Gossip algorithms are attractive for in-network processing in sensor networks because they
do not require any specialized routing, there is no bottleneck or single point of failure, and …

Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The
nodes interact with each other on a local level and diffuse information across the network to …

The paper studies distributed static parameter (vector) estimation in sensor networks with
nonlinear observation models and noisy intersensor communication. It introduces separably …

This paper considers gossip distributed estimation of a (static) distributed random field (aka,
large-scale unknown parameter vector) observed by sparsely interconnected sensors, each …

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

The alternating direction multipliers method (ADMM) has been recently proposed as a
practical and efficient algorithm for distributed computing. We discuss its applicability to the …

In this work and the supporting Parts II and III of this paper, also in the current issue, we
provide a rather detailed analysis of the stability and performance of asynchronous …

We study, by large deviations analysis, the asymptotic performance of Gaussian running
consensus distributed detection over random networks; in other words, we determine the …

This paper considers the problem of distributed adaptive linear parameter estimation in
multiagent inference networks. Local sensing model information is only partially available at …

In a leader-follower multi-agent system (MAS), the leader agents act as control inputs and
influence the states of the remaining follower agents. The rate at which the follower agents …