Biologically Inspired Networking

The development of computer networks has seen a paradigm shift from static, hierarchical network structures to highly distributed, autonomous systems without any form of centralized control. For networking nodes, the ability to self-adapt and self-organize in a changing environment has become a key issue. In conventional network structures, e.g., the Internet, there is usually a hierarchical order with centralized and static control. For example, hosts are aggregated to local area networks (LANs), which are connected via gateways to wide area networks (WANs) and network domains, etc., all using static connections and addressing. Recently, however, the trend leads more and more to networks that dynamically set up connections in an ad-hoc manner. Mobile ad-hoc networks (MANETs) are a prominent example, but also overlay structures such as peer-to-peer (P2P) networks require a scalable, robust and fully distributed operation with self-adaptive and self-organizing control mechanisms. The main control functions are no longer performed at intermediate nodes like routers, but shifted to the end-user nodes. Additionally, the location of these nodes may now be no longer static but can be mobile, imposing new challenges on the search for shared information in P2P networks or the location of a node in an ad hoc network. For these types of new dynamic networks, the following three requirements for network control are considered mandatory:

[1]  Tatsuya Suda,et al.  The Bio-Networking Architecture: a biologically inspired approach to the design of scalable, adaptive, and survivable/available network applications , 2001, Proceedings 2001 Symposium on Applications and the Internet.

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

[3]  Thomas C. Henderson,et al.  Reaction-diffusion patterns in smart sensor networks , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[4]  Anne-Marie Kermarrec,et al.  Epidemic information dissemination in distributed systems , 2004, Computer.

[5]  Kenji Leibnitz,et al.  On pollution in eDonkey-like peer-to-peer file-sharing networks , 2006, MMB.

[6]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[7]  Stephanie Forrest,et al.  Infect Recognize Destroy , 1996 .

[8]  James D. Murray Mathematical Biology: I. An Introduction , 2007 .

[9]  S. Strogatz,et al.  Synchronization of pulse-coupled biological oscillators , 1990 .

[10]  Masayuki Murata,et al.  TCP symbiosis: congestion control mechanisms of TCP based on Lotka-Volterra competition model , 2006 .

[11]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Falko Dressler,et al.  Efficient and Scalable Communication in Autonomous Networking using Bio-inspired Mechanisms , 2005, Informatica.

[13]  A. M. Turing,et al.  The chemical basis of morphogenesis , 1952, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences.

[14]  Joseph Y. Halpern,et al.  Gossip-based ad hoc routing , 2002, IEEE/ACM Transactions on Networking.

[15]  Jean-Yves Le Boudec,et al.  An Artificial Immune System for Misbehavior Detection in Mobile Ad-Hoc Networks with Virtual Thymus, Clustering, Danger Signal and Memory Detectors , 2004, Int. J. Unconv. Comput..

[16]  Jonathan Timmis,et al.  "Going Back to our Roots": Second Generation Biocomputing , 2005, Int. J. Unconv. Comput..

[17]  Naoki Wakamiya,et al.  Synchronization-Based Data Gathering Scheme for Sensor Networks , 2005, IEICE Trans. Commun..

[18]  Luca Maria Gambardella,et al.  AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks , 2005, Eur. Trans. Telecommun..

[19]  Patrick Thiran,et al.  Reaction-diffusion based transmission patterns for ad hoc networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[20]  Simon M. Garrett,et al.  How Do We Evaluate Artificial Immune Systems? , 2005, Evolutionary Computation.

[21]  Falko Dressler,et al.  Molecular Processes as a Basis for Autonomous Networking , 2004 .

[22]  Charles E. Perkins,et al.  Ad-hoc on-demand distance vector routing , 1999, Proceedings WMCSA'99. Second IEEE Workshop on Mobile Computing Systems and Applications.

[23]  Marco Dorigo,et al.  AntNet: Distributed Stigmergetic Control for Communications Networks , 1998, J. Artif. Intell. Res..

[24]  Tatsuya Suda,et al.  A middleware platform for a biologically inspired network architecture supporting autonomous and adaptive applications , 2005, IEEE Journal on Selected Areas in Communications.

[25]  Falko Dressler,et al.  Efficient and Scalable Communication in Autonomous Networking using Bio-inspired Mechanisms – An Overview , 2005 .

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

[27]  Atsushi Yoshida,et al.  Cooperative Control Based on Reaction-Diffusion Equation for Surveillance System , 2005, KES.

[28]  Leandro Nunes de Castro,et al.  An Overview of Artificial Immune Systems , 2004 .

[29]  Falko Dressler,et al.  Benefits of Bio-inspired Technologies for Networked Embedded Systems: An Overview , 2006, Organic Computing - Controlled Emergence.

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

[31]  D. Dasgupta Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[32]  Imrich Chlamtac,et al.  BIONETS: Bio-Inspired Networking for Pervasive Communication Environments , 2007, IEEE Transactions on Vehicular Technology.