Towards adaptive clustering in self-monitoring multi-agent networks

Piraveenan Mahendra Rajah; Mikhail Prokopenko; Peter Wang; Don Price

Towards adaptive clustering in self-monitoring multi-agent networks

Piraveenan Mahendra Rajah^*, Mikhail Prokopenko, Peter Wang, Don Price

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

A Decentralised Adaptive Clustering (DAC) algorithm for self-monitoring impact sensing networks is presented within the context of CSIRO-NASA Ageless Aero-space Vehicle project. DAC algorithm is contrasted with a Fixed-order Centralised Adaptive Clustering (FCAC) algorithm, developed to evaluate the comparative performance. A number of simulation experiments is described, with a focus on the scalability and convergence rate of the clustering algorithm. Results show that DAC algorithm scales well with increasing network and data sizes and is robust to dynamics of the sensor-data flux.

Original language	English
Pages (from-to)	796-805
Number of pages	10
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3682 LNAI
Publication status	Published - 2005
Externally published	Yes

Cite this

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title = "Towards adaptive clustering in self-monitoring multi-agent networks",

abstract = "A Decentralised Adaptive Clustering (DAC) algorithm for self-monitoring impact sensing networks is presented within the context of CSIRO-NASA Ageless Aero-space Vehicle project. DAC algorithm is contrasted with a Fixed-order Centralised Adaptive Clustering (FCAC) algorithm, developed to evaluate the comparative performance. A number of simulation experiments is described, with a focus on the scalability and convergence rate of the clustering algorithm. Results show that DAC algorithm scales well with increasing network and data sizes and is robust to dynamics of the sensor-data flux.",

author = "Rajah, {Piraveenan Mahendra} and Mikhail Prokopenko and Peter Wang and Don Price",

year = "2005",

language = "English",

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pages = "796--805",

journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Towards adaptive clustering in self-monitoring multi-agent networks. / Rajah, Piraveenan Mahendra; Prokopenko, Mikhail; Wang, Peter et al.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3682 LNAI, 2005, p. 796-805.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Towards adaptive clustering in self-monitoring multi-agent networks

AU - Rajah, Piraveenan Mahendra

AU - Prokopenko, Mikhail

AU - Wang, Peter

AU - Price, Don

PY - 2005

Y1 - 2005

N2 - A Decentralised Adaptive Clustering (DAC) algorithm for self-monitoring impact sensing networks is presented within the context of CSIRO-NASA Ageless Aero-space Vehicle project. DAC algorithm is contrasted with a Fixed-order Centralised Adaptive Clustering (FCAC) algorithm, developed to evaluate the comparative performance. A number of simulation experiments is described, with a focus on the scalability and convergence rate of the clustering algorithm. Results show that DAC algorithm scales well with increasing network and data sizes and is robust to dynamics of the sensor-data flux.

AB - A Decentralised Adaptive Clustering (DAC) algorithm for self-monitoring impact sensing networks is presented within the context of CSIRO-NASA Ageless Aero-space Vehicle project. DAC algorithm is contrasted with a Fixed-order Centralised Adaptive Clustering (FCAC) algorithm, developed to evaluate the comparative performance. A number of simulation experiments is described, with a focus on the scalability and convergence rate of the clustering algorithm. Results show that DAC algorithm scales well with increasing network and data sizes and is robust to dynamics of the sensor-data flux.

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M3 - Article

AN - SCOPUS:33745291324

SN - 0302-9743

VL - 3682 LNAI

SP - 796

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

Towards adaptive clustering in self-monitoring multi-agent networks

Abstract

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