Topic
Context awareness
About: Context awareness is a research topic. Over the lifetime, 5790 publications have been published within this topic receiving 119944 citations.
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TL;DR: Experiments have proved that the HRMM is capable of predicting the health status of the patients suffering from blood pressure disorders accurately, and it proved that NB-WOA accelerates the classification process and saves storage space.
52 citations
TL;DR: How NGSON, which already incorporates several aspects of virtualization, could take advantage of network virtualization mechanisms and programmable traffic steering capabilities to enhance and effectively support context-aware service composition along with adaptive service delivery is discussed.
Abstract: NGSON is an IEEE sponsored effort to standardize an open service ecosystem where different stakeholders may cooperate to satisfy users’ ever changing requirements by composing and delivering services dynamically across different service providers and network operators’ domains. A main distinguishing feature of NGSON is context awareness, that is, the capability of collecting, processing, and using context information to drive the interactions of the NGSON overlay nodes with service and network operators’ infrastructures for guaranteeing an optimal user experience. Recent advances in network control and management technologies, such as SDN, NFV, and service-oriented networking paradigms, are expected to make the network effectively able to cope with application service requirements in a more flexible, timely, and agile manner. In this article we discuss how NGSON, which already incorporates several aspects of virtualization, could take advantage of network virtualization mechanisms and programmable traffic steering capabilities to enhance and effectively support context-aware service composition along with adaptive service delivery. We propose an architectural model for NGSON that leverages these novel network service control capabilities and illustrate through a use case how this model could effectively be put into operation.
52 citations
18 Apr 2006
TL;DR: This work proposes a context-aware computing infrastructure, called FollowMe, which integrates an ontology based context model and a workflow based application model with the OSGi framework and can be customized to various domains.
Abstract: Pervasive computing is to enhance the environment by embedding many computers that are gracefully integrated with human users. To achieve this, the key research thrust is to create a smart context-awareness environment which should enclose various users and satisfy different needs of the users. Building such smart environments is still difficult and complex due to lacking a uniform infrastructure that can adapt to diverse smart domains. To address this problem, we propose a context-aware computing infrastructure, called FollowMe. Our infrastructure integrates an ontology based context model and a workflow based application model with the OSGi framework. By plugging different domain contexts and applications, FollowMe can be customized to various domains.
52 citations
TL;DR: The authors present an operationalization of Irwin Altman's privacy regulation theory for this purpose, describing how individual phases of the process can be supported and their experiences in developing context-adaptive privacy mechanisms for different applications and domains.
Abstract: Ubiquitous computing applications introduce multiple privacy challenges due to their sensing and actuation capabilities, which are often combined with ubiquitous interconnectivity. Because of the complexity of many ubicomp systems, users might have difficulties estimating the privacy implications of their actions and decisions. In this article, the authors discuss leveraging awareness about a user's context and respective context changes to dynamically support privacy decision making. They present an operationalization of Irwin Altman's privacy regulation theory for this purpose, describing how individual phases of the process can be supported. They also report on their experiences in developing context-adaptive privacy mechanisms for different applications and domains. This article is part of a special issue on privacy and security.
52 citations
TL;DR: This paper proposes an approach based on Bayesian networks for building recommender systems that minimize context acquisition and shows that a two-tiered context model can effectively capture the causal dependencies among context parameters, enabling a recommender system to compensate for missing and erroneous context inputs.
Abstract: Acquisition of context poses unique challenges to mobile context-aware recommender systems. The limited resources in these systems make minimizing their context acquisition a practical need, and the uncertainty in the mobile environment makes missing and erroneous context inputs a major concern. In this paper, we propose an approach based on Bayesian networks (BNs) for building recommender systems that minimize context acquisition. Our learning approach iteratively trims the BN-based context model until it contains only the minimal set of context parameters that are important to a user. In addition, we show that a two-tiered context model can effectively capture the causal dependencies among context parameters, enabling a recommender system to compensate for missing and erroneous context inputs. We have validated our proposed techniques on a restaurant recommendation data set and a Web page recommendation data set. In both benchmark problems, the minimal sets of context can be reliably discovered for the specific users. Furthermore, the learned Bayesian network consistently outperforms the J4.8 decision tree in overcoming both missing and erroneous context inputs to generate significantly more accurate predictions.
52 citations