A novel data dissemination model for organic data flows

First published in
Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 
Number in series
158 LNICST
Start Page
239
End Page
252
Citation
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (158): 239-252 (2015)
Contribution to Conference
7th International Conference on Mobile Networks and Management, MONAMI 2015 
Publisher DOI
10.1007/978-3-319-26925-2_18
Scopus ID
2-s2.0-84955298809
Publisher
Springer
The 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.
Schlagworte
Internet of things
Opportunistic networks
Organic data flows
Reinforcement algorithms
DDC Class
004: Informatik
380: Handel, Kommunikation, Verkehr