Joint Collaboration and Compression Design for Distributed Sequential Estimation in a Wireless Sensor Network

Xiancheng Cheng, Prashant Khanduri, Baixiao Chen, Pramod Varshney

Research output: Contribution to journalArticlepeer-review

Abstract

In this work, we propose a joint collaboration-compression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a collaboration matrix) with each other. Then a subset of sensors selected to communicate with the FC linearly compress their observations before transmission. We design near-optimal collaboration and linear compression strategies under power constraints via alternating minimization of the sequential minimum mean square error. The objective function for collaboration design is generally non-convex. We establish correspondence between the sparse collaboration matrix and the non-sparse vector consisting of the nonzero elements of the collaboration matrix. Then, we reformulate and solve the collaboration design problem using quadratically constrained quadratic program (QCQP). The compression design problem is solved using the same methodology. We propose two versions of compression design, one centralized scheme where the compression strategies are derived at the FC and decentralized, where the local sensors compute their individual compression strategies independently. Importantly, we show that the proposed methods can also be used for estimating time-varying random vector parameters. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.

Original languageEnglish (US)
Pages (from-to)5448-5462
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume69
DOIs
StatePublished - 2021

Keywords

  • collaboration-compression framework
  • distributed sequential estimation
  • energy allocation
  • non-convex QCQP
  • semidefinite programming
  • Wireless sensor networks

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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