Naomi Kuze
Abstract
Self-organization has the potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. The convergence of self-organizing control, however, is slow in some practical applications in comparison with control by conventional deterministic systems using global information. It is therefore important to facilitate the 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 the same state, it is difficult for an external controller to collect information about the network and to provide control inputs to the network, especially when the network size is large. This is because the computational cost for designing the external controller and for calculating the control inputs increases rapidly as the number of nodes in the network becomes large. Therefore, we partition a network into several sub-networks and introduce two types of controllers, a central controller and several sub-controllers that control the network in a hierarchical manner. In this study, we propose a hierarchical optimal feedback mechanism for self-organizing systems and apply this mechanism to potential-based self-organizing routing. Simulation results show that the proposed mechanism improves the convergence speed of potential-field construction (i.e., route construction) up to 10.6-fold with low computational and communication costs.
- A. C. Antoulas, D. C. Sorensen, and S. Gugercin. 2006. A survey of model reduction methods for large-scale systems. Contemp. Math. 280 (Oct. 2006), 193--219. Google Scholar
Cross Ref
- Shiníchi 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. J. Opt. Commun. 32, 4 (Dec. 2011), 233--242.Google ScholarCross Ref
- Sasitharan Balasubramaniam, Kenji Leibnitz, Pietro Lio, Dmitri Botvich, and Masayuki Murata. 2011. Biological principles for future internet architecture design. IEEE Commun. Mag. 49, 7 (July 2011), 44--52. Google ScholarCross Ref
- 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, Karlsruhe, Germany, 37--48. Google ScholarDigital Library
- Falko. Dressler. 2008. Self-Organization in Sensor and Actor Networks. Wiley, New York, NY.Google Scholar
- 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. J. Netw. Comput. Appl. 50 (Aug. 2014), 159--167. Google ScholarDigital Library
- J. Michael Herrmann, Michael Holicki, and Ralf Der. 2004. On Ashby’s homeostat: A formal model of adaptive regulation. From Animals to Animats (June 2004), 324--333.Google Scholar
- Takayuki Ishizaki, Kenji Kashima, Jun-ichi Imura, and Kazuyuki Aihara. 2014. Model reduction and clusterization of large-scale bidirectional networks. IEEE Trans. Autom. Control 59, 1 (Jan. 2014), 48--63. Google ScholarCross Ref
- Sangsu Jung, Malaz Kserawi, Dujeong Lee, and J-KK Rhee. 2009. Distributed potential field based routing and autonomous load balancing for wireless mesh networks. IEEE Commun. Lett. 13, 6 (June 2009), 429--431. Google ScholarDigital Library
- Daichi Kominami, Masashi Sugano, Masayuki Murata, and Takaaki Hatauchi. 2013. Controlled and self-organized routing for large-scale wireless sensor networks. ACM Trans. Sens. Netw. 10, 1 (Nov. 2013), 13:1--13:27.Google ScholarDigital Library
- Naomi Kuze, Daichi Kominami, Kenji Kashima, Tomoaki Hashimoto, and Masayuki Murata. 2015. Hierarchical optimal control method for controlling self-organized networks with light-weight cost. IEEE GLOBECOM 2015. In Proceedings of 2015 IEEE Global Communications Conference (GLOBECOM’15). IEEE, 1--7. Google ScholarCross Ref
- Naomi Kuze, Daichi Kominami, Kenji Kashima, Tomoaki Hashimoto, and Masayuki Murata. 2016. Controlling large-scale self-organized networks with lightweight cost for fast adaptation to changing environments. ACM Trans. Auton. Adapt. Syst. 11, 2 (July 2016), 9:1--9:26.Google ScholarDigital Library
- Munyoung Lee, Junghwan Song, Kideok Cho, Sangheon Pack, Jussi Kangasharju, Yanghee Choi, and Ted Taekyoung Kwon. 2015. SCAN: Content discovery for information-centric networking. Comput. Netw. 83, 4 (June 2015), 1--14. Google ScholarDigital Library
- Georg Martius and J. Michael Herrmann. 2010. Taming the beast: Guided self-organization of behavior in autonomous robots. In Proceedings of International Conference on Simulation of Adaptive Behavior. Springer, 50--61. Google ScholarCross Ref
- Christian Müller-Schloer, Hartmut Schmeck, and Theo Ungerer. 2011. Organic Computing-a Paradigm Shift for Complex Systems. Birkhaeuser, Berlin.Google Scholar
- Camelia-Mihaela Pintea. 2014. Advances in Bio-Inspired Computing for Combinatorial Optimization Problems. Springer, Berlin. Google ScholarCross Ref
- Mikhail Prokopenko. 2014. Guided Self-Organization: Inception. Springer, Berlin. Google ScholarCross Ref
- N. Sandell, P. Varaiya, M. Athans, and M. Safonov. 1978. Survey of decentralized control methods for large scale systems. IEEE Trans. Automat. Control 23, 2 (April 1978), 108--128. Google ScholarCross Ref
- Alireza Sheikhattar and Mehdi Kalantari. 2014. Distributed load balancing using alternating direction method of multipliers. In Proceedings of 2014 IEEE Global Communications Conference (GLOBECOM’14). IEEE, , 392--398. Google ScholarCross Ref
- 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, 113--117. Google ScholarCross Ref
- Xin-She Yang, Zhihua Cui, Renbin Xiao, Amir Hossein Gandomi, and Mehmet Karamanoglu. 2013. Swarm Intelligence and Bio-Inspired Computation: Theory and Applications. Elsevier, Amsterdam, The Netherlands. Google ScholarCross Ref
- 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. Commun. Surv. Tutor. 16 (July 2013), 513--537. Google ScholarCross Ref
- Chenyu Zheng and Douglas C Sicker. 2013. A survey on biologically inspired algorithms for computer networking. IEEE Commun. Surv. Tutor. 15, 3 (Jan. 2013), 1160--1191. Google ScholarCross Ref
- Kemin Zhou, John Comstock Doyle, Keith Glover, and others. 1995. Robust and Optimal Control. Prentice Hall, Upper Saddle River, NJ.Google Scholar
Index Terms
- Hierarchical Optimal Control Method for Controlling Large-Scale Self-Organizing Networks
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