Distributed Multiscale Data Analysis and Processing for Sensor Networks

Abstract

While multiresolution data analysis, processing, and compression hold considerable promise for sensor network applications, progress has been confounded by two factors. First, typical sensor data are irregularly spaced, which is incompatible with standard wavelet techniques. Second, the communication overhead of multiscale algorithms can become prohibitive. In this paper, we take a first step in addressing both shortcomings by introducing two new distributed multiresolution transforms. Our irregularly sampled Haar wavelet pyramid and telescoping Haar orthonormal wavelet basis provide efficient piecewise-constant approximations of sensor data. We illustrate with examples from distributed data compression and in-network wavelet de-noising.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA487722

Entities

People

  • Hyeokho Choi
  • Raymond Wagner
  • Richard G. Baraniuk
  • Shriram Sarvotham

Organizations

  • Rice University

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Compression
  • Computational Complexity
  • Data Analysis
  • Data Compression
  • Detectors
  • Equations
  • Hierarchies
  • Mathematics
  • Measurement
  • Networks
  • Sensor Networks
  • Signal Processing
  • Standards
  • Two Dimensional
  • Wavelet Transforms
  • Wireless Sensor Networks

Readers

  • Computer Networking
  • Image Processing and Computer Vision.

Technology Areas

  • Space
  • Space - Space Objects