Volume 62, Number 2, April 2003
|Page(s)||189 - 195|
|Published online||01 April 2003|
Similarities between communication dynamics in the Internet and the autonomic nervous system
Center for Polymer Studies and Department of Physics, Boston
University Boston, MA 02215, USA
2 NTT Network Innovation Laboratories - Tokyo, 180-8585, Japan
3 Department of Chemical Engineering, Northwestern University Evanston, IL 60208, USA
Accepted: 21 February 2003
The Internet is a world-wide communication network, whose optimization depends on the knowledge of the statistical characterization of the aggregated traffic flow. Internet traffic is dependent on a number of factors, including communication protocols, network topology, and human behavior. Using a recently proposed segmentation algorithm, we find a surprising analogy between the nonstationarity and the correlations in the communication dynamics in the Internet and in another communication network of great interest: the autonomic nervous system (ANS). The ANS controls involuntary muscle motion, secreting glands, and the heart, hence, we surmise that the time interval between successive heartbeats —an easily measured physiological signal— provides a probe of the communication dynamics for the ANS. We find quantitative similarities between the statistical properties of i) healthy heart rate variability and non-congested Internet traffic, and ii) diseased heart rate variability and congested Internet traffic. Our findings suggest that the understanding of the mechanisms underlying the “human-made” Internet could help to understand the “natural” network that controls the heart.
PACS: 05.45.-a – Nonlinear dynamics and nonlinear dynamical systems / 05.45.Tp – Time series analysis
© EDP Sciences, 2003
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.