Difference between revisions of "Lakeside Research Days'09"

Revision as of 15:16, 13 July 2009

Prolog

This meeting has been held on Friday, 19th of June 2009, 9AM - 1PM. The overall aim of this meeting was to collect ideas/problems which should be tackled during the Lakeside Research Days'09.

Participants: Christian Hofbauer (NES/ES), Alexander Onic (NES/ES), Markus Reichhartinger (SST/CM), Evsen Yanmaz (NES/MS), Johannes Klinglmayr (NES/MS), Istvan Fehervari (NES/MS), Kostyantyn Shchekotykhin (AINF/ISBI), Simon Triebenbacher (SST/AM), Markus Quaritsch (NES/PC), Helmut Adam (NES/MS), Laszlo Böszörmenyi (ITEC), Gerhard Friedrich (AINF/ISBI)

Research Days'09

The Lakeside Research Days 2009 take place as a five days workshop from July 13 to July 17, 2009.

Tentative workshop schedule:

Register to the workshop via doodle.

Group Works

Group Work 1

Group 2 - Bettstetter, Hofbauer, Marchenko, Tusch

Selected case study for robust self-organizing systems: Parliament

The parliament is (up to a certain minimum size) robust against instant and repeating (spreading)

• failures/removals of nodes (i.e., representatives)
• faulty/unexpected/unwanted behaviour of nodes, and
• malicious behaviour of nodes.

It shows the following properties:

• Self-healing due to iterated renewals based on elections
• External observation from mass press and citizens
• Well-defined structure and function
  • Mandate distribution is defined by distributions of votes
  • Government is defined by major number of mandates (mostly by coalitions)
  • Goal is to make decisions and laws
• Each representative may act as attractor, detractor, or neutral node
• Adaptive to change in the social environment
• Shows the emergent property of issued laws initiated by some voters, set up by its representatives, and valid for all citizens
• Fullfills all attributes of dependability: high availability, high reliability, safe, maintable, and secure

Rotusch 13:25, 13 July 2009 (UTC)

Group 4

Selected case studies for robust self-organizing systems

• Ant Nest
  • Goal
    • dependably find food and bring it back to the nest
  • Mechansim
    • randomly explore the surroundings leave trails from foodsources back to the nest (via pheromone)
    • follow the pheromone trail with the highest pheromone concentration - but allow for deviations from that path
  • Robustness
    • against dynamic food location changes (food churn)
    • against obstacles appearing on the path
    • against ants disappearing
  • Problems
    • degradation of robustness if high concentration of artificial pheromones are deposited
• P2P Networks (Example: File Sharing)
  • Goal
    • provide data items to participating peers
  • Mechansim
    • distributed storage of data items on the peer nodes
    • distributed reference infos on the peer nodes (normally via DHT)
  • Robustness
    • against leaving/failing and joining peer nodes (node churn)
    • against bottlenecks in underlying IP network
  • Problems
    • attacks
    • manipulations
    • malicious nodes

Group 5

• Self-organizing traffic lights
  • Waiting time depends on number of cars waiting (only sensor being a single camera)
  • No explicit communication between the lights - communication media = cars
  • Emergence of grups of cars that propagate on a green wave
  • Robust to changes in traffic situation, break-down of single lights (if they fall into a fail-safe state, such as a flashing yellow light or shutting down completely).
  • Discussion: Depends a lot on the density of the traffic (jamming situations). Explicit communication for traffic may be desirable, even though that may go against the self-organizing nature of the system.
• Self-organizing network routing
  • Any routing protocol broadcasting to discover a specific route (example: choose route which contains the packet with the highest TTL)
  • Ant routing
  • Robustness: break-down of nodes/lines, overload in parts of the network are handled in a self-organized way.
  • Limits: Package loss (needs to be handled by the upper layers)
• Viruses and worms (whether this is truly a self-organizing network was left open for discussion)
  • Worms start out as sngle "agent" that broadcasts itself throughout the network
  • Infected network-node is closed and can't be infected again.
  • Is this "epidemic" distribution a kind of self-organization?
    • Multiple agents
    • Distribution
  • But:
    • Emergence of structure?
    • Adaptability to changes from the environment?

Swieser 13:55, 13 July 2009 (UTC)

Scheduled Talks

Introduction to Self-Organizing Systems

F. Heylighen

Robustness and Dependability

W. Elmenreich

Self-Organization in Modular Robotics and Wireless Sensor Networks

A. van Rossum

Almende is a research company, based in Rotterdam, with a special focus on self-organization. In the form of a communication platform this is implemented as feedback of users of the system that subsequently reconfigures communication channels between them or service providers. A wireless sensor network takes this concept further in the form of a Kohonen network implemented on a wireless sensor nodes, that is able to cluster certain patterns which can be used for intruder detection or toilet management. At the end of the spectrum of self-organization lies modular robotics in which a Itti-Koch sensor fusion architecture is implemented, with an underlying evo-devo engine. Without human design changes in the topology are tested within such a developmental paradigm, so certain symmetries remain preserved across mutations. The developmental engine is a gene regulatory network as build by Bongard and the interactions between genes and their products is one of the most salient examples of self-organization at work. In engineering this type of non-linearities and dynamics is seen more and more as a tool, rather then a problem. There is however a real demand for thorough mathematical approaches similar to e.g. renormalization theory and applicable to real world problems and software.

High resolution dynamical mapping of social interactions with active RFID

A. Barrat, C. Cattuto, V. Colizza, J-F Pinton, W. van den Broeck, A. Vespignani

In this paper we present an experimental framework to gather data on face-to-face social interactions between individuals, with a high spatial and temporal resolution. We use active Radio Frequency Identification (RFID) devices that assess contacts with one another by exchanging low-power radio packets. When individuals wear the beacons as a badge, a persistent radio contact between the RFID devices can be used as a proxy for a social interaction between individuals. We present the results of recent pilot studies performed during conferences, and a subsequent preliminary data analysis, that provides an assessment of our method and highlights its versatility and applicability in many areas concerned with human dynamics.

Self-Organizing Networked Control Systems

C. Zschoppe

Vortrag Passau

H. de Meer

Self-organization: phase transitions, percolation and jamming

R. de Souza

Self-organization comes in many forms and seems to lack precise definition. Perhaps universal to all the phenomena is the formation of order from purely local interactions. Yet, local structures may also be influenced by global constraints. Here we review 1) a simple model of granular flow showing how the interplay of local and global constraints can delay the onset of jamming; and 2) limited pertubations that change the nature of the percolation transition in random networks. I will also attempt to discuss the robustness (or lack thereof) of these dynamical processes.

Synchronization and Spatio-Temporal Patterns in Neural Networks

M. Timme

Patterns of precisely timed and spatially distributed spikes have been experimentally observed in different neuronal systems. These spike patterns correlate with external stimuli and internal states and are thus considered key features of neural computation. Their dynamical origin, however, is unclear. One possible explanation for their occurrence is the existence of excitatorily coupled feed-forward structures, synfire chains, which are embedded in a network of otherwise random connectivity and receive a large number of random external inputs. We here show how precise spike timing and temporal locking can naturally arise in the nonlinear dynamics of recurrent neural networks that contain no additionally embedded feed-forward structures.

Robustness in Synchronization

C. Bettstetter and J. Klinglmayr