Noise-Assisted Control in Information Networks

In recent years, interesting phenomena were revealed where the inherent noise enhances the adaptability of a system to its changing environment in a resilient manner. The application of such methods can be found in a wide variety of fields, from biology to engineering. In this paper we discuss the application of noise-assisted mechanisms to control and manage information networks. Specifically, we focus on two mechanisms, stochastic resonance and adaptive response by attractor selection, and show how they can be applied in self-adaptive network control to improve the robustness and reliability of the system.

[1]  Naoki Wakamiya,et al.  Noise‐assisted distributed detection in sensor networks , 2007 .

[2]  Derek Abbott,et al.  Optimal information transmission in nonlinear arrays through suprathreshold stochastic resonance , 2006 .

[3]  Gregoire Nicolis,et al.  Stochastic resonance , 2007, Scholarpedia.

[4]  Yoichiro Ito,et al.  On the relation between fluctuation and response in biological systems , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Naoki Wakamiya,et al.  Noise-assisted detection in sensor network with suboptimal fusion of optimal detections , 2007 .

[6]  Kenji Leibnitz,et al.  WSN17-3: Self-Adaptive Ad-Hoc/Sensor Network Routing with Attractor-Selection , 2006, IEEE Globecom 2006.

[7]  Kurt Wiesenfeld,et al.  Minireview of stochastic resonance. , 1998, Chaos.

[8]  B. Kosko,et al.  Adaptive stochastic resonance , 1998, Proc. IEEE.

[9]  N. Stocks,et al.  Suprathreshold stochastic resonance in multilevel threshold systems , 2000, Physical review letters.

[10]  K. Kaneko,et al.  Adaptive Response of a Gene Network to Environmental Changes by Fitness-Induced Attractor Selection , 2006, PloS one.

[11]  Kunihiko Kaneko,et al.  Life: An Introduction to Complex Systems Biology , 2006 .

[12]  Carson C. Chow,et al.  Stochastic resonance without tuning , 1995, Nature.

[13]  Kenji Leibnitz,et al.  Biologically inspired self-adaptive multi-path routing in overlay networks , 2006, Commun. ACM.

[14]  Andreas Savvides,et al.  An Empirical Characterization of Radio Signal Strength Variability in 3-D IEEE 802.15.4 Networks Using Monopole Antennas , 2006, EWSN.

[15]  Kurt Wiesenfeld,et al.  Stochastic resonance and the benefits of noise: from ice ages to crayfish and SQUIDs , 1995, Nature.

[16]  Kenji Leibnitz,et al.  Resilient Multi-path Routing Based on a Biological Attractor Selection Scheme , 2006, BioADIT.

[17]  Masatoshi Misono,et al.  Noise-enhanced transmission of information in a bistable system , 1998 .

[18]  Kunihiko Kaneko,et al.  A Generic Mechanism for Adaptive Growth Rate Regulation , 2007, PLoS Comput. Biol..

[19]  Klaus Obermayer,et al.  Activity Driven Adaptive Stochastic Resonance , 2001, NIPS.