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

Power, Alexander; Kotonya, Gerald

doi:10.1145/3365871.3365872

Cited by 19 publications

(13 citation statements)

References 33 publications

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“…Atlidakis et al [50] introduced security rules to capture desirable properties of REST APIs and services, and showed how a stateful REST API fuzzer can be extended with active property checkers that automatically test and detect violations of these rules in cloud systems. Complex Patterns of Failure (CPoF) [51] is an approach that provides reactive and proactive fault tolerance through complex event processing and machine learning for IoT. The approach uses error events to train ML models to prevent and recover from errors in the future.…”

Section: Related Workmentioning

confidence: 99%

Run-Time Failure Detection Via Non-Intrusive Event Analysis in a Large-Scale Cloud Computing Platform

Cotroneo

Simone

Liguori³

et al. 2022

SSRN Journal

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Section: Related Workmentioning

confidence: 99%

Run-Time Failure Detection Via Non-Intrusive Event Analysis in a Large-Scale Cloud Computing Platform

Cotroneo

Simone

Liguori³

et al. 2022

SSRN Journal

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“…Many CEP and ML-based solutions have been applied in the real world. A CEP and ML-based approach to support fault-tolerance of IoT systems is proposed in work [30]. In [19], a framework based on CEP and deep learning is implemented, and an unattended bag computer vision task is illustrated to evaluate its feasibility.…”

Section: Related Workmentioning

confidence: 99%

The synergy of complex event processing and tiny machine learning in industrial IoT

Ren

Anicic

Runkler

2021

Proceedings of the 15th ACM International Conference on Distributed and Event-Based Systems

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Focusing on comprehensive networking, the Industrial Internetof-Things (IIoT) facilitates efficiency and robustness in factory operations. Various intelligent sensors play a central role, as they generate a vast amount of real-time data that can provide insights into manufacturing. Complex event processing (CEP) and machine learning (ML) have been developed actively in the last years in IIoT to identify patterns in heterogeneous data streams and fuse raw data into tangible facts. In a traditional compute-centric paradigm, the raw field data are continuously sent to the cloud and processed centrally. As IIoT devices become increasingly pervasive, concerns are raised since transmitting such an amount of data is energy-intensive, vulnerable to be intercepted, and subjected to high latency. Decentralized on-device ML and CEP provide a solution where data is processed primarily on edge devices. Thus communications can be minimized. However, this is no mean feat because most IIoT edge devices are resource-constrained with low power consumption. This paper proposes a framework that exploits ML and CEP's synergy at the edge in distributed sensor networks. By leveraging tiny ML and µCEP, we now shift the computation from the cloud to the resource-constrained IIoT devices and allow users to adapt on-device ML models and CEP reasoning rules flexibly on the fly. Lastly, we demonstrate the proposed solution and show its effectiveness and feasibility using an industrial use case of machine safety monitoring. CCS CONCEPTS• Computing methodologies → Machine learning approaches; • Computer systems organization → Real-time systems; Sensor networks; • Information systems → Data streaming.

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“…Combining IoT with data analytics becomes effective in real time applications like healthcare, telematics, smart cities and the like. [8]. We have a large number of real time applications available in the market which are fully dependent on the data for its functioning.…”

Section: 1role Of Data and Its Quality In Iot Applicationsmentioning

confidence: 99%

Validity as a Measure of Data Quality in Internet of Things Systems

Pandey¹,

Snigdh²

2021

Preprint

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Data quality became significant with the emergence of data warehouse systems. While accuracy is intrinsic data quality, validity of data presents a wider perspective, which is more representational and contextual in nature. Through our article we present a different perspective in data collection and collation. We focus on faults experienced in data sets and present validity as a function of allied parameters such as completeness, usability, availability and timeliness for determining the data quality. We also analyze the applicability of these metrics and apply modifications to make it conform to IoT applications. Another major focus of this article is to verify these metrics on aggregated data set instead of separate data values. This work focuses on using the different validation parameters for determining the quality of data generated in a pervasive environment. Analysis approach presented is simple and can be employed to test the validity of collected data, isolate faults in the data set and also measure the suitability of data before applying algorithms for analysis.

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

Cited by 19 publications

References 33 publications

Run-Time Failure Detection Via Non-Intrusive Event Analysis in a Large-Scale Cloud Computing Platform

Run-Time Failure Detection Via Non-Intrusive Event Analysis in a Large-Scale Cloud Computing Platform

The synergy of complex event processing and tiny machine learning in industrial IoT

Validity as a Measure of Data Quality in Internet of Things Systems

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