Service platform for rapid development and deployment of context-aware, mobile applications

Pokraev, Stanislav; Koolwaaij, Johan; Setten, Mark van; Broens, Tom; Costa, Paolo; Wibbels, Martin; Ebben, Peter; Strating, P.

doi:10.1109/icws.2005.106

Cited by 14 publications

(9 citation statements)

References 9 publications

(7 reference statements)

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“…A more recent approach defined by Pokraev et al [12] proposes a Web Services platform for the rapid development and deployment of context-aware mobile applications. The platform is built with SoC in mind, since the platform's context-aware components are developed separately from the implementation of the application's logic.…”

Section: Related Workmentioning

confidence: 99%

Context-aware media player (CaMP): Developing context-aware applications with separation of concerns

Paspallis

Achilleos

Kakousis

et al. 2010

2010 IEEE Globecom Workshops

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The constant advent of powerful mobile devices has raised the potential of building novel context-aware applications. These applications let the users enjoy a better experience by sensing their context and automating tasks that would otherwise require significant user attention. This paper presents two context-aware applications, built on top of the MUSIC middleware's context management framework. By describing the development steps, we reveal how development is facilitated via Separation of Concerns and how code reuse is enabled in terms of reusable context plug-ins. It is argued that this approach reduces the required development and maintenance effort and thus lowers the associated cost.

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Section: Related Workmentioning

confidence: 99%

Context-aware media player (CaMP): Developing context-aware applications with separation of concerns

Paspallis

Achilleos

Kakousis

et al. 2010

2010 IEEE Globecom Workshops

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“…However, it is not clarified if it is rule-based reasoning or not. Ontology models are used to model the context and POIs, and the information presented to the user on the smart phone is simultaneously updated [3,8,13,14].…”

Section: Compassmentioning

confidence: 99%

“…COMPASS uses several prediction methods, such as social filtering [15], case-based reasoning, and category learning [14,16].…”

Section: Compassmentioning

confidence: 99%

Matching points of interest with user context: an ANN approach

Yılmaz¹

2017

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, the design and development of an artificial neural network (ANN) for similarity value calculation in a context-aware system is proposed. This neural network is used by the neural agent of the iConAwa system. Since iConAwa is an intelligent, context-aware, multiagent system, it provides mobile users with context-aware information and services, and also provides communication with each other. Context and points of interest are modeled in a flexible and extensible way by using ontologies. iConAwa derives high-level implicit context from low-level explicit context by inference performed over the context ontology. This approach decouples context reasoning from the source code of the system. With the addition of a neural agent, which uses an ANN, the system has learning capability. By using a neural network for similarity value calculation, the system can adapt to the needs of different people. System owners can introduce their own similarity metric considering their own requirements, which further improves the iConAwa system. Thus, the extended iConAwa system combines expert system characteristics with the capability to learn.

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“…This is particularly interesting for mobile applications because these application function in constantly changing environments due to the movement of the user [3]. For example, a mobile tourist guide application could benefit from context by offering personalized tourist information based on the current physical location of the user [4].…”

Section: Introductionmentioning

confidence: 99%

Smart Sensing and Context

Havinga¹,

Lijding²,

Meratnia³

et al. 2006

Lecture Notes in Computer Science

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Abstract. Research in context-aware systems shows that using context information enables the development of personalized mobile applications. The context acquisition process in a context-aware (CA) system consists of two main roles: context producing entities (e.g. wrapped sensors) and context consuming entities (e.g. CA application). A CA system can be seen as a hierarchy of associated context producers and consumers which exchange contextual information. Managing contextual information used in context aware systems, introduces additional complexity for mobile application developers. We focus on the dynamic processes of discovery, selection, (re)binding and monitoring of entities that produce context information. Dynamic context binding is complex because of the dynamic nature, in terms of availability and quality, of context producers. We propose to delegate the responsibility for context binding to the middleware infrastructure and provide application designers with a declarative language to specify context information requirements on a high-level of abstraction. In this way, our Context-Aware Component Infrastructure (CACI) provides support for dynamic context bindings between application components and context producers.

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Service platform for rapid development and deployment of context-aware, mobile applications

Cited by 14 publications

References 9 publications

Context-aware media player (CaMP): Developing context-aware applications with separation of concerns

Context-aware media player (CaMP): Developing context-aware applications with separation of concerns

Matching points of interest with user context: an ANN approach

Smart Sensing and Context

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