  This paper proposes a novel distributed reduced--rank scheme and an adaptive
algorithm for distributed estimation in wireless sensor networks. The proposed
distributed scheme is based on a transformation that performs dimensionality
reduction at each agent of the network followed by a reduced-dimension
parameter vector. A distributed reduced-rank joint iterative estimation
algorithm is developed, which has the ability to achieve significantly reduced
communication overhead and improved performance when compared with existing
techniques. Simulation results illustrate the advantages of the proposed
strategy in terms of convergence rate and mean square error performance.
