Managing Quality of Context in Pervasive Computing

Abstract

Context-awareness plays a key role in a paradigm shift from traditional desktop styled computing to emerging pervasive computing. Many context-aware systems have been built to achieve the vision of pervasive computing and alleviate the human attention bottleneck; however, these systems are far from real world applications. Quality of context is critical in reducing the gap between existing systems and real-life applications. Aiming to provide the support of quality of context, in this paper, we propose a novel quality model for context information and a context management mechanism for inconsistency resolution. We also build a prototype system to validate our proposed model and mechanism, and to assist the development of contextaware applications. Through our evaluations and case study, context-aware applications can be built with the support of quality of context.

Data Quality Observation in Pervasive Environments

“…Manzoor et al's completeness definition requires setting weight for each attribute involved, which is highly impractical. Bu et al [6] have defined inconsistency as the difference between raw input values from different sources on the same context attribute, and proposed an inconsistency resolution algorithm based on comparing the appearance frequency of each value. Kim et al [7] have only defined representation consistency, which refers to the extent to which data is presented in the same format.…”

“…The proposed metric frameworks either provide overly simplistic definitions, or eluded the discussions about necessary parameterization. In addition, among all the discussions on QoC metrics, only Kim et al [7] and Bu et al [6] have provided small scale setup or simulation to apply some metrics. In contrast, our work is not intended to propose a general metric framework, as the choice of quality dimensions to be applied should be left to pervasive applications.…”

An adaptive environment based on Moodle with treating of quality of context

“…According to Bu et al (2006), due to limitations of sensor technology, is difficult to guarantee the accuracy of the detected data. However, if the context is measured by means of random sampling in an extended period, with the registration of the correct rate (the probability that the contexts in computers match situations in the real world), it is possible to provide a quantitative measure for the quality context on a context-sensitive system.…”

“…Delay Time: it is the time interval between the time when the situation occurs in the real world and the time when the situation is recognised in the computer (Bu et al, 2006). In connection speed, it is the time between the moment the speed is calculated and generates its result, and the time when this result is inserted into the database and used as context information.…”

Heuristics-Based Strategies for Resolving Context Inconsistencies in Pervasive Computing Applications

“…To resolve context inconsistencies automatically, various strategies have been proposed in the literature. Bu et al [1] propose discarding all contexts relevant to any inconsistency. Chomicki et al [4] suggest discarding the latest inputted event (context) if it causes any conflicting actions (inconsistencies), or to randomly discard some actions.…”