Abstract
As a typical ubiquitous/pervasive computing environment, a smart space usually consists of many sensors/actuators for interacting with the physical environment together with many context-aware applications providing users with kinds of services. However, as context-aware applications deployed in a smart space share the same physical environment, they may have influences on one another. These influences, if not carefully handled, might lead to performance degradation of running applications and further affect user experience. To guarantee the user experience, mechanisms for handling such influences are needed. In this paper, we model context-aware applications in a smart space together with their pair wise influences by a special colored weighted directed acyclic graph (CWDAG)-Influence Graph, and propose an efficient genetic algorithm for finding a fairly good plan for configuring applications running in the same smart space based on the influence graph. Exhausted simulations are carried out to show the effectiveness and efficiency of the proposed algorithm.
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