Developing context-aware pervasive computing applications: Models and approach☆
Section snippets
Motivation
It is well known that pervasive computing introduces a set of design challenges that are not present in traditional desktop computing. In particular, it requires applications that are capable of operating in highly dynamic environments and placing minimal demands on user attention. Context-aware applications aim to meet these requirements by adapting to selected aspects of the context of use, such as the current location, time and user activities.
In recent years, a variety of prototypical
Context modelling techniques
Recent research in the field of context-awareness has predominantly adopted an infrastructure-centred approach; that is, it has assumed that the complexity of engineering context-aware applications can be substantially reduced solely through the use of infrastructure capable of gathering, managing and disseminating context information to applications that require it. In line with this approach, a variety of solutions that acquire and interpret context information from sensors, and manage
Preference model
Appropriate context modelling techniques are a necessary, but not sufficient, prerequisite to managing the complexity involved in engineering context-aware applications. In all but the most trivial applications, additional tools are also desirable to support the decision-making process involved in mapping the context to appropriate application behaviours. This process is complicated by well known usability challenges associated with context-awareness, such as those related to predictability and
Programming models
Suitable programming models are crucial in helping to limit the complexity and effort involved in implementing context-aware applications; however, progress in developing new models has been slow. Although context servers are now frequently used for acquiring and managing context information, most applications do not make use of any form of support (for instance, programming toolkits or infrastructure) for interpreting and making decisions about context. In general, context-aware software is
Software infrastructure
We have implemented a software infrastructure incorporating our programming toolkit and support for related tasks, such as management of context information. In this section, we present an overview of the architecture and implementation.
The infrastructure is organised into loosely coupled layers as shown in Fig. 6. The context gathering layer acquires context information from sensors and then processes this information, through interpretation and data fusion (aggregation), to bridge the gap
Software engineering methodology
The models and infrastructure that we have presented are designed to support a wide variety of software engineering tasks. In this section, we outline the process that is generally followed when building a context-aware application using these tools. This process was abstracted from our experiences with building several context-aware applications, some of which are described in Section 7.
Fig. 7 illustrates our generic software engineering process graphically. The steps can be partitioned into
Case study: Context-aware communication
As a means of validating our models and infrastructure, we carried out a case study in which we built a context-aware communication tool. This section presents the objectives, design and outcomes of this study. Since completing the case study, we have further demonstrated the value of our approach by applying it to a variety of applications, some of which are briefly outlined in Section 7.3.3. A full discussion of these applications is beyond the scope of this paper; however, we refer the
Concluding remarks
This paper presented a set of conceptual models designed to facilitate the development of context-aware applications by introducing greater structure and improved opportunities for tool support into the software engineering process. As the evaluation in the previous section showed, our models and approach lead to applications that are maintainable, evolvable and based upon a set of reusable foundations, such as context definitions and context processing components. Further, they support a high
Acknowledgements
We gratefully acknowledge the contributions of Ted McFadden, Sasitharan Balasubramaniam, Peter Mascaro and Jessica Purser to the applications developed following our initial case study, which were described briefly in Section 7.3.3.
Karen Henricksen is a research scientist in the pervasive computing group at the Collaborative Research Centre for Enterprise Distributed Systems Technology (DSTC). Her research interests include context modelling and management, development and evaluation of context-aware applications, and privacy issues in pervasive computing. She received her Ph.D. in the area of context-aware pervasive computing from the University of Queensland in 2004. Contact her at [email protected].
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Karen Henricksen is a research scientist in the pervasive computing group at the Collaborative Research Centre for Enterprise Distributed Systems Technology (DSTC). Her research interests include context modelling and management, development and evaluation of context-aware applications, and privacy issues in pervasive computing. She received her Ph.D. in the area of context-aware pervasive computing from the University of Queensland in 2004. Contact her at [email protected].
Jadwiga Indulska is an associate professor in the School of Information Technology and Electrical Engineering at The University of Queensland. Her research interests include pervasive/ubiquitous computing, autonomic networks, mobile computing, distributed computing and high speed networks. In the past she led projects on pervasive and autonomic environments in the Collaborative Research Centre for Enterprise Distributed Systems Technology and currently leads a project on context-awareness and autonomic networks in National ICT Australia. She is a member of the IEEE Computer Society and the ACM. Contact her at [email protected], www.itee.uq.edu.au/~jaga.
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The work reported in this paper has been funded in part by the Co-operative Research Centre for Enterprise Distributed Systems Technology (DSTC) through the Australian Federal Government’s CRC Programme (Department of Education, Science, and Training).