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 …

This work proposes diffusion normalized least mean M-estimate algorithm based on the
modified Huber function, which can equip distributed networks with robust learning …

This work develops robust diffusion recursive least-squares algorithms to mitigate the
performance degradation often experienced in networks of agents in the presence of …

This brief proposes a robust distributed estimation algorithm in presence of impulsive noise.
Impulsive noises are present both in the measurements and in the communication links in a …

Adopting mean-square error (MSE) criterion, distributed estimation algorithms achieve
desirable performance if the background noise is drawn from the Gaussian distribution …

This brief paper proposes a robust generalized proportionate diffusion Least Mean Square
(LMS) algorithm for distributed estimation of a parameter vector in a network. The …

Time-series prediction is a popular application that relies on the collection of historical data
via sensors, which is then leveraged by predictive models to forecast future values or trends …

Employing the traditional least-mean-square (LMS) algorithm to estimate the weight vector
of an unknown system will result in an estimation bias when the input signal of the adaptive …

The popular distributed estimation algorithms based on the mean-square error criterion is
not robust against impulsive noise in the adaptive networks. To address the problem, the …

This paper proposes an adversary-resilient communication-efficient distributed estimation
algorithm for time-varying networks. It is a generalization of the doubly compressed diffusion …