Exploiting multi-spatial correlations of motion data in a body sensor network

Chun Hao Wu*, Yu-Chee Tseng

*Corresponding author for this work

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Human body motions usually exhibit a high degree of coherence and correlation in patterns. This allows exploiting spatial correlations of motion data being captured by a body sensor network. Since human bodies are relatively small, earlier work has shown how to compress motion data by allowing a node to overhear at most κ = 1 node's transmission and exploit the correlation with its own data for data compression. In this work, we consider multi-spatial correlations by extending κ = 1 to κ > 1 and constructing a partial-ordering directed acyclic graph (DAG) to represent the compression dependencies among sensor nodes. While a minimum-cost tree for κ = 1 can be found in polynomial time, we show that finding a minimum-cost DAG is NP-hard even for κ = 2. We then propose an efficient heuristic and verify its performance by real sensing data.

Original languageEnglish
Article number6171805
Pages (from-to)662-665
Number of pages4
JournalIEEE Communications Letters
Volume16
Issue number5
DOIs
StatePublished - 1 May 2012

Keywords

  • Body sensor network
  • Data compression
  • Inertial sensor
  • Pervasive computing
  • Wireless sensor network

Fingerprint Dive into the research topics of 'Exploiting multi-spatial correlations of motion data in a body sensor network'. Together they form a unique fingerprint.

  • Cite this