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dc.contributor.authorFang, Lei
dc.contributor.authorDobson, Simon Andrew
dc.identifier.citationFang , L & Dobson , S A 2014 , Data collection with in-network fault detection based on spatial correlation . in 2014 International Conference on Cloud and Autonomic Computing (ICCAC) . pp. 56-65 .
dc.identifier.otherPURE: 155034940
dc.identifier.otherPURE UUID: 898b3f1f-5c57-49da-b0c0-2227cc9bd3fe
dc.identifier.otherWOS: 000370731000009
dc.identifier.otherORCID: /0000-0001-9633-2103/work/70234174
dc.identifier.otherScopus: 84923961454
dc.description.abstractEnvironmental 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.
dc.relation.ispartof2014 International Conference on Cloud and Autonomic Computing (ICCAC)en
dc.rights© © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectFault detectionen
dc.subjectSensor networksen
dc.subjectOnline learningen
dc.subjectEnergy efficiencyen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectSDG 7 - Affordable and Clean Energyen
dc.titleData collection with in-network fault detection based on spatial correlationen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen

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