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