Efficient profile aggregation and policy evaluation in a middleware for adaptive mobile applications

There is a large consensus on the need for a middleware to efficiently support adaptation in pervasive and mobile computing. Advanced forms of adaptation require the aggregation of context data and the evaluation of policy rules that are typically provided by multiple sources. This paper addresses the problem of designing the reasoning core of a middleware that supports these tasks, while guaranteeing very low response times as required by mobile applications. Technically, the paper presents strategies to deal with conflicting rules, algorithms that implement the strategies, and algorithms that detect and solve potential rule cycles. A detailed experimental analysis supports the theoretical results and shows the applicability of the resulting middleware in large-scale applications.