On the convergence of distributed random grouping for average consensus on sensor networks with time-varying graphs

Dynamical connection graph changes are inherent in networks such as peer-to-peer networks, wireless ad hoc networks, and wireless sensor networks. Considering the influence of the frequent graph changes is thus essential for precisely assessing the performance of applications and algorithms on such...

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Bibliographic Details
Published in2007 46th IEEE Conference on Decision and Control pp. 4233 - 4238
Main Authors Jen-Yeu Chen, Jianghai Hu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2007
Subjects
Aggregates
Algorithm design and analysis
Computer networks
Convergence
Distributed computing
Mobile ad hoc networks
Peer to peer computing
Performance analysis
Upper bound
Wireless sensor networks
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Summary:Dynamical connection graph changes are inherent in networks such as peer-to-peer networks, wireless ad hoc networks, and wireless sensor networks. Considering the influence of the frequent graph changes is thus essential for precisely assessing the performance of applications and algorithms on such networks. In this paper, we analyze the performance of an epidemic-like algorithm, DRG (distributed random grouping), for average aggregate computation on a wireless sensor network with dynamical graph changes. Particularly, we derive the convergence criteria and the upper bounds on the running time of the DRG algorithm for a set of graphs that are individually disconnected but jointly connected in time. An effective technique for the computation of a key parameter in the derived bounds is also developed. Numerical results and an application extended from our analytical results to control the graph sequences are presented to exemplify our analysis.
ISBN:9781424414970
1424414970
ISSN:0191-2216
DOI:10.1109/CDC.2007.4434493