Finding self-similarities in opportunistic people networks

Ling Jyh Chen; Yung Chih Chen; Tony Sun; Paruvelli Sreedevi; Kuan Ta Chen; Chen Hung Yu; Hao Hua Chu

doi:10.1109/INFCOM.2007.266

Finding self-similarities in opportunistic people networks

Ling Jyh Chen^*, Yung Chih Chen, Tony Sun, Paruvelli Sreedevi, Kuan Ta Chen, Chen Hung Yu, Hao Hua Chu

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

20 Citations (Scopus)

Abstract

Opportunistic network is a type of Delay Tolerant Networks (DTN) where network communication opportunities appear opportunistic. In this study, we investigate opportunistic network scenarios based on public network traces, and our contributions are the following: First, we identify the censorship issue in network traces that usually leads to strongly skewed distribution of the measurements. Based on this knowledge, we then apply the Kaplan-Meier Estimator to calculate the survivorship of network measurements, which is used in designing our proposed censorship removal algorithm (CRA) that is used to recover censored data. Second, we perform a rich set of analysis illustrating that UCSD and Dartmouth network traces show strong self-similarity, and can be modeled as such. Third, we pointed out the importance of these newly revealed characteristics in future development and evaluation of opportunistic networks.

Original language	English
Title of host publication	Proceedings - IEEE INFOCOM 2007
Subtitle of host publication	26th IEEE International Conference on Computer Communications
Pages	2286-2290
Number of pages	5
DOIs	https://doi.org/10.1109/INFCOM.2007.266
Publication status	Published - 2007
Externally published	Yes
Event	IEEE INFOCOM 2007: 26th IEEE International Conference on Computer Communications - Anchorage, AK, United States Duration: 2007 May 6 → 2007 May 12

Publication series

Name	Proceedings - IEEE INFOCOM
ISSN (Print)	0743-166X

Other

Other	IEEE INFOCOM 2007: 26th IEEE International Conference on Computer Communications
Country/Territory	United States
City	Anchorage, AK
Period	2007/05/06 → 2007/05/12

ASJC Scopus subject areas

General Computer Science
Electrical and Electronic Engineering

Access to Document

10.1109/INFCOM.2007.266

Cite this

Chen, LJ, Chen, YC, Sun, T, Sreedevi, P, Chen, KT, Yu, CH & Chu, HH 2007, Finding self-similarities in opportunistic people networks. in Proceedings - IEEE INFOCOM 2007: 26th IEEE International Conference on Computer Communications., 4215848, Proceedings - IEEE INFOCOM, pp. 2286-2290, IEEE INFOCOM 2007: 26th IEEE International Conference on Computer Communications, Anchorage, AK, United States, 2007/05/06. https://doi.org/10.1109/INFCOM.2007.266

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title = "Finding self-similarities in opportunistic people networks",

abstract = "Opportunistic network is a type of Delay Tolerant Networks (DTN) where network communication opportunities appear opportunistic. In this study, we investigate opportunistic network scenarios based on public network traces, and our contributions are the following: First, we identify the censorship issue in network traces that usually leads to strongly skewed distribution of the measurements. Based on this knowledge, we then apply the Kaplan-Meier Estimator to calculate the survivorship of network measurements, which is used in designing our proposed censorship removal algorithm (CRA) that is used to recover censored data. Second, we perform a rich set of analysis illustrating that UCSD and Dartmouth network traces show strong self-similarity, and can be modeled as such. Third, we pointed out the importance of these newly revealed characteristics in future development and evaluation of opportunistic networks.",

author = "Chen, {Ling Jyh} and Chen, {Yung Chih} and Tony Sun and Paruvelli Sreedevi and Chen, {Kuan Ta} and Yu, {Chen Hung} and Chu, {Hao Hua}",

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AU - Chen, Ling Jyh

AU - Chen, Yung Chih

AU - Sun, Tony

AU - Sreedevi, Paruvelli

AU - Chen, Kuan Ta

AU - Yu, Chen Hung

AU - Chu, Hao Hua

PY - 2007

Y1 - 2007

N2 - Opportunistic network is a type of Delay Tolerant Networks (DTN) where network communication opportunities appear opportunistic. In this study, we investigate opportunistic network scenarios based on public network traces, and our contributions are the following: First, we identify the censorship issue in network traces that usually leads to strongly skewed distribution of the measurements. Based on this knowledge, we then apply the Kaplan-Meier Estimator to calculate the survivorship of network measurements, which is used in designing our proposed censorship removal algorithm (CRA) that is used to recover censored data. Second, we perform a rich set of analysis illustrating that UCSD and Dartmouth network traces show strong self-similarity, and can be modeled as such. Third, we pointed out the importance of these newly revealed characteristics in future development and evaluation of opportunistic networks.

AB - Opportunistic network is a type of Delay Tolerant Networks (DTN) where network communication opportunities appear opportunistic. In this study, we investigate opportunistic network scenarios based on public network traces, and our contributions are the following: First, we identify the censorship issue in network traces that usually leads to strongly skewed distribution of the measurements. Based on this knowledge, we then apply the Kaplan-Meier Estimator to calculate the survivorship of network measurements, which is used in designing our proposed censorship removal algorithm (CRA) that is used to recover censored data. Second, we perform a rich set of analysis illustrating that UCSD and Dartmouth network traces show strong self-similarity, and can be modeled as such. Third, we pointed out the importance of these newly revealed characteristics in future development and evaluation of opportunistic networks.

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Finding self-similarities in opportunistic people networks

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