Enhancing intelligence and dependability of a product line enabled pervasive middleware
Pervasive and Mobile Computing , Volume 6 - Issue 2 p. 198- 217
To provide good support for user-centered application scenarios in pervasive computing environments, pervasive middleware must react to context changes and prepare services accordingly. At the same time, pervasive middleware should provide extended dependability via self-management capabilities, to conduct self-diagnosis of possible malfunctions using the current runtime context, and self-configuration and self-adaptation when there are service mismatches. In this article, we present an approach to combine the power of BDI practical reasoning and OWL/SWRL ontologies theoretical reasoning in order to improve the intelligence of pervasive middleware, supported by a set of Self-Management Pervasive Service (SeMaPS) ontologies featuring dynamic context, complex context, and self-management rules modeling. In this approach, belief sets are enriched with the results of OWL/SWRL theoretical reasoning to derive beliefs that cannot be obtained directly or explicitly. This is demonstrated with agents negotiating sports appointments. To cope with self-management, the corresponding monitoring, configuration, adaptation and diagnosis rules are developed based on OWL and SWRL utilizing SeMaPS ontologies. Evaluations show this combined reasoning approach can perform well, and that Semantic Web-based self-management is promising for pervasive computing environments.
|BDI (Belief-Desire-Intention) agents, Middleware, OWL (Web Ontology Language), Self-diagnosis, Self-management, SWRL (Semantic Web Rule Language), XVCL (XML-based Variant Configuration Language)|
|Pervasive and Mobile Computing|
|Organisation||Department of Systems and Computer Engineering|
Zhang, W. (Weishan), Hansen, K.M. (Klaus Marius), & Kunz, T. (2010). Enhancing intelligence and dependability of a product line enabled pervasive middleware. Pervasive and Mobile Computing, 6(2), 198–217. doi:10.1016/j.pmcj.2009.07.002