Title :
Bayesian Detection in Bounded Height Tree Networks
Author :
Tay, Wee-Peng ; Tsitsiklis, John N. ; Win, Moe Z.
Author_Institution :
Lab. for Inf. & Decision Syst., MIT, Cambridge, MA
Abstract :
We study the asymptotic detection performance of large sensor networks, configured as trees with bounded height, in which information is progressively compressed as it moves towards the root of the tree. We show that the error probability decays exponentially fast, and we provide bounds for the error exponent. We analyze further the case where the tree has certain symmetry properties, and derive simple, easily implementable, suboptimal strategies
Keywords :
Bayes methods; error statistics; sensor fusion; trees (mathematics); wireless sensor networks; Bayesian detection; asymptotic detection performance; bounded height tree networks; error probability; large sensor networks; symmetry properties; Bayesian methods; Contracts; Costs; Data compression; Error probability; Laboratories; Network topology; Sensor fusion; Sensor systems; Ultra wideband communication;
Conference_Titel :
Data Compression Conference, 2007. DCC '07
Print_ISBN :
0-7695-2791-4
DOI :
10.1109/DCC.2007.16
