Context and resource awareness in opportunistic network data dissemination

Authors:
Chiara Boldrini;Marco Conti;Andrea Passarella
Affiliations:
CNR-IIT, Via G. Moruzzi, 1 - 56124 Pisa, Italy;CNR-IIT, Via G. Moruzzi, 1 - 56124 Pisa, Italy;CNR-IIT, Via G. Moruzzi, 1 - 56124 Pisa, Italy
Venue:
WOWMOM '08 Proceedings of the 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks
Year:
2008

Citing 0
Cited 6

Modelling data dissemination in opportunistic networks

Proceedings of the third ACM workshop on Challenged networks
ContentPlace: social-aware data dissemination in opportunistic networks

Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems
Design and performance evaluation of ContentPlace, a social-aware data dissemination system for opportunistic networks

Computer Networks: The International Journal of Computer and Telecommunications Networking
Cellular traffic offloading through opportunistic communications: a case study

Proceedings of the 5th ACM workshop on Challenged networks
Fair content dissemination in participatory DTNs

Ad Hoc Networks
RIS: A Reciprocal Incentive Scheme in Selfish Opportunistic Networks

Wireless Personal Communications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Opportunistic networks are challenging mobile ad hoc networks characterised by frequent disconnections and partitioning. In this paper we focus on data dissemination services, i.e. cases in which data should be disseminated in the network without a priori knowledge about the set of intended destinations. We propose a general autonomic data dissemination framework that exploits information about the users’ context and social behaviour, to decide how to replicate and replace data on nodes’ buffers. Furthermore, our data dissemination scheme explicitly takes into account resource constraints, by jointly considering the expected utility of data replication and the associated costs. The results we present show that our solution is able to improve data availability, provide fairness among nodes, and reduce the network load, with respect to reference proposals available in the literature.