Data collection with in-network fault detection based on spatial correlation

Lei Fang; Simon Andrew Dobson

doi:10.1109/ICCAC.2014.9

Data collection with in-network fault detection based on spatial correlation

Lei Fang, Simon Andrew Dobson

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Environmental sensing exposes sensor nodes to environmental stresses that can lead to various kinds of sampling failure. Recognising such faults in the network can improve data reliability therefore making sensor networks suitable candidate
for critical monitoring applications. We develop a technique that builds a spatial model of a sensor network and its observations, and show how this can be updated in-network to provide outlier detection even for non-stationary time series. The solution does not require local storage of learning data or any centralised control.
The method is evaluated by both real world implementation and simulation, and the results are promising.

Original language	English
Title of host publication	2014 International Conference on Cloud and Autonomic Computing (ICCAC)
Pages	56-65
Number of pages	10
DOIs	https://doi.org/10.1109/ICCAC.2014.9
Publication status	Published - 8 Sept 2014

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Fault detection
Sensor networks
Online learning
Energy efficiency

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10.1109/ICCAC.2014.9

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title = "Data collection with in-network fault detection based on spatial correlation",

abstract = "Environmental sensing exposes sensor nodes to environmental stresses that can lead to various kinds of sampling failure. Recognising such faults in the network can improve data reliability therefore making sensor networks suitable candidate for critical monitoring applications. We develop a technique that builds a spatial model of a sensor network and its observations, and show how this can be updated in-network to provide outlier detection even for non-stationary time series. The solution does not require local storage of learning data or any centralised control. The method is evaluated by both real world implementation and simulation, and the results are promising.",

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AU - Dobson, Simon Andrew

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AB - Environmental sensing exposes sensor nodes to environmental stresses that can lead to various kinds of sampling failure. Recognising such faults in the network can improve data reliability therefore making sensor networks suitable candidate for critical monitoring applications. We develop a technique that builds a spatial model of a sensor network and its observations, and show how this can be updated in-network to provide outlier detection even for non-stationary time series. The solution does not require local storage of learning data or any centralised control. The method is evaluated by both real world implementation and simulation, and the results are promising.

KW - Fault detection

KW - Sensor networks

KW - Online learning

KW - Energy efficiency

UR - http://cac2014.cis.fiu.edu/

UR - https://www.scopus.com/pages/publications/84923961454

U2 - 10.1109/ICCAC.2014.9

DO - 10.1109/ICCAC.2014.9

M3 - Conference contribution

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EP - 65

BT - 2014 International Conference on Cloud and Autonomic Computing (ICCAC)

ER -

Data collection with in-network fault detection based on spatial correlation

Abstract

UN SDGs

Keywords

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