Adaptive networks rely on in-network and collaborative processing among distributed
agents to deliver enhanced performance in estimation and inference tasks. Information is …

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 …

Demand side energy consumption scheduling is a well-known issue in the smart grid
research area. However, there is lack of a comprehensive method to manage the demand …

We study the performance of diffusion least-mean squares algorithms for distributed
parameter estimation in multi-agent networks when nodes exchange information over …

In this paper, we thoroughly investigate the tracking behavior of a wide range of adaptive
networks under non-stationary conditions. Under these conditions, we study and analyze the …

Partial diffusion‐based recursive least squares (PDRLS) is an effective way of lowering
computational load and power consumption in adaptive network implementation. In this …

The performance of a partial diffusion Kalman filtering (PDKF) algorithm for the networks
with noisy links is studied here. A closed-form expression for the steady-state mean square …

We present diffusion algorithms for distributed estimation and detection over networks that
endow all nodes with both spatial cooperation abilities and temporal processing abilities …

We study the effect of fading in the communication channels between sensor nodes on the
performance of incremental least mean square (ILMS) algorithm, and derive steady state …

The article studies the steady‐state performance of a diffusion least‐mean squares (LMS)
adaptive network with imperfect communications where the topology is random (links may …