Reputation-based content dissemination for user generated wireless podcasting

Liang Hu; Lars Dittmann; J.-Y. Le Boudec

doi:10.1109/WCNC.2009.4917944

Reputation-based content dissemination for user generated wireless podcasting

Liang Hu, Lars Dittmann, J.-Y. Le Boudec

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Abstract

User-generated podcasting service over human-centric opportunistic network can facilitate user-generated content sharing while humans are on the move beyond the coverage of infrastructure networks. We focus on the aspects of designing efficient forwarding and cache replacement schemes of such service under the constraints of limited capability of handheld device and limited network capacity. In particular, the design of those schemes is challenged by the lack of podcast channel popularity information at each node which is crucial for forwarding and caching decisions. We design a distributed reputation system based on modified Bayesian framework that enable each node estimates the channel popularity in a efficient way. It estimates channel popularity by not only first hand observations but also second hand observations from other nodes. Our simulation result shows reputation system can always well estimate most popular, intermediate and low popular channels, compare to history-based rank scheme which can only well estimate a few most popular channels. Reputation system significantly outperforms history-based rank when the public cache size is small or "a" parameter of Zipf-like distribution is small.

Original language	English
Title of host publication	Proceedings, WCNC
Publisher	IEEE
Publication date	2009
Pages	1-6
ISBN (Print)	978-1-4244-2947-9
DOIs	https://doi.org/10.1109/WCNC.2009.4917944
Publication status	Published - 2009
Event	IEEE Wireless Communications and Networking Conference 2009 - Budapest, Hungary Duration: 5 Apr 2009 → 8 Apr 2009 http://wcnc2009.ieee-wcnc.org/

Conference

Conference	IEEE Wireless Communications and Networking Conference 2009
Country/Territory	Hungary
City	Budapest
Period	05/04/2009 → 08/04/2009
Internet address	http://wcnc2009.ieee-wcnc.org/

Bibliographical note

Copyright: 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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title = "Reputation-based content dissemination for user generated wireless podcasting",

abstract = "User-generated podcasting service over human-centric opportunistic network can facilitate user-generated content sharing while humans are on the move beyond the coverage of infrastructure networks. We focus on the aspects of designing efficient forwarding and cache replacement schemes of such service under the constraints of limited capability of handheld device and limited network capacity. In particular, the design of those schemes is challenged by the lack of podcast channel popularity information at each node which is crucial for forwarding and caching decisions. We design a distributed reputation system based on modified Bayesian framework that enable each node estimates the channel popularity in a efficient way. It estimates channel popularity by not only first hand observations but also second hand observations from other nodes. Our simulation result shows reputation system can always well estimate most popular, intermediate and low popular channels, compare to history-based rank scheme which can only well estimate a few most popular channels. Reputation system significantly outperforms history-based rank when the public cache size is small or {"}a{"} parameter of Zipf-like distribution is small.",

author = "Liang Hu and Lars Dittmann and {Le Boudec}, J.-Y.",

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PY - 2009

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N2 - User-generated podcasting service over human-centric opportunistic network can facilitate user-generated content sharing while humans are on the move beyond the coverage of infrastructure networks. We focus on the aspects of designing efficient forwarding and cache replacement schemes of such service under the constraints of limited capability of handheld device and limited network capacity. In particular, the design of those schemes is challenged by the lack of podcast channel popularity information at each node which is crucial for forwarding and caching decisions. We design a distributed reputation system based on modified Bayesian framework that enable each node estimates the channel popularity in a efficient way. It estimates channel popularity by not only first hand observations but also second hand observations from other nodes. Our simulation result shows reputation system can always well estimate most popular, intermediate and low popular channels, compare to history-based rank scheme which can only well estimate a few most popular channels. Reputation system significantly outperforms history-based rank when the public cache size is small or "a" parameter of Zipf-like distribution is small.

AB - User-generated podcasting service over human-centric opportunistic network can facilitate user-generated content sharing while humans are on the move beyond the coverage of infrastructure networks. We focus on the aspects of designing efficient forwarding and cache replacement schemes of such service under the constraints of limited capability of handheld device and limited network capacity. In particular, the design of those schemes is challenged by the lack of podcast channel popularity information at each node which is crucial for forwarding and caching decisions. We design a distributed reputation system based on modified Bayesian framework that enable each node estimates the channel popularity in a efficient way. It estimates channel popularity by not only first hand observations but also second hand observations from other nodes. Our simulation result shows reputation system can always well estimate most popular, intermediate and low popular channels, compare to history-based rank scheme which can only well estimate a few most popular channels. Reputation system significantly outperforms history-based rank when the public cache size is small or "a" parameter of Zipf-like distribution is small.

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Reputation-based content dissemination for user generated wireless podcasting

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