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 Agents Multiple conflicting goals and objectives Discrete set of possible actions

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

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

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

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

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

10. Graph Colouring Agent function / utility variable / state Graph Colouring ProblemEquivalent Factor Graph

11. Graph Colouring Equivalent Factor Graph Utility Function

12. Graph Colouring

14. Optimality

15. Communication Cost

16. Robustness to Message Loss

17. Wide Area Surveillance Scenario 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. time duty cycle t ime duty cycle

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?