Riassunto:
This volume contains the papers from BIOWIRE 2007, the first in a series of wo- shops on the bio-inspired design of networks, and additional papers contributed from the research area of bio-inspired computing and communication. The workshop took place at the University of Cambridge during April 2–5, 2007 with sponsorship from the US/UK International Technology Alliance in Network and Information Sciences. Its objective was to present, discuss and explore the recent developments in the field of bio-inspired design of networks, with particular regard to wireless networks and the self-organizing properties of biological networks. The workshop was organized by Jon Crowcroft (University of Cambridge), Don Towsley (University of Massachusetts), Dinesh Verma (IBM T. J. Watson Research Center), Vasilis Pappas (IBM T. J. Watson Research Center), Ananthram Swami (ARL), Tom McCutcheon (DSTL) and Pietro Liò (University of Cambridge). The program for BIOWIRE 2007 included 54 speakers covering a diverse range of topics, categorized as follows: 1. Self-organized communication networks in insects 2. Neuronal communications 3. Bio-computing 4. Epidemiology 5. Network theory 6. Wireless and sensorial networks 7. Brain: models of sensorial integration The BIOWIRE workshop focuses on achieving a common ground for knowledge sharing among scientists with expertise in investigating the application domain (e. g. , biological, wireless, data communication and transportation networks) and scientists with relevant expertise in the methodology domain (e. g. , mathematics and statistical physics of networks).
Contenuti:
Bio-Inspired Computing and Communication.- Biological Networks.- A Complex Network Approach to the Determination of Functional Groups in the Neural System of C. Elegans.- Modelling Gene Regulatory Networks.- The Role of Simplifying Models in Neuroscience: Modelling Structure and Function.- An Artificial Chemistry for Networking.- Biomimicry: Further Insights from Ant Colonies?.- Network-Related Challenges and Insights from Neuroscience.- Network Epidemics.- Networks in Epidemiology.- Epidemiology and Wireless Communication: Tight Analogy or Loose Metaphor?.- Epidemic Spreading of Computer Worms in Fixed Wireless Networks.- Wireless Epidemic Spread in Dynamic Human Networks.- Complex Networks.- Stochastic Spreading Processes on a Network Model Based on Regular Graphs.- Weighted and Directed Network on Traveling Patterns.- Communication Networks in Insect Societies.- The Topological Fortress of Termites.- Evolutionary and Temporal Dynamics of Transcriptional Regulatory Networks.- Phase Patterns of Coupled Oscillators with Application to Wireless Communication.- Self-organizing Desynchronization and TDMA on Wireless Sensor Networks.- Bio-Inspired Network Model.- Bio-Inspired Multi-agent Collaboration for Urban Monitoring Applications.- Bio-Inspired Approaches for Autonomic Pervasive Computing Systems.- Biologically Inspired Self Selective Routing with Preferred Path Selection.- Biologically Inspired Approaches to Networks: The Bio-Networking Architecture and the Molecular Communication.- Network Protocol in Wireless Communication.- User-Centric Mobility Models for Opportunistic Networking.- Wavelet-Domain Statistics of Packet Switching Networks Near Traffic Congestion.- A Circulatory System Approach for Wireless Sensor Networks.- Epcast: Controlled Dissemination in Human-Based Wireless Networks Using Epidemic Spreading Models.- Maintaining Spatial-Temporal Knowledge through Human Interaction.- Data Management.- Beta Random Projection.- Biologically Inspired Classifier.- Distributed Computing.- Human Heuristics for Autonomous Agents.- Designing Biological Computers: Systemic Computation and Sensor Networks.- A Rule System for Network-Centric Operation in Massively Distributed Systems.- Field-Based Coordination for Pervasive Computing Applications.- Coalition Games and Resource Allocation in Ad-Hoc Networks.- Security.- Bio-Inspired Topology Maintenance Protocols for Secure Wireless Sensor Networks.- Dynamic Topologies for Robust Scale-Free Networks.
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