Markov Model Inferencing in Distributed Systems

Chen Lu, Jason M. Schwier, Richard R. Brooks, Christopher Griffin, Satish Bukkapatnam

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Hidden Markov Models (HMMs) are a common tool in pattern recognition. Applications of HMMs include voice recognition, texture recognition, handwriting recognition, gait recognition, tracking, and human behavior recognition. Variations of these applications can also be used in distributed sensor networks. The chapter introduces the methods of inferring HMMs from data streams. It provides background on HMMs and explains several HMM inference algorithms. In the applications, HMMs are inferred from data streams in sensor network using different approaches. K. L. Shalizi’s approach finds statistically significant groupings of the training data that correspond to HMM states. The drawback of Baum-Welch algorithm is that it requires a priori knowledge of the structure of the HMM. Shalizi developed the causal state splitting and reconstruction algorithm to infer HMMs without this prior information. HMMs have been used in sensor networks as well for a wide range of applications, that is, target action recognition, target tracking, sensor localization, and side channel analysis.

Original languageEnglish (US)
Title of host publicationDistributed Sensor Networks
Subtitle of host publicationSecond Edition: Image and Sensor Signal Processing
PublisherCRC Press
Pages569-580
Number of pages12
ISBN (Electronic)9781439862834
ISBN (Print)9781439862827
DOIs
StatePublished - Jan 1 2016

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Engineering

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