1 / 21

Self‐Organising Sensors for Wide Area Surveillance using the Max‐Sum Algorithm

Self‐Organising Sensors for Wide Area Surveillance using the Max‐Sum Algorithm. Alex Rogers and Nick Jennings School of Electronics and Computer Science University of Southampton acr@ecs.soton.ac.uk Alessandro Farinelli Department of Computer Science University of Verona Verona, Italy

bikita
Download Presentation

Self‐Organising Sensors for Wide Area Surveillance using the Max‐Sum Algorithm

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Self‐Organising Sensors for Wide Area Surveillance using the Max‐Sum Algorithm Alex Rogers and Nick Jennings School of Electronics and Computer Science University of Southampton acr@ecs.soton.ac.uk Alessandro Farinelli Department of Computer Science University of Verona Verona, Italy alessandro.farinelli@univr.it

  2. Overview • Self-Organisation • Landscape of Decentralised Coordination Algorithms • Local Message Passing Algorithms • Max-sum algorithm • Graph Colouring • Wide Area Surveillance Scenario • Future Work

  3. Self-Organisation Sensors

  4. Self-Organisation • Multiple conflicting goals and objectives • Discrete set of possible actions Agents

  5. Self-Organisation • Multiple conflicting goals and objectives • Discrete set of possible actions • Some locality of interaction Agents

  6. Self-Organisation • Multiple conflicting goals and objectives • Discrete set of possible actions • Some locality of interaction Maximise Social Welfare: Agents

  7. Self-Organisation No direct communication Solution scales poorly Central point of failure Who is the centre? Decentralised self-organisation through local computation and message passing. • Speed of convergence, guarantees of optimality, communication overhead, computability Central point of control Agents

  8. Landscape of Algorithms Optimality Complete Algorithms DPOP OptAPO ADOPT Message Passing Algorithms Sum-Product Algorithm Iterative Algorithms Best Response (BR) Distributed Stochastic Algorithm (DSA) Fictitious Play (FP) Communication Cost

  9. Max-Sum Algorithm Find approximate solutions to global optimisation through local computation and message passing: A simple transformation: allows us to use the same algorithms to maximise social welfare: Factor Graph Variable nodes Function nodes

  10. Graph Colouring Graph Colouring Problem Equivalent Factor Graph Agent function / utility variable / state

  11. Graph Colouring Utility Function Equivalent Factor Graph

  12. Graph Colouring

  13. Graph Colouring

  14. Optimality

  15. Communication Cost

  16. Robustness to Message Loss

  17. Wide Area SurveillanceScenario Dense deployment of sensors to detect pedestrian and vehicle activity within an urban environment. Unattended Ground Sensor

  18. Energy Constrained Sensors Maximise event detection whilst using energy constrained sensors: • Use sense/sleep duty cycles to maximise network lifetime of maintain energy neutral operation. • Coordinate sensors with overlapping sensing fields. duty cycle time duty cycle time

  19. Self-Organising Sensor Network

  20. Energy-Aware Sensor Networks

  21. Future Work • Continuous action spaces • Max-sum calculations are not limited to discrete action space • Can we perform the standard max-sum operators on continuous functions in a computationally efficient manner? • Bounded Solutions • Max-sum is optimal on tree and limited proofs of convergence exist for cyclic graphs • Can we construct a tree from the original cyclic graph and calculate an lower bound on the solution quality?

More Related