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Autonomic Information Diffusion in Intermittently Connected Networks

Abstract : In this work, we introduce a framework for designing autonomic information diffusion mechanisms in intermittently connected wireless networks. Our approach is based on the use of techniques and tools drawn from evolutionary computing research, which enable to embed evolutionary features in epidemic-style forwarding mechanisms. In this way, it is possible to build a system in which information dissemination strategies change at runtime to adapt to the current network conditions in a distributed autonomic fashion. A case study is then introduced, for which design and implementation choices are presented and discussed. Simulation results are reported to validate the ability of the proposed protocol to converge to the optimal operating point (or close to it) in unknown and changing environments.
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Contributor : Sara Alouf Connect in order to contact the contributor
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Sara Alouf, Iacopo Carreras, Álvaro Fialho, Daniele Miorandi, Giovanni Neglia. Autonomic Information Diffusion in Intermittently Connected Networks. Mieso K. Denko and Laurence T. Yang and Yan Zhang. Autonomic Computing and Networking, Springer, pp.411-433, 2009, ⟨10.1007/978-0-387-89828-5_17⟩. ⟨hal-00640974⟩



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