1. Agent-Based Coordination of Sensor Networks Alex Rogers School of Electronics and Computer Science University of Southampton acr@ecs.soton.ac.uk

2. Overview Decentralised Coordination Landscape of Algorithms –Optimality vs Communication Costs Local Message Passing Algorithms –Max-sum algorithm –Graph Colouring Example Application –Wide Area Surveillance Scenario Future Work & Sensor Testbed

3. Decentralised Coordination Agents Multiple conflicting goals and objectives Discrete set of possible actions Some locality of interaction

4. Decentralised Coordination Sensors Multiple conflicting goals and objectives Discrete set of possible actions Some locality of interaction

5. Decentralised Coordination Agents Multiple conflicting goals and objectives Discrete set of possible actions Some locality of interaction

6. Decentralised Coordination Agents Central point of control Decentralised control and coordination 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?

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

8. Sum-Product 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:

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

10. Graph Colouring Equivalent Factor Graph Utility Function

11. Max-Sum Calculations Variable to Function: Information aggregation Function to Variable: Marginal Maximisation Decision: Choose state that maximises sum of all messages

12. Graph Colouring

14. Optimality

15. Communication Cost

16. Robustness to Message Loss

17. Hardware Implementation

18. Energy-Aware Sensor Networks

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

20. 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

21. Energy-Aware Sensor Networks

23. Empirical Evaluation

24. Autonomous Mobile Sensors

25. Future Work Continuous action spaces –Not limited to discrete actions Bounded Solutions –Prune edges from the cyclic factor graph to reveal a tree –Run Max-Sum on this tree –Calculate a bound on how far this solution is from the real optimal solution Factor Graph

26. Publications Farinelli, A., Rogers, A., Petcu, A. and Jennings, N. R. (2008) Decentralised Coordination of Low-Power Embedded Devices Using the Max-Sum Algorithm. In: Proceedings of the Seventh International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-08), Estoril, Portugal. Waldock, A., Nicholson, D. and Rogers, A. (2008) Cooperative Control using the Max-Sum Algorithm. In: Proceedings of the Second International Workshop on Agent Technology for Sensor Networks, Estoril, Portugal. Farinelli, A., Rogers, A. and Jennings, N. (2008) Maximising Sensor Network Efficiency Through Agent-Based Coordination of Sense/Sleep Schedules. In: Proceedings of the Workshop on Energy in Wireless Sensor Networks in conjunction with DCOSS 2008, Santorini, Greece.

27. SunSPOT Network Chipcon 2431 SoC –8051 processor, 8KB RAM SunSPOT network –Java enabled, 180 MHz 32bit ARM –Accelerometers, light, temperature sensors –Programming over-the-air

28. Questions?