Distributed estimation strategies over wireless sensor networks are one of the active areas
of research due to the wide range of applications in a variety of fields ranging from …

The aim of this paper is to propose a resource allocation strategy for dynamic training and
inference of machine learning tasks at the edge of the wireless network, with the goal of …

In this paper, a novel widely linear complex-valued estimated-input adaptive filter (WLC-
EIAF) is first proposed for processing noisy input and output data in the complex domain …

In this paper, we will analyze a basic class of diffusion adaptive filters based on least mean
squares algorithms. Both stability and performance analyses will be carried out under a …

With the rise of the processing power of networked agents in the last decade, second-order
methods for machine learning have received increasing attention. To solve the distributed …

Distributed estimation over networks draws much attraction in recent years. In many
situations, due to imperfect information communication among nodes, the performance of …

This brief focuses on the development of a linear Kalman filtering algorithm when the control
input variable is corrupted by noises. The noisy input is considered in the derivation process …

Sensor networks are an indispensable part of the Internet of Things (IoT), where sensors
perform data acquisition and information processing tasks to obtain the parameters of …

In this paper, we consider the problem of distributed estimation over adaptive networks in
the presence of noisy input, output and communication links. First, a diffusion average …

In this letter, a novel diffusion Volterra (DV) algorithm is proposed for distributed in-network
system identification in the presence of α-stable noise. The proposed algorithm is based on …