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dc.creatorFoerster, Anna
dc.creatorUdugama, Asanga
dc.creatorGörg, Carmelita
dc.creatorKuladinithi, Koojana
dc.creatorTimm-Giel, Andreas
dc.creatorCama Pinto, Alejandro
dc.date.accessioned2018-11-14T16:59:34Z
dc.date.available2018-11-14T16:59:34Z
dc.date.issued2015
dc.identifier.issn1867-8211
dc.identifier.urihttp://hdl.handle.net/11323/974
dc.description.abstractThe number of computing devices of the IoT are expected to grow exponentially. To address the communication needs of the IoT, research is being done to develop new networking architectures and to extend existing architectures. An area that lacks attention in these efforts is the emphasis on utilisation of omnipresent local data. There are a number of issues (e.g., underutilisation of local resources and dependence on cloud based data) that need to be addressed to exploit the benefits of utilising local data. We present a novel data dissemination model, called the Organic Data Dissemination (ODD) model to utilise the omni-present data around us, where devices deployed with the ODD model are able to operate even without the existence of networking infrastructure. The realisation of the ODD model requires innovations in many different area including the areas of opportunistic communications, naming of information, direct peer-to-peer communications and reinforcement learning. This paper focuses on highlighting the usage of the ODD model in real application scenarios and the details of the architectural components.es_CO
dc.language.isoengen_US
dc.publisherLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineeringen_US
dc.rightsAtribución – No comercial – Compartir igualen_US
dc.subjectInternet of thingsen_US
dc.subjectOpportunistic networksen_US
dc.subjectOrganic data flowsen_US
dc.subjectReinforcement algorithmsen_US
dc.titleA novel data dissemination model for organic data flowsen_US
dc.typeArticleen_US


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