From biology to evolve-able pervasive ICT systems

The emergence of future pervasive ICT environments, characterized by the massive proliferation of embedded electronic devices with communication and computing capabilities, are posing enormous challenges to existing approaches to networking, service provisioning and system management. In this paper, we make a case to go for biology as possible source of novel, revolutionary design patterns suitable to large-scale highly dynamic ICT systems. We also present a case study, the EU- funded BIONETS project, which tackles the increased complexity of large-scale ubiquitous systems by applying an evolutionary framework to the provisioning of pervasive services.

[1]  Imrich Chlamtac,et al.  BIONETS: BIO-inspired NExt generaTion networkS , 2004, WAC.

[2]  Mark Weiser,et al.  The computer for the 21st Century , 1991, IEEE Pervasive Computing.

[3]  Diomidis Spinellis,et al.  A survey of peer-to-peer content distribution technologies , 2004, CSUR.

[4]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[5]  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.

[6]  C. Tschudin.  Fraglets – a Metabolistic Execution Model for Communication Protocols , 2003 .

[7]  Diane J. Cook,et al.  Smart environments - technology, protocols and applications , 2004 .

[8]  Peter Dittrich,et al.  Chemical Computing , 2004, UPP.

[9]  G. Krauss.  Biochemistry of signal transduction and regulation , 1999 .

[10]  Devavrat Shah,et al.  Throughput-delay trade-off in energy constrained wireless networks , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

[11]  Christian Becker,et al.  An epidemic model for information diffusion in MANETs , 2002, MSWiM '02.

[12]  Daniele Miorandi,et al.  Evolutionary Epidemic Routing , 2007 .

[13]  J. Foster.  Computational genetics: Evolutionary computation , 2001, Nature Reviews Genetics.

[14]  Leandro Nunes de Castro,et al.  Artificial Immune Systems: A New Computational Approach , 2002 .

[15]  Imrich Chlamtac,et al.  Mobile ad hoc networking: imperatives and challenges , 2003, Ad Hoc Networks.

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

[17]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[18]  Ellen W. Zegura,et al.  Message ferry route design for sparse ad hoc networks with mobile nodes , 2006, MobiHoc '06.

[19]  James H. Aylor,et al.  Computer for the 21st Century , 1999, Computer.

[20]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[21]  Marco Dorigo.  Ant colony optimization , 2004, Scholarpedia.

[22]  David Tse,et al.  Mobility increases the capacity of ad-hoc wireless networks , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[23]  Wolfgang Banzhaf,et al.  Genetic Programming: An Introduction , 1997 .

[24]  Roberto Montemanni,et al.  Design patterns from biology for distributed computing , 2006, TAAS.

[25]  Stuart A. Kauffman,et al.  The origins of order , 1993 .

[26]  Panganamala Ramana Kumar,et al.  RHEINISCH-WESTFÄLISCHE TECHNISCHE HOCHSCHULE AACHEN , 2001 .

[27]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[28]  Tarek Abdelzaher.  Towards an Architecture for Distributed Cyber-Physical Systems , 2006 .