self‐organising sensors for wide area surveillance using the max‐sum algorithm
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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 [email protected] Alessandro Farinelli Department of Computer Science University of Verona Verona, Italy - PowerPoint PPT PresentationTRANSCRIPT
Self Organising Sensors for ‐Wide Area Surveillance using the
Max Sum Algorithm‐
Alex Rogers and Nick JenningsSchool of Electronics and Computer Science
University of [email protected]
Alessandro FarinelliDepartment of Computer Science
University of VeronaVerona, Italy
Overview
• Self-Organisation– Landscape of Decentralised Coordination
Algorithms• Local Message Passing Algorithms
– Max-sum algorithm– Graph Colouring
• Wide Area Surveillance Scenario• Future Work
Self-Organisation
Sensors
Self-Organisation
Agents
• Multiple conflicting goals and objectives• Discrete set of possible actions
Self-Organisation
Agents
• Multiple conflicting goals and objectives• Discrete set of possible actions• Some locality of interaction
Self-Organisation
Agents
Maximise Social Welfare:• Multiple conflicting goals and objectives• Discrete set of possible actions• Some locality of interaction
Self-Organisation
Agents
Central point of controlDecentralised 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?
Landscape of Algorithms
Complete Algorithms
DPOPOptAPOADOPT
Communication Cost
Optimality
Iterative Algorithms
Best Response (BR)Distributed Stochastic
Algorithm (DSA) Fictitious Play (FP)
Message Passing
Algorithms
Sum-ProductAlgorithm
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:
Graph Colouring
Agentfunction / utility
variable / state
Graph Colouring Problem Equivalent Factor Graph
Graph Colouring
Equivalent Factor GraphUtility Function
Graph Colouring
Graph Colouring
Optimality
Communication Cost
Robustness to Message Loss
Wide Area Surveillance Scenario
Dense deployment of sensors to detect pedestrian and vehicle activity within an urban environment.
Unattended Ground Sensor
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
Self-Organising Sensor Network
Energy-Aware Sensor Networks
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?