Regression in sensor networks: training distributively with alternating projections

Joel B. Predd; Sanjeev R. Kulkarni; H. Vincent Poor

doi:10.1117/12.620194

16 September 2005 Regression in sensor networks: training distributively with alternating projections

Joel B. Predd, Sanjeev R. Kulkarni, H. Vincent Poor

Proceedings Volume 5910, Advanced Signal Processing Algorithms, Architectures, and Implementations XV; 591006 (2005) https://doi.org/10.1117/12.620194
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

Abstract

Wireless sensor networks (WSNs) have attracted considerable attention in recent years and motivate a host of new challenges for distributed signal processing. The problem of distributed or decentralized estimation has often been considered in the context of parametric models. However, the success of parametric methods is limited by the appropriateness of the strong statistical assumptions made by the models. In this paper, a more flexible nonparametric model for distributed regression is considered that is applicable in a variety of WSN applications including field estimation. Here, starting with the standard regularized kernel least-squares estimator, a message-passing algorithm for distributed estimation in WSNs is derived. The algorithm can be viewed as an instantiation of the successive orthogonal projection (SOP) algorithm. Various practical aspects of the algorithm are discussed and several numerical simulations validate the potential of the approach.

Citation Download Citation

Joel B. Predd, Sanjeev R. Kulkarni, and H. Vincent Poor "Regression in sensor networks: training distributively with alternating projections", Proc. SPIE 5910, Advanced Signal Processing Algorithms, Architectures, and Implementations XV, 591006 (16 September 2005); https://doi.org/10.1117/12.620194

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