Providing Fault Tolerance via Complex Event Processing and Machine Learning for IoT Systems

Power, Alexander and Kotonya, Gerald (2019) Providing Fault Tolerance via Complex Event Processing and Machine Learning for IoT Systems. In: Proceedings of the 9th International Conference on the Internet of Things, IoT 2019. ACM, New York. ISBN 9781450372077

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Abstract

Fault-tolerance (FT) support is a key challenge for ensuring dependable Internet of Things (IoT) systems. Many existing FT-support mechanisms in IoT are static, tightly coupled, inflexible implementations that struggle to adapt in dynamic IoT environments. This paper proposes Complex Patterns of Failure (CPoF), an approach to providing reactive and proactive FT using Complex Event Processing (CEP) and Machine Learning (ML). Error-detection strategies are defined as nondeterministic finite automata (NFA) and implemented via CEP systems. Reactive-FT support is monitored and learned from to train ML models that proactively handle imminent future occurrences of known errors. We evaluated CPoF on an indoor agriculture system with experiments that used time and error correlations to preempt battery-depletion failures. We trained predictive models to learn from reactive-FT support and provide preemptive error recovery.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 9th International Conference on the Internet of Things, IoT 2019 http://doi.acm.org/10.1145/3365871.3365872
ID Code:
138815
Deposited By:
Deposited On:
07 Feb 2020 10:15
Refereed?:
Yes
Published?:
Published
Last Modified:
19 Sep 2020 07:17