Improving the Quality of Context-Aware Applications: An Activity-Oriented Context Approach

Author(s):  
Xuansong Li ◽  
Xianping Tao ◽  
Jian Lu
2020 ◽  
Author(s):  
Diandre de Paula ◽  
Daniel Saraiva ◽  
Romeiro Natália ◽  
Nuno Garcia ◽  
Valderi Leithardt

With the growth of ubiquitous computing, context-aware computing-based applications are increasingly emerging, and these applications demonstrate the impact that context has on the adaptation process. From the context, it will be possible to adapt the application according to the requirements and needs of its users. Therefore, the quality of the context information must be guaranteed so that the application does not have an incorrect or unexpected adaptation process. But like any given data, there is the possibility of inaccuracy and/or uncertainty and so Quality of Context (QoC) plays a key role in ensuring the quality of context information and optimizing the adaptation process. To guarantee the Quality of Context it is necessary to study a quality model to be created, which will have the important function of evaluating the context information. Thus, it is necessary to ensure that the parameters and quality indicators to be used and evaluated are the most appropriate for a given type of application. This paper aims to study a context quality model for the UbiPri middleware, defining its quality indicators to ensure its proper functioning in the process of adaptation in granting access to ubiquitous environments. Keywords: QoC, Model, Context-Aware, Data, Privacy


Author(s):  
José Bringel Filho ◽  
Nazim Agoulmine

Ubiquitous Health (U-Health) smart homes are intelligent spaces capable of observing and correctly recognizing the activities and health statuses of their inhabitants (context) to provide the appropriate support to achieve an overall sense of health and well-being in their inhabitants’ daily lives. With the intrinsic heterogeneity and large number of sources of context information, aggregating and reasoning on low-quality raw sensed data may result in conflicting and erroneous evaluations of situations, affecting directly the reliability of the U-Health systems. In this environment, the evaluation and verification of Quality of Context (QoC) information plays a central role in improving the consistency and correctness of context-aware U-Health applications. Therefore, the objective of this chapter is to highlight the impact of QoC on the correct behavior of U-Health systems, and introduce and analyze the existing approaches of modeling, evaluating, and using QoC to improve its context-aware decision-making support.


2008 ◽  
Vol 3 (3) ◽  
Author(s):  
Kamran Sheikh ◽  
Maarten Wegdam ◽  
Marten Van Sinderen

2014 ◽  
Vol 29 (2) ◽  
pp. 154-170 ◽  
Author(s):  
Atif Manzoor ◽  
Hong-Linh Truong ◽  
Schahram Dustdar

AbstractLimitations of sensors and the situation of a specific measurement can affect the quality of context information that is implicitly collected in pervasive environments. The lack of information about Quality of Context (QoC) can result in degraded performance of context-aware systems in pervasive environments, without knowing the actual problem. Context-aware systems can take advantage of QoC if context producers also provide QoC metrics along with context information. In this paper, we analyze QoC and present our model for processing QoC metrics. We evaluate QoC metrics considering the capabilities of sensors, circumstances of specific measurement, requirements of context consumer, and the situation of the use of context information. We also illustrate how QoC metrics can facilitate in enhancing the effectiveness and efficiency of different tasks performed by a system to provide context information in pervasive environments.


2018 ◽  
Vol 14 (2) ◽  
pp. 165-196 ◽  
Author(s):  
Asma Abdul Ghani Al-Shargabi ◽  
Francois Siewe

PurposeThis paper aims to introduce a comprehensive framework for quality of context in pervasive context-aware systems. The framework includes the context quality characteristics, the quality policy, the quality calculation methods and the quality control algorithm.Design/methodology/approachThis study used the constructive research method to introduce the framework. The data of a context-aware flooding prediction system to evaluate the approach were used.FindingsThe framework improves the quality of captured context by resolving the missing, error context and the context conflicts using the quality characteristics and quality control process that are introduced in the framework.Originality/valueThis work is original. It is based on the author’s PhD work in De Montfort University.


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