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As the connectivity of consumer devices is rapidly growing and cloudcomputing technologies are becoming more widespread, cloud-aided techniques forparameter estimation can be designed to exploit the theoretically unlimitedstorage memory and computational power of the cloud, while relying oninformation provided by multiple sources. With the ultimate goal of developingmonitoring and diagnostic strategies, this report focuses on the design of aRecursive Least-Squares (RLS) based estimator for identification over a groupof devices connected to the cloud. The proposed approach, that relies onNode-to-Cloud-to-Node (N2C2N) transmissions, is designed so that: (i) estimatesof the unknown parameters are computed locally and (ii) the local estimates arerefined on the cloud. The proposed approach requires minimal changes to local(pre-existing) RLS estimators.

Cloud-aided collaborative estimation by ADMM-RLS algorithms for connected vehicle prognostics