Fault-tolerant algorithms for detecting event regions in wireless sensor networks using statistical hypothesis test

Donglei Cao, Beihong Jin, Jiannong Cao

Research output: Journal article publicationConference articleAcademic researchpeer-review

Abstract

Detecting event regions in a monitored environment is a canonical task of wireless sensor networks (WSNs). It is a hard problem because sensor nodes are prone to failures and have scarce energy. In this paper, we seek distributed and localized algorithms for fault-tolerant event region detection. Most existing algorithms only assume that events are spatially correlated, but we argue that events are usually both spatially and temporally correlated. By examining the temporal correlation of sensor measurements, we propose two detection algorithms by applying statistical hypothesis test (SHT). Our analyses show that SHT-based algorithm is more accurate in detecting event regions. Moreover, it is more energy efficient since it gets rid of frequent measurement exchanges. In order to improve the capability of fault recognition, we extend SHT-based algorithm by examining both spatial and temporal correlations of sensor measurements, and our analyses show that extended SHT-based algorithm can recognize almost all faults when sensor network is densely deployed.
Original languageEnglish
Article number4724374
Pages (from-to)631-638
Number of pages8
JournalProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
DOIs
Publication statusPublished - 1 Dec 2008
Event2008 14th IEEE International Conference on Parallel and Distributed Systems, ICPADS'08 - Melbourne, VIC, Australia
Duration: 8 Dec 200810 Dec 2008

Keywords

  • Event region detection
  • Fault tolerance
  • Statistical hypothesis test
  • Temporal correlation examining
  • Wireless sensor network

ASJC Scopus subject areas

  • Hardware and Architecture

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