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[null,"1.","Record Nr.",null,"UNISA996203254303316"]
[null,null,"Titolo",null,"2005 4th International Workshop on Information Processing in Sensor Networks"]
[null,null,"Pubbl/distr/stampa",null,"[Place of publication not identified], : I E E E, 2005"]
[null,null,"ISBN",null,"1-5090-9770-8"]
[null,null,"Descrizione fisica",null,"1 online resource"]
[null,null,"Disciplina",null,"025.04"]
[null,null,"Soggetti",null,"Information networks"]
[{"$nulls":4},"Multisensor data fusion"]
[{"$nulls":4},"Sensor networks"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Sommario/riassunto","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."]