general
enough to be used by different pervasive computing applications,
specific enough for encompassing existing services and Exploitation Discovery
applications, and flexible enough for supporting the dynamic
History
addition of new devices. Context identification
First we introduce our proposal for a distributed architecture Perception (symbolic)
that manages context information (Section 2), then we define a Sensors (numeric)
context representation (Section 3) which is independent of
applications and an architecture enabling their evolution. The
Figure 1: Model for context in pervasive computing. Data coming from
openness of the system will require dealing with heterogeneous
sensors are aggregated and elaborated into the context used by applications
representations that will have to be reconciled before being used (from[7]). This paper does not consider the orthogonal aspects (discovery,
(section 4). For that purpose, we will take advantage of solutions history and security).
developed for the “semantic web”.
However, multi-application context modelling is now
understood in pervasive computing [7] and raises the issue of
2 CONTEXTS considering context independently from applications. Figure 1
Context is the set of information (partly) characterizing the shows the way to progressively elaborate context information from
situation of some entity [5]. The notion of context is not universal sensors to applications. We will follow this approach and this paper
but relative to some situation [15, 11]. This can be a physical details the content of the perception and situation layers so that
situation (as the spatio-temporal location of some person) or they can support the dynamic nature of the environment (new
functional (as the current task of the person). sensors and applications appear and disappear).
1
INRIA Rhône-Alpes, France
2
France Telecom R&D, France
2.2 Contexts in artificial intelligence 3 A CONTEXT INFORMATION
In artificial intelligence, the notion of context is, in general, MANAGEMENT COMPONENT
concerned with the representation of information. It is used for Pervasive Computing applications retrieve context data directly or
accounting for two phenomena: the context of validity of indirectly from sensors, which are grounded in the physical
information [16] and the efficiency of reasoning in narrower environment. We propose an architecture in which applications do
contexts [1]. not need to directly connect to each sensor available and where
John McCarthy [17] proposed a formalization of context adding a new sensor does not require all applications to be
based on context « reification » as well as the « meta-predicate » ist, recompiled and redeployed.
ist(p,c) meaning that assertion p is true in context c. The
approaches of context in artificial intelligence allow grouping
knowledge in micro-theories [1] and to reason within those. In this 3.1 Architecture
framework (that of Cyc), the context is a more precise frame for
interpreting information. This kind of approach can be used in Designing an architecture for hosting context-aware services,
pervasive computing in order to integrate and interpret data suggests the development of a context management service for
provided by sensors. Taking advantage of the theory associated providing other services or devices with context information [6, 7,
with the sensor enables reducing the ambiguity of the data it 11]. We have identified several alternative approaches for
delivers. In that view, raw data issued from sensors, are generally designing the target architecture. The first approach lets
not weakened but rather enriched (and aggregated with other applications directly communicate with sensors they have an
information sources allowing to further precise their interpretation). interest in. This approach requires applications to know in advance
[14] describes the way to express this kind of context within the who they need to communicate with to get the information they
semantic web by providing each triple information on its origin need. Furthermore it adds complexity to the process of information
(« quad »). The same model is implemented in modern RDF aggregation, as this process should then be handled by the
managers [2]. applications themselves and overloads sensors activity. Finally this
Although work from McCarthy and Guha consider contexts as approach makes it difficult to insert new sensors into the
independent theories related to some particular knowledge field, environment and thus doesn't comply with our flexibility
Fausto Giunchiglia instead considers contexts as concurrent requirement.
viewpoints on the same information. He expresses the relations In the framework of service oriented architectures, the second
between contexts as « mappings » used for importing information approach consists of building a context management service [4]
under some context into another. This approach can be useful in whose job is to collect sensors information and forward this
pervasive computing when several information sources provide information to applications that need it. This approach makes it
comparable information. These works found their way within possible to gather sensor information in a single place so that
semantic web tools through the C-OWL language [18]. A information could be easily aggregated. For example, a system that
comparison of both approaches is made in [19]. provides local temperature and atmospheric is very useful in a
home environment. At a city level, the same information is useful;
however it doesn't need the same degree of precision. The
2.3 Synthesis drawback of such a system is that it centralizes the management of
context information, which is contradictory to the concept of
In summary, pervasive computing tends to consider context as context. More specifically, this system provides information about
what characterizes the situation while artificial intelligence rather the activity environment (a special case of context information),
characterizes the information itself. More notably, Pervasive however this information is not contextual as it is independent of
computing very often deals with the particular context of an the current task or situation, i.e. that of the client application.
application while artificial intelligence determines the context in Moreover, with such a system, the scope of context management
function of the information source. In pervasive computing, would be efficient in a limited area only.
information coming from sources is very often weakened in order We have adopted a third approach in which each device or
to fit the application needs while artificial intelligence tends to service embeds a context management component (CMC) for
enrich it with further information. maintaining context information for its own use or for the benefit
Of course, these approaches are rather complementary than of others (Figure 2). The main advantage of this approach is that
competitors. In general, raw data can go through weakening and new devices can join online or leave, without having to recompile
enrichment, thus bridging both approaches. or reinitialize any part of the whole environment. This component
In pervasive computing, upgrading the environment is not an provides mechanisms for helping context-aware devices to request
option: the environment must be designed from scratch in order to context information from context sensitive devices.
evolve. Our goal is to contribute to dealing with the dynamic
evolution of context [7]. For that purpose, we design an
architecture supporting the introduction of new context elements
(provided from some new device) and the introduction of new
applications without interruption of the environment.
This component-based context management architecture relies
on a context modelling formalism based on semantic web
technologies. We demonstrate how they can be used to
dynamically extend the environment.
Web
CMC . Privacy/Tru
st Alignment
Service
.
CMC
CMC
CMC CMC . CMC
.
. . CMC
.
Figure 2: Each device embeds a context management component (CMC) and a semantic description of its context.
implement the open world assumption under which it is always
3.2 Interaction possible to add more information to a context characterization; and
Applications should be able to query context information they are they have been designed to work in a networked way.
interested in and some services should be able to provide context
information, such as aggregated context information to other devi-
ces. For this purpose we design a protocol that makes the best of 4.1 Context model and language
available services. We need to be able to identify a service, to In this dynamic pervasive computing environment, each CMC
know what kind of context information it could provide and to manages context information of its device. To express its context
interact with it to get access to this information. Thus the context model, its needs or its capabilities, we use semantic web languages.
management component provides a few methods. In our They ensure interoperability between these heterogeneous devices.
description the first element is the query, the second is the response The ground language for the semantic web is RDF (Resource
type: Description Framework [8]). It enables expressing assertions of the
form subject-predicate-object. The strength of RDF is that the
Id() -> URI: The identifier of the service; names of entities (subjects, predicates or objects) are URIs (the
Cl(URI) -> URI: The class of the identified service; identifiers of the web that can be seen as a generalization of URLs:
Desc(URI) -> OWL: The description of the information that http://www.w3c.org/sw). This opens the possibility for different
the component can provide; RDF documents to refer precisely to an entity (it is reasonable to
Req(RDQL) -> RDF. assume that a URI denotes the same thing for all of its users).
The OWL language [9], has been designed for expressing
The first method allows identifying devices that are available in the « ontologies » or conceptual models of a domain of knowledge. It
environment. The identifier can then be used to contact the device. constrains the interpretation of RDF graphs concerning this domain.
Alternatively, it could be used to get a more detailed description of OWL defines classes of objects and predicates and makes it
the device (e.g., in case the identifier is a URI pointing to a possible to declare constraints applying to them (i.e., that the
network location where a description of the identified object is « output » of a « thermometer » is a « temperature »).
stored). A second method identifies the class (in OWL terminology) The context model that we use at that stage is very simple: a
of the device. In theory, this class should be accessible from the context is a set of RDF assertions. Interoperability is guaranteed
network and once its definition is found, it provides a detailed through considering that context-aware devices are consumers and
description of the device. A third method provides the device producers of RDF. However, this is not precise enough and devices
description (or rather that of context information they provide) in may want to extract only the relevant information from context
an OWL formalism (OWL-S). A fourth method is used to post sources. For that purpose, a language like RDQL [10] is useful for
queries to the devices and to get the context information returned. querying or subscribing to context sources. In order to post the
Thus any device is able to: find out, in its environment, relevant queries to the adequate components, it is necessary that
services that are able to provide information relevant to its own components publish the OWL classes of objects and properties on
context, get features of services that have been found (for example, which they can answer.
measurement precision), connect to the selected service to get the
information sought.
We need a language to describe the context model of 4.2 Why ontologies?
heterogeneous devices so that these devices can interact in a
dynamic environment. If we can add components at any time, they may not be easily
usable. Indeed, there is no a priori reason that components
available, new applications and new sensors are compatible.
4 OPENESS, DYNAMICS AND Fortunately, knowledge representation techniques, and namely the
HETEROGENITY open world assumption, makes it possible to introduce new device
specifications in the environment by extending the ontology,
The languages developed for the semantic web, and particularly through specifying a new concept or a property. Using ontologies
RDF and OWL, are adapted to context representation in pervasive to characterize the situations permits new equipment whose
computing and particularly to the representation of dynamically capabilities have not been known at the beginning to enter and new
evolving contexts for two reasons: these languages are open: they applications to benefit from these possibilities. The applications
must be as general as possible describing the information they need 5 RELATED WORKS
whereas the context management system must be as precise as
possible on the information it makes available. This approach In pervasive computing, it is largely recognized that handling
enables the most specialized applications to take advantage of context information is essential. As we presented, there are many
CMCs. The essential point is to have sufficiently generic different management systems for context information. The one
ontologies to cover the various concepts implied in pervasive which is the nearest to what we presented here is the work on
computing applications [12]. contextors [11]. It proposes a library of elements able to provide
context information: it makes it possible to combine contextual
information on a distributed mode. On the other hand, this system
4.3 Taking advantage of heterogeneous resources does not establish how to dynamically add devices without
stopping the system or other devices. Regarding to the use of the
The context management system we propose makes it possible to semantic Web technologies to represent context, there are several
introduce new devices in the environment by extending the proposals to extend the languages of the semantic Web in order to
ontologies in such a way that existing applications can make the contextualize the assertions [14, 19, 2]. With regard to the use of
best use of them. However, this view holds if all parties share the OWL to represent the context information, [12] introduces a high
same ontology. level ontology of contextual information for pervasive computing.
Unfortunately this is not always the case and agreeing on
standard, universal and self contained context ontology is not a
reasonable assumption. This raises the issue of matching context 6 CONCLUSION AND PERSPECTIVES
information with applications context information requirements.
There are three alternative approaches addressing interoperability We specifically addressed the problem of adaptability of context
in pervasive computing environments: (i) A priori standardisation management to an ever-evolving world. This is achieved by
of ontologies, (ii) setting up mediators among ontologies and (iii) a providing a distributed component-based architecture and by using
dynamical ontology matching service. These three approaches are semantic web technologies. Components enable the addition, at
not incompatible and might even be jointly used. For example any moment, of new devices that can provide information about the
parties could agree on sharing common high level ontologies. context of applications. The use of RDF and OWL ensures
Letting more specific level ontology evolve freely and interoperability between components developed independently by
independently is a strategy enabling a close account for a fast taking advantage of the open character of these technologies.
evolving domain. Moreover, using ontology alignment modules allows dealing with
As ontologies, matching services should be available for the necessary heterogeneity between components. The proposed
applications and context managers through network access. They approach relies on a minimal commitment on basic technologies:
provide an interface that allows the explicit handling of ontologies RDF, OWL, and some identification protocol.
alignments developed in the framework of the semantic web [20]. We are currently developing a demonstrator of this
We propose to set up one (or more) ontology matching service(s) technology. It consists of a toolkit for developers of pervasive
(Figure 3). The goal of such services is to help agents (context applications which help them deploy a distributed context
managers in our case) to find a matching between different management system. This toolkit provides a component for
ontologies. These services provide mechanisms for finding out managing (searching, broadcasting and updating) context
ontologies close to a given ontology, archiving (and retrieving) information.
past alignments, dynamically computing matching between two
ontologies and translating queries and responses to queries between
context managers that use different ontologies [13].
Physical Context
isA Context isA
isA isA
Temperature Brig htness WEB Ho use
Context
Room Context
isA
isA isA
Temperature °C Temperature °F Temperature °K isA isA isA
Avera ge Time Resident
Temperature
⌠
Ph ysical Avera ge
context Temperature
Alignment
isA
Service isA
isA
myRo omTemperature
Temperature Brightness
>>
Figure 3: For finding correspondence between its model and the model of the context information provider, the window service asks to an alignment service
to translate his model to another device model.
ACKNOWLEDGEMENTS
Fano Ramparany and Jérôme Pierson are partially supported by the
European project Amigo (IST-2004-004182); Jérôme Euzenat is
partially supported by the European network of excellence
Knowledge Web (IST-2004-507482).
REFERENCES
[1] R. Guha, Contexts: a formalization and some applications, PhD thesis,
Stanford university (CA US), 1991 (Technical Report STAN-CS-91-
1399-Thesis et MCC ACT-CYC-423-91).
[2] O. Khriyenko, V. Terziyan, Context description framework for the
semantic web, Proceedings Context 2005 Context representation and
reasoning workshop, Paris (FR), 2005
[3] A. Dey, D. Salber, G. Abowd, A conceptual framework and a toolkit
for supporting the rapid prototyping of context-aware applications,
Human-Computer Interaction 16:97-166., 2001
[4] P. Dourish, Seeking a foundation for context-aware computing,
Human-Computer Interaction, 16(2-3), 2001.
[5] M. Chalmers, A Historical View of Context, Computer supported
cooperative work 13(3), 223-247, 2004.
[6] A. Dey, Understanding and using context, Personal and ubiquitous
computing 5(1):4-7, 2001.
[7] J. Coutaz, J. Crowley, S. Dobson, D. Garlan, Context is key,
Communications of the ACM 48(3):49-53, 2005.
[8] G. Klyne, J. Carroll, Eds., Resource Description Framework (RDF):
Concepts and Abstract Syntax, W3C Recommendation, 2004
http://www.w3.org/TR/rdf-concepts/
[9] M. Dean, G. Schreiber Eds, OWL Web Ontology Language:
Reference, W3C Recommendation, 2004. http://www.w3.org/TR/owl-
ref/
[10] A. Seaborne, RDQL — A Query Language for RDF, W3C Member
submission, 2004. http://www.w3.org/Submission/2004/SUBM-
RDQL-20040109/
[11] J. Crowley, J. Coutaz, G. Rey, P. Reignier, Perceptual components for
context aware computing, Proceedings International Conference on
Ubiquitous Computing, Göteborg (SW), pp. 117-134, 2002.
[12] X. H. Wang, D. Q. Zhang, T. Gu, H. Keng Pung, Ontology based
context modeling and reasoning in OWL, Proceedings 2nd
International conference on pervasive computing and communication
Workshop on Context Modeling and Reasoning (CoMoRea), Orlando
(FL US), 2000.
[13] J. Euzenat, Alignment infrastructure for ontology mediation and other
applications, Proceedings ICSOC 1st international workshop on
Mediation in semantic web services, pp.81-95, Amsterdam (NL),
2005.
[14] R. Guha, R. Fikes, R. McCool, Contexts for the Semantic Web,
Proceedings 3rd ISWC, Hiroshima (JP), LNCS 3298:32-46, 2004.
[15] H. Chen, T. Finin, A. Joshi, An Ontology for Context-Aware
Pervasive Computing Environments, Knowledge engineering review
18(3):197-207, 2004.
[16] J. de Kleer, An assumption-based TMS, Artificial Intelligence
28(2):127-162, 1986.
[17] J. McCarthy, Notes on formalizing context, Proceedings 13th IJCAI,
Chambéry (FR), pp. 555-560, 1993.
[18] P. Bouquet, F. Giunchiglia, F. van Harmelen, L. Serafini, H.
Stuckenschmidt, C-OWL: contextualizing ontologies, Proceedings 2nd
ISWC, Sanibel Island (FL US), LNCS 2870:164-179, 2003
[19] L. Serafini, P. Bouquet, Comparing formal theories of context in AI,
Artificial Intelligence 155:1-67, 2004.
[20] J. Euzenat, An API for ontology alignment, Proceedings 3rd ISWC,
Hiroshima (JP), LNCS 3298:698-712, 2004.