Distributed Low-Rank Estimation Based on Joint Iterative Optimization in Wireless Sensor Networks
This addresses the challenge of efficient data processing and communication in wireless sensor networks, representing an incremental improvement over existing techniques.
The paper tackles the problem of distributed estimation in wireless sensor networks by proposing a novel distributed reduced-rank scheme and adaptive algorithm, achieving significantly reduced communication overhead and improved performance in terms of convergence rate and mean square error.
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.