Acoustic target tracking using tiny wireless sensor devices

Qixin Wang, Wei Peng Chen, Rong Zheng, Kihwal Lee, Lui Sha

Research output: Contribution to journalArticle

Abstract

With the advancement of MEMS technologies, wireless networks consist of tiny sensor devices hold the promise of revolutionizing sensing in a wide range of application domains because of their flexibility, low cost and ease of deployment. However, the constrained computation power, battery power, storage capacity and communication bandwidth of the tiny devices pose challenging problems in the design and deployment of such systems. Target localization using acoustic signal with tiny wireless devices is a particularly difficult task due to the amount of signal processing and computation involved. In this paper, we provide an in-depth study of designing such wireless sensor networks for real-world acoustic tracking applications. We layout a cluster-based architecture to address the limitations of the tiny sensing devices. To achieve effective utilization of the scarce wireless bandwidth, a quality-driven paradigm to suppress redundant information and resolve contention is proposed. One instance of the quality-driven approach is implemented in the acoustic tracking system, where the quality of the tracking reports can be quantified numerically. We demonstrate the effectiveness of our proposed architecture and protocols using a sensor network testbed based on UCBerkeley mica motes. Considering the performance limitations of tiny sensor devices, the achieved acoustic target tracking accuracy is extraordinarily good. Our experimental study also shows that the acoustic target tracking quality can be indeed measured and used to assist resource allocation decisions. This application-driven design and implementation exercises also serve to identify important areas of further work in in-network processing and communications.

Original languageEnglish (US)
Pages (from-to)642-657
Number of pages16
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2634
StatePublished - Dec 1 2003

Keywords

  • Acoustic target tracking
  • Quality driven
  • Redundancy suppression
  • Sink tree routing
  • Wireless sensor network

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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