02289oam 2200445zu 450 99620325430331620210806235834.01-5090-9770-8(CKB)1000000000022675(SSID)ssj0000453788(PQKBManifestationID)12128878(PQKBTitleCode)TC0000453788(PQKBWorkID)10481690(PQKB)11337283(NjHacI)991000000000022675(EXLCZ)99100000000002267520160829d2005 uy engur|||||||||||txtccr2005 4th International Workshop on Information Processing in Sensor Networks[Place of publication not identified]I E E E20051 online resourceBibliographic Level Mode of Issuance: Monograph0-7803-9201-9 We 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.Information networksCongressesMultisensor data fusionCongressesSensor networksCongressesInformation networksMultisensor data fusionSensor networks025.04PQKBPROCEEDING9962032543033162005 4th International Workshop on Information Processing in Sensor Networks2511805UNISA