LEADER 02289oam 2200445zu 450 001 996203254303316 005 20210806235834.0 010 $a1-5090-9770-8 035 $a(CKB)1000000000022675 035 $a(SSID)ssj0000453788 035 $a(PQKBManifestationID)12128878 035 $a(PQKBTitleCode)TC0000453788 035 $a(PQKBWorkID)10481690 035 $a(PQKB)11337283 035 $a(NjHacI)991000000000022675 035 $a(EXLCZ)991000000000022675 100 $a20160829d2005 uy 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$a2005 4th International Workshop on Information Processing in Sensor Networks 210 31$a[Place of publication not identified]$cI E E E$d2005 215 $a1 online resource 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-7803-9201-9 330 $aWe consider a network of distributed sensors, where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximum-likelihood estimate of the parameters. This scheme doesn't involve explicit point-to-point message passing or routing; instead, it diffuses information across the network by updating each node's data with a weighted average of its neighbors' data (they maintain the same data structure). At each step, every node can compute a local weighted least-squares estimate, which converges to the global maximum-likelihood solution. This scheme is robust to unreliable communication links. We show that it works in a network with dynamically changing topology, provided that the infinitely occurring communication graphs are jointly connected. 606 $aInformation networks$vCongresses 606 $aMultisensor data fusion$vCongresses 606 $aSensor networks$vCongresses 615 0$aInformation networks 615 0$aMultisensor data fusion 615 0$aSensor networks 676 $a025.04 801 0$bPQKB 906 $aPROCEEDING 912 $a996203254303316 996 $a2005 4th International Workshop on Information Processing in Sensor Networks$92511805 997 $aUNISA