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to integer n-sequences and the nodes in the path generate all n! permutations. The extra node has a more complicated action, adding a certain quantity to some of the numbers.

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Diese Dissertation wurde vom Cuvillier Verlag Göttingen als Buch unter ISBN 978-3-95404-019-3 veröffentlicht (www.cuvillier.de). iContents

Messaging is a basic service in sensornets. Yet the unique system and application prop-erties of sensornets pose substantial challenges for the messaging design: Firstly, dynamic wireless links, constrained resources, and application diversity challenge the architecture and protocol design of sensornet messaging; Secondly, complex faults and large system scale introduce new challenges to the design of fault-tolerant protocols. The objective of this dissertation is to address the aforementioned challenges of sensornet messaging. Despite the extensive effort in studying sensornet messaging, the lack of a basic un-derstanding of its essential components has been an obstacle for reliable, efcient, and reusable messaging services in sensornets. To address this problem, one task of this dis-sertation is to identify the basic components of sensornet messaging and to study the re-lated algorithmic design issues. More specically, we propose the messaging architecture SMA that consists of three components: trafc-adaptive link estimation and routing (TLR), application-adaptive structuring (AST), and application-adaptive scheduling (ASC). TLR deals with dynamic wireless links as well as the impact of application trafc patterns on

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journal homepage: www.elsevier.com/locate/adhoc Enabling efficient reprogramming through reduction of executable modules

Abstract Basic ideas of rough set theory were proposed by Zdzis law Pawlak [85, 86] in the early 1980’s. In the ensuing years, we have witnessed a systematic, world–wide growth of interest in rough sets and their applications. The main goal of rough set analysis is induction of approximations of con-cepts. This main goal is motivated by the basic fact, constituting also the main problem of KDD, that languages we may choose for knowledge description are incomplete. A fortiori, we have to describe concepts of interest (features, proper-ties, relations etc.) not completely but by means of their reflections (i.e. approx-imations) in the chosen language. The most important issues in this induction process are: – construction of relevant primitive concepts from which approximations of more complex concepts are assembled, – measures of inclusion and similarity (closeness) on concepts, – construction of operations producing complex concepts from the primitive ones.