Cognitive resource-aware dynamic service composition in mobile computing environments

Abstract

The vision of spontaneously delivering ubiquitous services to mobile users stands on the tradition of reflecting functional and non-functional requirements. Nevertheless, existing approaches do not reason in advance about the potential conflicts of human physical activities with human-computer interaction based tasks. That is, the proactive and spontaneous delivery of ubiquitous services for users on the move may lead to cognitive resources depletions, which can hinder the execution of physical activities. The reason is related to the competition for limited cognitive resources between the human-computer interactions required by ubiquitous services, and the users` activities. This competition may burden the user with unexpected interruptions that overload the central human-cognitive capacity. We have proved, by a controlled, semi-naturalistic user study based on an analysis of attention allocation, that the problem of cognitive resources depletions also occurs in the domain of mobile computing environments. Therefore, this dissertation introduces a novel cognitive engineering mechanism to bind and schedule mobile service functionalities during runtime in accordance with situational demands of cognitive resources. The proposed approach is built on two theories from cognitive psychology: the human-processing system, and the multiple cognitive resources theories. Briefly, these theories stand that there are certain structures in the human-processing system that, when they are shared by different cognitive resources involved in diverse time-shared activities, account for the competence for its limited capacity. We reflect these structures in a novel service and activity description model, which is enriched with a cognitive resources layer. A computational model assesses the degree of both the cognitive resources demand and the cognitive resources interferences among multiple, concurrent services and physical activities on top of this description model. Additionall...