Context-aware systems offer entirely new opportunities for application developers and for end users by gathering context data and adapting systems behavior accordingly. Especially in combination with mobile devices these mechanisms are of high value and are used to increase usability tremendously. In this pap er, 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 analyze important aspects in context-aware computing on the basis of the presented systems.

The development of context-aware applications will require tools that are based on clearly defined models of context and system software architecture. This essay introduces models for each of these, examines the tradeoffs among the different alternatives, and describes a blackboard-based context architecture that is being used in the construction of interactive workspaces. 1.

Many "ubiquitous computing" applications need a constant flow of information about their environment to be able to adapt to their changing context. To support these "context-aware" applications we propose a graph-based abstraction for collecting, aggregating, and disseminating context information. The abstraction models context information as events, produced by sources and flowing through a directed acyclic graph of event-processing operators and delivered to subscribing applications. Applications describe their desired event stream as a tree of operators that aggregate low-level context information published by existing sources into the high-level context information needed by the application. The operator graph is thus the dynamic combination of all applications' subscription trees.

Pervasive computing applications are increasingly leveraging contextual information from several sources to provide users with behavior appropriate to the environment in which they reside. If these sources of contextual information are used and deployed in an ad hoc manner, however, they may provide overlapping functionality, fail to provide needed functionality, and require the use of inconsistent interfaces by applications. To overcome these problems, we introduce a Contextual Information Service that provides applications with contextual information via a virtual database. Unlike previous efforts, our service provides applications a consistent, lightweight, and powerful mechanism for obtaining contextual information, and includes explicit support for the on demand computation of contextual information. We show, via example applications and a Contextual Information Service prototype that we have implemented, how this approach can be used to allow proactive applications to adapt their behavior to match a user’s current environment.

Emerging pervasive computing technologies transform the way we live and work by embedding computation in our surrounding environment. To avoid increasing complexity, and allow the user to concentrate on her tasks, applications in a pervasive computing environment must automatically adapt to their changing context, including the user state and the physical and computational environment in which they run. Solar is a middleware platform to help these "context-aware" applications aggregate desired context fom heterogeneous sources and to locate environmental services depending on the current context. By moving most of the context computation into the infrastructure, Solar allows applications to run on thin mobile clients more effectively. By providing an open framework to enable dynamic injection of context processing modules, Solar shares these modules across many applications, reducing application development cost and network traffic. By distributing these modules across network nodes and reconfiguring the distribution at runtime, Solar achieves parallelism and online load balancing.

In this paper we motivate a Context Fusion Network (CFN), an infrastructure model that allows contextaware applications to select distributed data sources and compose them with customized data-fusion operators into a directed acyclic information fusion graph. Such a graph represents how an application computes high-level understandings of its execution context from low-level sensory data. Multiple graphs by different applications inter-connect with each other to form a global graph. A key advantage of a CFN is re-usability, both at code-level and instance-level, facilitated by operator composition. We designed and implemented a distributed CFN system, Solar, which maps the logical operator graph representation onto a set of overlay hosts. In particular, Solar meets the challenges inherent to heterogeneous and volatile ubicomp environments. By abstracting most complexities into the infrastructure, Solar facilitates both the development and deployment of context-aware applications. We present the operator composition model, basic services of the Solar overlay network, and programming support for the developers. We also discuss some applications built with Solar and the lessons we learned from our experience. 1

Abstract — Today’s mobile applications require constant adaptation to their changing environments, or contexts. Technological advancements have increased the pervasiveness of mobile computing devices such as laptops, handhelds, and embedded sensors. The sheer amount of context information available for adaptation places a heightened burden on application developers as they must manage and utilize vast amounts of data from diverse sources. Facilitating programming in this datarich environment requires a middleware that provides context information to applications in an abstract form. In this paper, we demonstrate the feasibility of such a middleware that allows programmers to focus on high-level interactions among programs and to employ declarative abstract specifications of context in settings that exhibit transient interactions with opportunistically encountered components. We also discuss the novel context-aware abstractions the middleware provides and the programming knowledge necessary to write applications using our middleware. Finally, we provide examples demonstrating the flexibility of the infrastructure and its ability to support differing tasks from a wide variety of application domains. I.

Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware of and adapt to their changing contexts in highly dynamic environments. Today, building context-aware systems is a complex task due to lack of an appropriate infrastructure support in intelligent environments. A context-aware infrastructure requires an appropriate context model to represent, manipulate and access context information. In this paper, we propose a formal context model based on ontology using OWL to address issues including semantic context representation, context reasoning and knowledge sharing, context classification, context dependency and quality of context. The main benefit of this model is the ability to reason about various contexts. Based on our context model, we also present a Service-Oriented Context-Aware Middleware (SOCAM) architecture for building of context-aware services.

Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware and adapt to highly dynamic environments. Today, building context-aware mobile services is a complex and timeconsuming task. In this paper, we present a Service-oriented Context-Aware Middleware (SOCAM) architecture for the building and rapid prototyping of context-aware mobile services. We propose an ontology-based approach to model various contexts. Our context model supports semantic representation, context reasoning and context knowledge sharing. We take a service-oriented approach to build our middleware which supports tasks including acquiring, discovering, interpreting, accessing various contexts and interoperability between different context-aware systems.

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