Presence, Location, and Instant Messaging in a Context-Aware Application Framework

Peddemors, Arjan J. H.; Lankhorst, Marc M.; Heer, Johan de

doi:10.1007/3-540-36389-0_22

Cited by 27 publications

(21 citation statements)

References 4 publications

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“…Past research work proceeded mainly along two dimensions. The first dimension aims to enrich presence information, such as PLIM [20], ConChat [21] and F@ [22]. PLIM combines functionalities related to location, presence and instant messaging in a context-aware mobile application framework.…”

Section: Implementation and Experimental Resultsmentioning

confidence: 99%

Towards pervasive instant messaging and presence awareness

Zhang

Law

Wang

et al. 2009

International Journal of Pervasive Computing and Communications

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Abstract-This work envisions the benefits of applying the Instant Messaging (IM) paradigm in pervasive computing environments. With IM in such an environment, all smart entities, human or not, can interact using IM as the unified interface. To realize this vision, the design of a Smart Instant Messaging (SIM) system is proposed, which features context-aware presence management, user-centric resource configuration, and adaptive grouping. This system extends the Jabber-based IM framework and relies on an ontology-based supporting middleware to handle the chore of retrieving and interpreting contextual information. Three prototype versions of a client have been implemented and their performance in terms of memory usage and response time is evaluated.

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Section: Implementation and Experimental Resultsmentioning

confidence: 99%

Towards pervasive instant messaging and presence awareness

Zhang

Law

Wang

et al. 2009

International Journal of Pervasive Computing and Communications

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“…Modeling and inference of user context were also studied in [6,10,16]. There has also been some preliminary work trying to establish a link between user location and context [16,17], as well as showing that location changes over time can be used to learn a user's activities and even predict future activities [15]. …”

Section: Related Workmentioning

confidence: 99%

Intuitive network applications: Learning user context and behavior

Bila¹,

Cao²,

Dinoff³

et al. 2008

Bell Labs Tech. J.

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There are various dimensions involved in this approach: 1) machine learning techniques to gather user habits and preferences in an automated fashion; 2) human factors-directed approaches to explicitly gather additional information from the user, while minimizing the (perceived) inconvenience to him; 3) providing the user with an easy mechanism to view and control the acquired information (whether learned or explicitly gathered); and 4) ensuring that the overall information monitoring and analysis are done in a robust, scalable fashion, and the acquired information can be used at run time in a near-real time fashion. In this paper, we focus primarily on an overview of the INA architecture and present some details on machine learning directed at an advertising

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“…Using positioning information derived from GPS or cellular network topology data (if available), mobile devices could also publish their current geographical coordinates in the PI to enable applications to provide them with location-based content and services [7].…”

Section: Presencementioning

confidence: 99%