Abstract-Future software systems will be highly dynamic. We are already experiencing, for example, a world where CyberPhysical Systems (CPSs) play a more and more crucial role. CPSs integrate computational, physical, and networking elements; they comprise a number of subsystems, or entities, that are connected and work together. The open and highly distributed nature of the resulting system gives rise to unanticipated runtime management issues such as the organization of subsystems and resource optimization.In this paper, we focus on the problem of knowledge sharing among cooperating entities of a highly distributed and selfadaptive CPS. Specifically, the research question we address is how to minimize the knowledge that needs to be shared among the entities of a CPS. If all entities share all their knowledge with each other, the performance, energy and memory consumption as well as privacy are unnecessarily negatively impacted. To reduce the amount of knowledge to share between CPS entities, we envision a role-based adaptive knowledge exchange technique working on partial runtime models, i.e., models reflecting only part of the state of the CPS. Our approach supports two adaptation dimensions: the runtime type of knowledge and conditions over the knowledge. We illustrate the feasibility of our technique by discussing its realization based on two state-of-the-art approaches.
In this paper a navigation system for mobile phones is proposed that optimizes vehicle routing using road and traffic information to avoid traffic congestion. Congestion causes inefficiency of transportation infrastructure and increase the total travel time. It also raises the level of air pollution, and fuel consumption and hence damages the environment. The current location of the car or the user is tracked by GPS enabled phone and the system then checks the database for any road constructions, car accidents or other incidents that driver should avoid in his vicinity. The proposed system also suggests a possible re-route to the destination by calculating the shortest path through A* algorithm excluding the point of traffic jams. Apart from facilitating the user, this location based perosonalized service will also increase the data usage of the mobile user and hence, lends a great support to the telecom operators.
This paper describes SeSaMe, an RDF-enriched self-adaptive middleware for highly dynamic and autonomic complex systems. Semantic technologies make components of a system mutually interoperable and enable them to exchange information even when they are developed and deployed on different platforms. SeSaMe uses semantics to achieve dynamic component discovery and self-configurability in the network of Things, and to support the communication among heterogeneous Things. The paper exemplifies its contribution for harmonizing heterogeneous components in the context of a smart greenhouse scenario.
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