research-article

Self-Organizing Control Mechanism Based on Collective Decision-Making for Information Uncertainty

Authors:

Daichi Kominami,

Tomoaki Hashimoto,

Masayuki MurataAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 13, Issue 1

Article No.: 7, Pages 1 - 21

https://doi.org/10.1145/3183340

Published: 16 April 2018 Publication History

Abstract

Because of the rapid growth in the scale and complexity of information networks, self-organizing systems are increasingly being used to realize novel network control systems that are highly scalable, adaptable, and robust. However, the uncertainty of information (with regard to incompleteness, vagueness, and dynamics) in self-organizing systems makes it difficult for them to work appropriately in accordance with the network state. In this study, we apply a model of the collective decision-making of animal groups to enable self-organizing control mechanisms to adapt to information uncertainty. Specifically, we apply a mathematical model of collective decision-making that is known as the effective leadership model (ELM). In the ELM, informed individuals (those who are experienced or well-informed) take the role of leading the others. In contrast, uninformed individuals (those who perceive only local information) follow neighboring individuals. As a result of the collective behavior of informed/uninformed individuals, the animal group achieves consensus. We consider a self-organizing control mechanism using potential-based routing with an optimal control, and propose a mechanism for determining a data-packet forwarding scheme based on the ELM. Through evaluation by simulation, we show that, in a situation in which the perceived information is incomplete and dynamic, nodes can forward data packets in accordance with the network state by applying the ELM.

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Cited By

Abdennadher I(2022)DAACS : a Decision Approach for Autonomic Computing SystemsThe Journal of Supercomputing10.1007/s11227-021-04011-z78:3(3883-3904)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11227-021-04011-z
Liu WRan WNantogma SXu Y(2021)Adaptive Information Sharing with Ontological Relevance Computation for Decentralized Self-Organization SystemsEntropy10.3390/e2303034223:3(342)Online publication date: 14-Mar-2021
https://doi.org/10.3390/e23030342
Kuze NKominami DKashima KHashimoto TMurata M(2018)Self-Organizing Control Mechanisms According to Information Confidence for Improving Performance2018 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOCOM.2018.8647892(1-6)Online publication date: Dec-2018
https://doi.org/10.1109/GLOCOM.2018.8647892

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Published In

cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 13, Issue 1

March 2018

184 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/3208359

Editor:
Bashar Nuseibeh
The Open University, UK

Issue’s Table of Contents

Copyright © 2018 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2018

Accepted: 01 January 2018

Revised: 01 December 2017

Received: 01 April 2017

Published in TAAS Volume 13, Issue 1

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Cited By

Abdennadher I(2022)DAACS : a Decision Approach for Autonomic Computing SystemsThe Journal of Supercomputing10.1007/s11227-021-04011-z78:3(3883-3904)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11227-021-04011-z
Liu WRan WNantogma SXu Y(2021)Adaptive Information Sharing with Ontological Relevance Computation for Decentralized Self-Organization SystemsEntropy10.3390/e2303034223:3(342)Online publication date: 14-Mar-2021
https://doi.org/10.3390/e23030342
Kuze NKominami DKashima KHashimoto TMurata M(2018)Self-Organizing Control Mechanisms According to Information Confidence for Improving Performance2018 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOCOM.2018.8647892(1-6)Online publication date: Dec-2018
https://doi.org/10.1109/GLOCOM.2018.8647892

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