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Description and Composition of Bio-Inspired Design Patterns: The Gradient Case

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3rd Workshop on Bio-Inspired and Self-* Algorithms for Distributed Systems. Slides of the presentation: Description and Composition of Bio-Inspired Design Patterns: The Gradient Case

3rd Workshop on Bio-Inspired and Self-* Algorithms for Distributed Systems. Slides of the presentation: Description and Composition of Bio-Inspired Design Patterns: The Gradient Case

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  • Nowadays, new techlogies are providing us complex infrastructures, such as, Mobile Ad-hoc networks, wireless Sensor networks or Robotic swarm. They are characterized by Openness, large scale, and unpredictability. Such infrastructures allow the implementation of a wide range of new applications, such as, (ad-hoc communication infrastructure), disater prevention (toxic cloud monitorisation, tsunami detection), etc.. These applications presents important requirements, namely, Scalability and Robustness where traditional approaches can’t be applied.
  • Since long, Self-organisation has been study in Physics and Biology. This studies provided a good description of the organising activities and their emergent behaviours. Main examples are Ant colonies, Steam cells, School of fish or flocking, Bacteria.
  • Hovering information model that is our main model for dynamic storing data in mobile environments. WE make contributions…..
  • Poner ejemplo de las pheromonas….
  • \\item \\textbf{Agents}: autonomous and pro-active software entities running in a host. \\item \\textbf{Infrastructure}: the infrastructure is composed by a set of connected Hosts and Infrastructural Agents. A \\textbf{Host} is an entity with computational power, communication capabilities and may have sensors and actuators. Hosts provide services to the agents. An \\textbf{Infrastructural Agent} is an autonomous and pro-active entity, acting over the system at the infrastructure level. Infrastructural agents may be in charge of implementing those environmental behaviors present in nature, such as diffusion, evaporation, aggregation, etc. \\item \\textbf{Environment}: The Environment is the space where the Infrastructure is located. An \\textbf{Event} is a phenomenon of interest that appears in the Environment and that may be sensed by the Agents using the sensors provided by the Hosts.
  • Analizando la literatura…. Como los modulos se pueden reutilizar. Ejemplo the gossip optimizado.
  • When we apply the mechanism Where the mechanism has been applied… Environment moves to When?
  • When we apply the mechanism Where the mechanism has been applied… Environment moves to When?
  • When we apply the mechanism Where the mechanism has been applied… Environment moves to When?
  • When we apply the mechanism Where the mechanism has been applied… Environment moves to When?
  • When we apply the mechanism Where the mechanism has been applied… Environment moves to When?
  • When we apply the mechanism Where the mechanism has been applied… Environment moves to When?
  • heoretical and practical framework for the decentralized development and execution of self-aware and adaptive services for future and emerging pervasive network scenarios A framework composed of an ecosystem of services for

Description and Composition of Bio-Inspired Design Patterns: The Gradient Case Description and Composition of Bio-Inspired Design Patterns: The Gradient Case Presentation Transcript

  • Description and Composition of Bio-Inspired Design Patterns: The Gradient Case Jose Luis Fernandez-Marquez University of Geneva, Switzerland Joseluis.fernandez@unige.ch http://iss.unige.ch In collaboration with: Giovanna Di Marzo Serugendo – University of Geneva, Switzerland Josep Lluis Arcos – IIIA-CSIC, Barcelona, Spain Mirko Viroli - University of Bologna, Italy Sara Montagna - University of Bologna, Italy 1
  • Outline  Motivation  Goal  Bio-Inspired Design Patterns   Gradient Pattern   Chemotaxis Pattern  Applications  Framework: SAPERE Project  Conclusions and Future research 2
  • Motivation   Characterized by:   Large Scale   Openness   Unpredictability   Wide range of new applications   Requirements:   Scalability   Robustness Traditional Approaches (centralised, not distributed) 3
  • Motivation  Bio-Inspired Self-Organising mechanisms have been applied in those infrastructures, achieving results that go beyond traditional approaches, (ACO, PSO, flocking, Digital pheromones….) . However,  The knowledge and experience on how, when, and where to use them is spread across the corresponding literature.   It is very difficult to grasp what are their capabilities and weakness. 4
  • Goal  To analyse existing literature, providing a catalogue of Bio-inspired Mechanisms for Self-Organizing Systems.  To describe those mechanisms as design patterns, identifying how, where, and when to be applied.  Identify the relationship between the presented mechanisms, providing a better description and making it easier to compose new patterns or adapt the existing patterns to solve new problems.  Demonstrate the applicability of those mechanisms tackling with different domains:   Dynamic Optimization   Spatial Computing   Sensor Networks 5
  • Bio-Inspired Design Pattern 6
  • Bio-Inspired Design Pattern 7
  • Bio-Inspired Design Patterns Flocking Foraging Quorum Sensing Chemotaxis Morphogenesis Digital Pheromones Gradients GossipRepulsion Evaporation Replication Aggregation Spreading 8
  • Bio-Inspired Design PatternsPattern Description When Where How Typical Case SolutionProblemEnvironment Known Uses Entities / Dynamics Consequences Implementation ForcesAliases Biological Inspiration Related Patterns 9
  • The Gradient Pattern  Problem: Large systems suffer from lack of global knowledge to estimate the consequences of the actions performed by other agents beyond their communication range.  Solution: Information spreads from the location it is initially deposited and aggregates when it meets other information. During spreading, additional information about the senders distance and direction is provided: either through a distance value (incremented or decremented); or by modifying the information to represent its concentration (lower concentration when information is further away).  Abstract transition rule: 10
  • The Gradient Pattern  Dynamics: 11
  • The Gradient Pattern  Dynamics: 12
  • The Chemotaxis Pattern  Problem: Decentralised motion coordination aiming at detecting sources or boundaries of events.  Solution: Agents locally sense gradient information and follow the gradient in a specified direction (either follow higher gradient values, lower gradient values, or equipotential lines of gradients).  Abstract transition rule: 13
  • The Chemotaxis Pattern  Dynamics: 14
  • Applications  Dynamic Optimisation:   We extended PSO with the Evaporation Pattern to deal with dynamic and noisy optimisation.  Hovering Information in Spatial Computing:   We defined and analysed a collection of algorithms based on the Replication Pattern and the Repulsion Pattern, for persistent storage of information at specific geographical areas.  Detecting Diffuse Events Sources   We implemented the Chemotaxis Pattern for localizing dynamically changing diffuse events using WSN. 15
  • Framework: SAPERE project   Theoretical and practical framework for decentralized development and execution of self-aware and adaptive services for future and emerging pervasive network scenarios.   Chemical Interactions among Services   Smooth data/service distinction   Spontaneous interactions of available services   Bio-chemical reactions   Middleware for Android phones / tablets   Context-awareness (user, situation recognition)   Case Study   Focus on public/private displays for crowd steering   Domains   Context-Aware Advertisement, Crowd Steering, User guidance   EU Funded Project (SAPERE: http://www.sapere-project.eu)FACULTÉ DES SCIENCES U Bologna, U Modena, U Linz, U St-Andrews   Collaboration: U Geneva,ÉCONOMIQUES ET SOCIALES   2010-2013Département des Hautes Etudes Commerciales -HEC
  • Framework: SAPERE project Crowd Steering through Self-Organising Public Displays •  Collaborative displays •  Self-organising spontaneous interactions   Bio-inspired (gradients, gossip, stigmergy, flocking)FACULTÉ DES SCIENCESÉCONOMIQUES ET SOCIALESDépartement des Hautes Etudes Commerciales -HEC
  • Conclusions  This work is a step forward for engineering self-Organising Systems.  We presented a catalogue of bio-inspired Self-Organising mechanisms, as design patterns.  We analysed the relations between the mechanisms, making easier their composition and adaptation to solve new problems.  We contributed in different domains using bio-inspired Self-Organising Mechanisms:   Dynamic Optimisation (Evaporation mechanism)   Sensor Networks (Chemotaxis mechanism)   Spatial Computing (Replication + repulsion) 18
  • Future Works  SAPERE Project:   Add New Patterns in the catalogue.   Self-Adaptation of parameters.   Self-Composition of patterns.   Implementation of services. 19
  • Any questions?Thank you for your attention! Jose Luis Fernandez-Marquez Joseluis.fernandez@unige.ch 20