Abstract
Decentralizing optimization problems across a network can reduce the time required to achieve a solution. We consider a wide-area surveillance (WAS) 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.
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© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Ebden, M., Roberts, S. (2010). Graph Marginalization for Rapid Assignment in Wide-Area Surveillance. In: Zheng, J., Mao, S., Midkiff, S.F., Zhu, H. (eds) Ad Hoc Networks. ADHOCNETS 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 28. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11723-7_47
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DOI: https://doi.org/10.1007/978-3-642-11723-7_47
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