Int. J. Ad Hoc and Ubiquitous Computing, Vol. 2, No. 4, 2007 263
Copyright © 2007 Inderscience Enterprises Ltd.
A survey on context-aware systems
Matthias Baldauf
V-Research, Industrial Research and Development,
Stadtstrasse 33, 6850 Dornbirn, Austria
E-mail: matthias.baldauf@v-research.at
Schahram Dustdar* and Florian Rosenberg
Distributed Systems Group, Information Systems Institute,
Vienna University of Technology, Argentinierstrasse 8/184-1, 1040 Vienna, Austria
E-mail: dustdar@infosys.tuwien.ac.at E-mail: rosenberg@infosys.tuwien.ac.at
*Corresponding author
Abstract: Context-aware systems offer entirely new opportunities for application developers and
for end users by gathering context data and adapting systems behaviour accordingly. Especially
in combination with mobile devices these mechanisms are of high value and are used to
increase usability tremendously. In this paper, we present common architecture principles of
context-aware systems and derive a layered conceptual design framework to explain the different
elements common to most context-aware architectures. Based on these design principles, we
introduce various existing context-aware systems focusing on context-aware middleware and
frameworks, which ease the development of context-aware applications. We discuss various
approaches and analyse important aspects in context-aware computing on the basis of the
presented systems.
Keywords: context-awareness; context framework; context middleware; sensors; context model;
context ontology; context-aware services.
Reference to this paper should be made as follows: Baldauf, M., Dustdar, S. and Rosenberg, F.
(2007) ‘A survey on context-aware systems’, Int. J. Ad Hoc and Ubiquitous Computing, Vol. 2,
No. 4, pp.263–277.
Biographical notes: Matthias Baldauf is project manager at V-Research, an Austrian
competence center for industrial research and development. In the Department of Technical
Logistics he develops location-aware systems based on GPS, GSM and RFID technology with a
focus on track and trace solutions. His research interests include modern localisation methods and
efficient, flexible localisation architectures.
Schahram Dustdar is a Full Professor of Computer Science with a focus on Internet Technologies
at the Distributed Systems Group, Information Systems Institute, Vienna University of
Technology (TU Wien). In 1999 he co-founded Caramba Labs Software AG (CarambaLabs.com)
in Vienna, a venture capital co-funded software company focused on software for collaborative
processes in teams. Caramba Labs was nominated for several (international and national) awards.
He has published some 100 scientific papers as conference-, journal-, and book contributions.
He has written three academic books, one professional book, and co-edited six
books/proceedings. More information can be found at: http://www.infosys.tuwien.ac.at/Staff/sd.
Florian Rosenberg is research assistant and PhD student at the Distributed Systems Group,
Information Systems Institute, Vienna University of Technology. His research areas include
context-aware and autonomic services, service-oriented architectures and web service
technologies. More information can be found at: http://www.infosys.tuwien.ac.at/Staff/rosenberg.
1 Introduction
With the appearance and penetration of mobile devices such
as notebooks, PDAs, and smart phones, pervasive
(or ubiquitous) systems are becoming increasingly popular
these days. The term ‘pervasive’ introduced first by Weiser
(1991) refers to the seamless integration of devices into
the users everyday life. Appliances should vanish into the
background to make the user and his tasks the central focus
rather than computing devices and technical issues.
One field in the wide range of pervasive computing are
the so-called context-aware (or sentient) systems.
Context-aware systems are able to adapt their operations to
the current context without explicit user intervention and
thus aim at increasing usability and effectiveness by taking
environmental context into account. Particularly when it

264 M. Baldauf, S. Dustdar and F. Rosenberg
comes to using mobile devices, it is desirable that programs
and services react specifically to their current location, time
and other environment attributes and adapt their behaviour
according to the changing circumstances as context data
may change rapidly. The needed context information may
be retrieved in a variety of ways, such as applying sensors,
network information, device status, browsing user profiles
and using other sources. The history of context-aware
systems started when Want et al. (1992) introduced
their Active Badge Location System which is considered to
be one of the first context-aware applications. The infrared
technology based system is able to determine a user’s
current location which was used to forward phone calls to a
telephone close to the user. In the middle of the 1990s,
a couple of location-aware tour guides (Abowd et al., 1997;
Sumi et al., 1998; Cheverst et al., 2000) emerged which
provided information according to the user’s current
location. While location information is by far the most
frequently used attribute of context, attempts to use other
context information as well have grown over the last few
years as the examples in this paper will show. Hence,
it is a challenging task to define the word ‘context’
and many researchers tried to find their own definition for
what context actually includes. In literature the term
context-aware appeared in Schilit and Theimer (1994) the
first time. There the authors describe context as location,
identities of nearby people, objects and changes to those
objects. Such enumerations of context examples were often
used in the beginning of context-aware systems research.
Ryan et al. (1997) referred to context as the user’s location,
environment, identity and time. Dey (1998) defines context
as the user’s emotional state, focus of attention, location and
orientation, date and time, as well as objects and people in
the user’s environment. Another common way of defining
context was the use of synonyms. Hull et al. (1997) describe
context as the aspects of the current situation. These kind of
definitions are often too wide. However, a good one can be
found in Brown (1996). Brown defines context to be the
elements of the user’s environment which the computer
knows about. One of the most accurate definitions is
given by Dey and Abowd (2000b). These authors refer to
context as
“any information that can be used
to characterize the situation of entities
(i.e., whether a person, place or object)
that are considered relevant to the interaction
between a user and an application, including
the user and the application themselves.”
One popular way to classify context instances is the
distinction of different context dimensions. Prekop and
Burnett (2003) and Gustavsen (2002) call these dimensions
external and internal, and Hofer et al. (2002) refer to
xtitphysical and logical context. The external (physical)
dimension refers to context that can be measured by
hardware sensors, i.e., location, light, sound, movement,
touch, temperature or air pressure, whereas the internal
(logical) dimension is mostly specified by the user or
captured by monitoring user interactions, i.e., the user’s
goals, tasks, work context, business processes, the user’s
emotional state. Most context-aware systems make use of
external context factors as they provide useful data, such as
location information. Furthermore, external attributes are
easy to sense by using off-the-shelf sensing technologies.
Virtually all systems presented in this paper apply physical
context information. Examples for the use of logical
data are the Watson Project (Budzik and Hammond, 2000)
and the IntelliZap Project (Finkelstein et al., 2001) which
support the user by providing relevant information due to
information read out of opened web pages, documents, etc.
When dealing with context, three entities can be
distinguished (Dey and Abowd, 2001): places (rooms,
buildings etc.), people (individuals, groups) and things
(physical objects, computer components etc.). Each of these
entities may be described by various attributes which can be
classified into four categories: identity (each entity has a
unique identifier), location (an entity’s position, co-location,
proximity etc.), status (or activity, meaning the intrinsic
properties of an entity, e.g., temperature and lightning for a
room, processes running currently on a device etc.) and time
(used for timestamps to accurately define situation, ordering
events etc.).
This paper is structured as follows. Section 2 introduces
current design principles for context-aware systems and
common context models used in various context-aware
systems. In Section 3, we present a comparison of existent
context-aware systems and explain approaches, varieties
and similarities. In Section 4, we discuss the presented
approaches, and highlight advantages and disadvantages.
Finally, Section 5 draws some concluding remarks and
presents some future work in this area.
2 Design principles
In this section, we describe basic design principles and
introduce a conceptually layered framework, to associate the
functionality implemented in existent frameworks to
various layers. Furthermore, we depict different context
models used for representing, storing and exchanging
contextual information.
2.1 Architecture
Context-aware systems can be implemented in many ways.
The approach depends on special requirements and
conditions such as the location of sensors (local or remote),
the amount of possible users (one user or many), the
available resources of the used devices (high-end-PCs or
small mobile devices) or the facility of a further extension
of the system. Furthermore, the method of context-data
acquisition is very important when designing context-aware
systems because it predefines the architectural style of the
system at least to some extent. Chen (2004) presents three
different approaches on how to acquire contextual
information.