Abstract. Mobile and embedded devices like PDAs, mobile phones, and all kinds of consumer hardware populate the world we live in. Despite the vision of ubiquitous computing and its idea of spontaneous interaction among these devices more than fifteen years ago, most of them are still isolated and restricted in their interaction capabilities. One reason for this limitation is the poor support for dynamic adaptation and evolution of software in distributed environments. This paper proposes AOCI, an Aspect-Oriented Component Infrastructure that takes the core ideas of AOP, the separation of concerns and system modularization to make them more adaptable and evolvable, to the domain of component systems. Components are usually considered as black boxes that can be combined to a complex system using their outer interfaces. In the context of our infrastructure, components export possible adaptation points, which are enriched by ontological information. This enables the application of AOP techniques without detailed knowledge about the component’s internals, enabling dynamic and distributed adaptation. Our prototype is based on OSGi and provides a complete infrastructure to weave local as well as remote components. We demonstrate the feasibility of our approach by adapting the RUBiS infrastructure (a web-based bidding system) to support dynamic user-centric error detection. 1

Abstract. An increasing number of computationally enhanced objects is distributed around us in physical space, which are equipped – or at least can be provided – with sensors for measuring spatial contexts like position, direction and acceleration. We consider spatial relationships between them, which can basically be acquired by a pairwise comparison of their spatial contexts, as crucial information for a variety of applications. If such objects do have wireless communication capabilities, they will be able to build up an ad-hoc network and exchange their spatial contexts among each other. However, processing detailed sensor information and routing it through the network lowers their battery lifetime or even may exceed the capabilities of embedded systems with limited resources. Thus, we present a novel and efficient approach for inferring and distributing spatial contexts in multi-hop networks, which builds upon qualitative spatial representation and reasoning techniques. Simulation results show its behavior with respect to common network topologies. 1