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Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions

Gaddam, Anuroop, Wilkin, Timothy, Angelova Turkedjieva, Maia and Gaddam, Jyotheesh 2020, Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions, Electronics, vol. 9, no. 3, pp. 511-511, doi: 10.3390/electronics9030511.

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Title Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions
Author(s) Gaddam, AnuroopORCID iD for Gaddam, Anuroop orcid.org/0000-0001-5112-9849
Wilkin, TimothyORCID iD for Wilkin, Timothy orcid.org/0000-0003-4059-1354
Angelova Turkedjieva, MaiaORCID iD for Angelova Turkedjieva, Maia orcid.org/0000-0002-0931-0916
Gaddam, Jyotheesh
Journal name Electronics
Volume number 9
Issue number 3
Start page 511
End page 511
Total pages 15
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2020
ISSN 2079-9292
2079-9292
Keyword(s) sensor reliability
outlier detection
time series
Internet of Things
anomaly detection
multi-agent deep reinforcement learning
Language eng
DOI 10.3390/electronics9030511
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30135756

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.