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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao International Journa...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
International Journal of Network Management
Article . 2008 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
DBLP
Article . 2008
Data sources: DBLP
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Delay management in delay‐tolerant networks

Authors: Qunwei Zheng; Xiaoyan Hong; Pu Wang; Lei Tang; Jun Liu 0009;

Delay management in delay‐tolerant networks

Abstract

AbstractIn delay‐tolerant networks (DTN), nodes explore various opportunities to connect and communicate with each other. A series of encounters of different nodes will create such opportunities and spread a message among many nodes and eventually deliver to the designated destination. We study one common DTN scenario where the message exchanges happen when nodes meet others at certain locations. In this situation, the success of message delivery and the quality of the delivery depend highly on the likeliness of nodes' encounters and time elapsed between encounters. We study two important qualities of service requirements: the probability of two nodes meeting each other (encounter probability) and the time it takes for two nodes to meet (encounter delay). The key considerations are how nodes pick their next locations (mobility patterns) and the features of the dwell time. In this paper, we will study several mobility patterns, including random movement, and activity agenda based movement. We also study an additional message delivery constraint, i.e., a message will be dropped if not delivered within a limited number of locations visited. We develop mathematical formulas using Markov chain as a main tool. Our work is presented as an illustration through case studies. The methodology applies to mobility models alike and is extendable to real trace analysis. We present numerical results when closed form formulas cannot be acquired. Our results help the management of message delivery for DTN, e.g., in selecting a proper time‐to‐live threshold for a message. Copyright © 2008 John Wiley & Sons, Ltd.

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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!
2
Average
Average
Average
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