  This paper presents a novel distributed low-rank scheme and adaptive
algorithms for distributed estimation over wireless networks. The proposed
distributed scheme is based on a transformation that performs dimensionality
reduction at each agent of the network followed by transmission of a reduced
set of parameters to other agents and reduced-dimension parameter estimation.
Distributed low-rank joint iterative estimation algorithms based on alternating
optimization strategies are developed, which can achieve significantly reduced
communication overhead and improved performance when compared with existing
techniques. A computational complexity analysis of the proposed and existing
low-rank algorithms is presented along with an analysis of the convergence of
the proposed techniques. Simulations illustrate the performance of the proposed
strategies in applications of wireless sensor networks and smart grids.
