Impact-based contextual service selection in a ubiquitous robotic environment

Context has a crucial importance in the way actions are perceived and done, especially in ubiquitous robotics where context is rich and subject to substantial variations. Given that service selection focuses on the nonfunctional performance of services, it must be tightly related to the context. Unfortunately, as far as we know, previous works have not effectively considered this relation. First, most of the existing selection models rely on Quality of Service (QoS) parameters that have been estimated according to the previous executions. However, two consecutive executions might occur in two very different contexts and then behave differently. Thus, this paper argues that these QoS parameters should be predicted from context. Finally, the aggregation of these QoS parameters into a score reflects the expectations on a service; it should also be context-dependent. In this article, a solution addressing these points is proposed for auxiliary services. Auxiliary services assist another service during its execution, usually by delivering a data stream. Instead of focusing on their individual performances, selection considers their impact on the assisted service. We propose to obtain this model through a multilayer perceptron under batch learning. Thus, focus is given to the sample generation. This model is validated in a ubiquitous robotic scenario involving a localization service selection.