|dc.description.abstract||Context-aware systems use context information to decide what adaptation actions to perform in response to changes in their environment. Depending on applications, context information includes physical context (e.g. temperature and location), user context (e.g. user preferences and user activity), and ICT context (e.g. device capabilities and battery power). Sensors are the main mean of capturing context. Unfortunately, sensed context data are commonly prone to imperfection due to the technical limitations of sensors, their availability, dysfunction, and the highly dynamic nature of environment. Consequently, sensed context data might be imprecise, erroneous, conflicting, or simply missing. To limit the impact of context imperfection on the behavior of a context-aware system, a notion of Quality of Context (QoC) is used to measure quality of any information that is used as context information. Adaptation is performed only if the context data used in the decision-making has an appropriate quality level.
This thesis conducts a novel framework for QoC in context-aware systems, which is called MCFQoC (Multilayered-Context Framework for Quality of Context). The main innovative features of our framework, MCFQoC, include: (1) a new definition that generalizes the notion of QoC to encompass sensed context as well as user profiled context; (2) a novel multilayer context model, that distinguishes between three context abstractions: context situation, context object, and context element in descending order. A context element represents a single value and many context elements can be compound into a context object. Many context objects in turn form a context situation; (3) a novel model of QoC parameters which extends the existing parameters with new quality parameter and explicitly distributes the quality parameters across the three layers of context abstraction; (4) a novel algorithm, RCCAR (Resolving Context Conflicts Using Association Rules), which has been developed to resolve conflicts in context data using the Association Rules (AR) technique; (5) a novel mechanism to define QoC policy by assigning weights to QoC parameters using a multi-criteria decision-making technique called Analytical Hierarchy Process (AHP); (6) and finally, a novel quality control algorithm called IPQP (Integrating Prediction with Quality of context Parameters for Context Quality Control) for handling context conflicts, context missing values, and context erroneous values. IPQP is extension of RCCAR.
Our framework, MCFQoC, has been implemented in MatLab and evaluated using a case study of a flood forecast system. Results show that the framework is expressive and modular, thanks to the multilayer context model and also to the notion QoC policy which enables us to assign weights for QoC’s parameters depending on quality requirements of each specific application. This flexibility makes it easy to apply our approach to a wider type of context-aware applications. As a part of MCFQoC framework, IPQP algorithm has been successfully tested and evaluated for QoC control using a variety of scenarios. The algorithm RCCAR has been tested and evaluated either individually and as a part of MCFQoC framework with a significant performance concerning resolving context conflicts. In addition, RCCAR has achieved a good success comparing to traditional prediction methods such as moving average (MA), weighted moving average, exponential smoothing, doubled exponential smoothing, and autoregressive moving average (ARMA).||en