Title :
Asymptotically Optimal Distributed Censoring
Author :
Tay, Wee-Peng ; Tsitsiklis, John N. ; Win, Moe Z.
Author_Institution :
Lab. for Inf. & Decision Syst., Massachusetts Inst. of Technol., Cambridge, MA
Abstract :
We consider the problem of Bayesian decentralized binary detection in a sensor network in which the sensors have access to some side information that affects the statistics of the measurements they make. Sensors can decide whether or not to make a measurement and transmit a message to the fusion center ("censoring"), and also have a choice of the transmission function from measurements to messages. We consider the case of a large number of sensors, characterize the optimal error exponent, and derive asymptotically optimal strategies. We show that the optimal strategy consists of dividing the sensors into two groups, with sensors in each group using the same policy
Keywords :
Bayes methods; sensor fusion; wireless sensor networks; Bayesian decentralized binary detection; asymptotically optimal distributed censoring; fusion center; sensor network; Bayesian methods; Cost function; Extraterrestrial measurements; Face detection; Laboratories; Random variables; Sensor fusion; Sensor phenomena and characterization; Sensor systems; Statistics;
Conference_Titel :
Information Theory, 2006 IEEE International Symposium on
Conference_Location :
Seattle, WA
Print_ISBN :
1-4244-0505-X
Electronic_ISBN :
1-4244-0504-1
DOI :
10.1109/ISIT.2006.261860
