agent-based coordination of sensor networks
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Agent-Based Coordination of Sensor Networks. Alex Rogers School of Electronics and Computer Science University of Southampton [email protected]. Overview. Decentralised Coordination Landscape of Algorithms Optimality vs Communication Costs Local Message Passing Algorithms - PowerPoint PPT PresentationTRANSCRIPT
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Agent-Based Coordination of Sensor Networks
Alex Rogers
School of Electronics and Computer ScienceUniversity of Southampton
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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
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Decentralised Coordination
Agents
• Multiple conflicting goals and objectives• Discrete set of possible actions• Some locality of interaction
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Decentralised Coordination
Sensors
• Multiple conflicting goals and objectives• Discrete set of possible actions• Some locality of interaction
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Decentralised Coordination
Agents
• Multiple conflicting goals and objectives• Discrete set of possible actions• Some locality of interaction
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Decentralised Coordination
Agents
Central point of controlDecentralised 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?
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Landscape of Algorithms
Complete Algorithms
DPOPOptAPOADOPT
Communication Cost
Optimality
Probability Collectives
Iterative Algorithms
Best Response (BR)Distributed Stochastic
Algorithm (DSA) Fictitious Play (FP)
Message Passing
Algorithms
Sum-ProductAlgorithm
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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:
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Graph Colouring
Agentfunction / utility
variable / state
Graph Colouring Problem Equivalent Factor Graph
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Graph Colouring
Equivalent Factor Graph
Utility Function
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Max-Sum Calculations
Variable to Function: Information aggregation
Function to Variable: Marginal Maximisation
Decision:Choose state that maximises
sum of all messages
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Graph Colouring
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Graph Colouring
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Optimality
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Communication Cost
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Robustness to Message Loss
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Hardware Implementation
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Energy-Aware Sensor Networks
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Wide Area Surveillance Scenario
Dense deployment of sensors to detect pedestrian and vehicle activity within an urban environment.
Unattended Ground Sensor
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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
time
duty cycle
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Energy-Aware Sensor Networks
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Energy-Aware Sensor Networks
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Empirical Evaluation
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Autonomous Mobile Sensors
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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
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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.
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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
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Questions?