TinyAD: Memory-Efficient Anomaly Detection for Time-Series Data in Industrial IoT
Author:
Affiliation:
1. School of Information Technology and Electrical Engineering, University of Queensland, Saint Lucia, Australia
2. Institute for Integrated and Intelligent Systems, Griffith University, Southport, Australia
Funder
Australian Research Council
Discovery Early Career Researcher Award
Discovery Project
UQ New Staff Research Start-up
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/9424/10355676/10093068.pdf?arnumber=10093068
Reference31 articles.
1. A Comprehensive Study of Anomaly Detection Schemes in IoT Networks Using Machine Learning Algorithms
2. IoT-Based Solutions to Monitor Water Level, Leakage, and Motor Control for Smart Water Tanks
3. IoT-integrated Smart Grid Using PLC and NodeMCU
4. DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series
5. Long short term memory networks for anomaly detection in time series;Malhotra,2015
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