research-article

Controlling Large-Scale Self-Organized Networks with Lightweight Cost for Fast Adaptation to Changing Environments

Authors:

Daichi Kominami,

Tomoaki Hashimoto,

Masayuki MurataAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 11, Issue 2

Article No.: 9, Pages 1 - 26

https://doi.org/10.1145/2856424

Published: 06 June 2016 Publication History

Abstract

Self-organization has potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. Convergence of self-organizing control, however, is slow in some practical applications compared to control with conventional deterministic systems using global information. It is therefore important to facilitate convergence of self-organizing controls. In controlled self-organization, which introduces an external controller into self-organizing systems, the network is controlled to guide systems to a desired state. Although existing controlled self-organization schemes could achieve this feature, convergence speed for reaching an optimal or semioptimal solution is still a challenging task. We perform potential-based self-organizing routing and propose an optimal feedback method using a reduced-order model for faster convergence at low cost. Simulation results show that the proposed mechanism improves the convergence speed of potential-field construction (i.e., route construction) by at most 22.6 times with low computational and communication cost.

References

[1]

Réka Albert and Albert-László Barabási. 2002. Statistical mechanics of complex networks. Reviews of Modern Physics 74, 1, 47--97.

[2]

A. C. Antoulas, D. C. Sorensen, and S. Gugercin. 2006. A survey of model reduction methods for large-scale systems. Contemporary Mathematics 280, 193--219.

[3]

Shin’ichi Arakawa, Yuki Minami, Yuki Koizumi, Takashi Miyamura, Kohei Shiomoto, and Masayuki Murata. 2011. A managed self-organization of virtual network topology controls in WDM-based optical networks. Journal of Optical Communications 32, 4, 233--242.

[4]

Sasitharan Balasubramaniam, Kenji Leibnitz, Pietro Lio, Dmitri Botvich, and Masayuki Murata. 2011. Biological principles for future Internet architecture design. IEEE Communications Magazine 49, 7, 44--52.

[5]

Anindya Basu, Alvin Lin, and Sharad Ramanathan. 2003. Routing using potentials: A dynamic traffic-aware routing algorithm. In Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications. ACM, New York, NY, 37--48.

Digital Library

[6]

Jürgen Branke, Moez Mnif, Christian Muller-Schloer, and Holger Prothmann. 2006. Organic computing—addressing complexity by controlled self-organization. In Proceedings of the 2nd International Symposium on Leveraging Applications of Formal Methods, Verification, and Validation (ISoLA’06). 185--191.

Digital Library

[7]

Falko Dressler. 2008. Self-Organization in Sensor and Actor Networks. Wiley.

Digital Library

[8]

Paul Erdős and Alfréd Rényi. 1961. On the strength of connectedness of a random graph. Acta Mathematica Hungarica 12, 1, 261--267.

[9]

Suyong Eum, Yozo Shoji, Masayuki Murata, and Nozomu Nishinaga. 2014. Design and implementation of ICN-enabled IEEE 802.11 access points as nano data centers. Journal of Network and Computer Applications 50, C, 159--167.

Digital Library

[10]

Pascal Gahinet and Pierre Apkarian. 1994. A linear matrix inequality approach to H ∞ control. International Journal of Robust and Nonlinear Control 4, 4, 421--448.

[11]

Takayuki Ishizaki, Kenji Kashima, Jun-Ichi Imura, and Kazuyuki Aihara. 2014. Model reduction and clusterization of large-scale bidirectional networks. IEEE Transactions on Automatic Control 59, 1, 48--63.

[12]

S. Jung, M. Kserawi, D. Lee, and J.-K. K. Rhee. 2009. Distributed potential field based routing and autonomous load balancing for wireless mesh networks. IEEE Communications Letters 13, 6, 429--431.

Digital Library

[13]

Donald E. Kirk. 2012. Optimal Control Theory: An Introduction. Courier Corporation, North Chelmsford, MA.

[14]

Daichi Kominami, Masashi Sugano, Masayuki Murata, and Takaaki Hatauchi. 2013. Controlled and self-organized routing for large-scale wireless sensor networks. ACM Transactions on Sensor Networks 10, 1, 13:1--13:27.

Digital Library

[15]

Naomi Kuze, Daichi Kominami, Kashima Kashima, Tomoaki Hashimoto, and Masayuki Murata. 2014. Enhancing convergence with optimal feedback for controlled self-organizing networks. In Proceedings of the 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall). 1--7.

[16]

Munyoung Lee, Junghwan Song, Kideok Cho, Sangheon Pack, Jussi Kangasharju, Yanghee Choi, and Ted Taekyoung Kwon. 2014. SCAN: Content discovery for information-centric networking. Computer Networks 83, C, 1--14.

Digital Library

[17]

John M. Mulvey, Robert J. Vanderbei, and Stavros A. Zenios. 1995. Robust optimization of large-scale systems. Operations Research 43, 2, 264--281.

Digital Library

[18]

Camelia-Mihaela Pintea. 2014. Advances in Bio-Inspired Computing for Combinatorial Optimization Problems. Springer, Berlin, Germany.

Digital Library

[19]

Mikhail Prokopenko. 2014. Guided Self-Organization: Inception. Springer, Berlin, Germany.

Digital Library

[20]

Hartmut Schmeck, Christian Müller-Schloer, Emre Çakar, Moez Mnif, and Urban Richter. 2010. Adaptivity and self-organization in organic computing systems. ACM Transactions on Autonomous and Adaptive Systems 5, 3, Article No. 10.

Digital Library

[21]

Alireza Sheikhattar and Mehdi Kalantari. 2013. Fast convergence scheme for potential-based routing in wireless sensor networks. In Proceedings of the Wireless Communications and Networking Conference (WCNC’13). IEEE, Los Alamitos, CA, 1980--1985.

[22]

A. Varga. 1991. Balancing free square-root algorithm for computing singular perturbation approximations. In Proceedings of the 30th IEEE Conference on Decision and Control, Vol. 2. IEEE, Los Alamitos, CA, 1062--1065.

[23]

Duncan J. Watts and Steven H. Strogatz. 1998. Collective dynamics of ‘small-world’ networks. Nature 393, 440--442.

[24]

Chengjie Wu, Ruixi Yuan, and Hongchao Zhou. 2008. A novel load balanced and lifetime maximization routing protocol in wireless sensor networks. In Proceedings of the 67th IEEE Vehicular Technology Conference. IEEE, Los Alamitos, CA, 113--117.

[25]

Xin-She Yang, Zhihua Cui, Renbin Xiao, Amir Hossein Gandomi, and Mehmet Karamanoglu. 2013. Swarm Intelligence and Bio-Inspired Computation: Theory and Applications. Elsevier, Netherlands.

Digital Library

[26]

Zhongshan Zhang, Keping Long, Jianping Wang, and Falko Dressler. 2013. On swarm intelligence inspired self-organized networking: Its bionic mechanisms, designing principles and optimization approaches. Communications Surveys and Tutorials 16, 513--537.

[27]

Chenyu Zheng and Douglas C. Sicker. 2013. A survey on biologically inspired algorithms for computer networking. IEEE Communications Surveys and Tutorials 15, 3, 1160--1191.

[28]

Kemin Zhou, John Comstock Doyle, and Keith Glover. 1995. Robust and Optimal Control. Prentice Hall, Upper Saddle River, NJ.

Digital Library

Cited By

Hou ZFan BYi XWu SZhao Y(2022)Node Assignment-Based Routing for Low-Earth Orbit Satellite Navigation Augmentation NetworksProceedings of 2022 10th China Conference on Command and Control10.1007/978-981-19-6052-9_43(468-485)Online publication date: 30-Aug-2022
https://doi.org/10.1007/978-981-19-6052-9_43
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
Rosas FMediano PUgarte MJensen H(2018)An Information-Theoretic Approach to Self-Organisation: Emergence of Complex Interdependencies in Coupled Dynamical SystemsEntropy10.3390/e2010079320:10(793)Online publication date: 16-Oct-2018
https://doi.org/10.3390/e20100793
Show More Cited By

Index Terms

Controlling Large-Scale Self-Organized Networks with Lightweight Cost for Fast Adaptation to Changing Environments
1. Networks
  1. Network protocols
    1. Network layer protocols
      1. Routing protocols

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Published In

cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 11, Issue 2

Special Section on Best Papers from SASO 2014 and Regular Articles

July 2016

267 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/2952298

Editors:
Manish Parashar
Rutgers University, USA
,
Franco Zambonelli
University of Modena e Reggio Emilia, Italy

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Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2016

Accepted: 01 December 2015

Revised: 01 October 2015

Received: 01 November 2014

Published in TAAS Volume 11, Issue 2

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Cited By

Hou ZFan BYi XWu SZhao Y(2022)Node Assignment-Based Routing for Low-Earth Orbit Satellite Navigation Augmentation NetworksProceedings of 2022 10th China Conference on Command and Control10.1007/978-981-19-6052-9_43(468-485)Online publication date: 30-Aug-2022
https://doi.org/10.1007/978-981-19-6052-9_43
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
Rosas FMediano PUgarte MJensen H(2018)An Information-Theoretic Approach to Self-Organisation: Emergence of Complex Interdependencies in Coupled Dynamical SystemsEntropy10.3390/e2010079320:10(793)Online publication date: 16-Oct-2018
https://doi.org/10.3390/e20100793
Kuze NKominami DKashima KHashimoto TMurata M(2018)Self-Organizing Control Mechanism Based on Collective Decision-Making for Information UncertaintyACM Transactions on Autonomous and Adaptive Systems10.1145/318334013:1(1-21)Online publication date: 16-Apr-2018
https://dl.acm.org/doi/10.1145/3183340
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
Kuze NKominami DKashima KHashimoto TMurata M(2017)Hierarchical Optimal Control Method for Controlling Large-Scale Self-Organizing NetworksACM Transactions on Autonomous and Adaptive Systems10.1145/312464412:4(1-23)Online publication date: 27-Oct-2017
https://dl.acm.org/doi/10.1145/3124644
Behzadan VNourmohammadi AGunes MYuksel M(2017)On Fighting Fire with FireProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201710.1145/3110025.3119404(1120-1127)Online publication date: 31-Jul-2017
https://dl.acm.org/doi/10.1145/3110025.3119404
Shigaki SKuze NKominami DKashima KMurata M(2017)Self-organizing wireless sensor networks based on biological collective decision making for treating information uncertainty2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)10.1109/WiMOB.2017.8115789(167-174)Online publication date: Oct-2017
https://doi.org/10.1109/WiMOB.2017.8115789

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