Graph marginalization for rapid assignment in wide-area surveillance

Decentralizing optimization problems across a network can reduce the time required to achieve a solution. We consider a wide-area surveillance sensor network observing an environment by varying the state of each sensor so as to assign it to one or more moving objects. The aim is to maximize an arbit...

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Veröffentlicht in:Ad hoc networks Jg. 9; H. 2; S. 180 - 188
Hauptverfasser: Ebden, Mark, Roberts, Stephen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.03.2011
Schlagworte:
Agents
Belief propagation
Coalition formation
Computer simulation
Graphs
Max-sum algorithm
Networks
Optimization
Sensor networks
Sensors
Surveillance
Tracking
Utilities
Sensor networks
Coalition formation
Agents
Max-sum algorithm
Belief propagation
ISSN:1570-8705, 1570-8713
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Decentralizing optimization problems across a network can reduce the time required to achieve a solution. We consider a wide-area surveillance sensor network observing an environment by varying the state of each sensor so as to assign it to one or more moving objects. The aim is to maximize an arbitrary utility function related to object tracking or object identification, using graph marginalization in the form of belief propagation. The algorithm performs well in an example application with six heterogeneous sensors. In larger network simulations, the time savings owing to decentralization quickly exceed 90%, with no reduction in optimality.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1570-8705
1570-8713
DOI:10.1016/j.adhoc.2010.06.002