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Computer Networks
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License: Elsevier Non-Commercial
Data sources: UnpayWall
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Computer Networks
Article . 2016 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Efficient Multicast Algorithms in Opportunistic Mobile Social Networks using Community and Social Features

Authors: Xiao Chen; Charles Shang; Britney Wong; Wenzhong Li; Suho Oh;

Efficient Multicast Algorithms in Opportunistic Mobile Social Networks using Community and Social Features

Abstract

Abstract Opportunistic Mobile Social Networks (OMSNs), formed by people moving around carrying mobile devices, enhance spontaneous communication among users that opportunistically encounter each other without additional infrastructure. Multicast is an important communication service in OMSNs. Most of the existing multicast algorithms neglect or adopt static social factors that are inadequate to catch nodes’ dynamic contact behavior. In this paper, we introduce dynamic social features and its enhancement to capture nodes’ contact behavior, consider more social relationships among nodes, and adopt community structure in the multicast compare-split schemes to select the best relay nodes to improve multicast efficiency. We propose two multicast algorithms based on these new features. The first one Multi-CSDO involves destination nodes only in community detection while the second one Multi-CSDR involves both the destination nodes and the relay candidates in community detection. The analysis of the algorithms is given and simulation results using two real OMSN traces show that our new algorithms outperform the existing ones in delivery rate, latency, and number of forwardings.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
22
Top 10%
Top 10%
Top 10%
hybrid