Cited by 5Distributed systems and fault toleranceFault Detection and Control SystemsIoT and Edge/Fog Computing

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Fault-Tolerant IoT System Using Software-Based “Digital Twin”

Open access
Baranwal, Tanish, Varada, Srihari, Das, Santanu, Haider, Mohammad R.·2024·2024 IEEE 10th World Forum on Internet of Things (WF-IoT)

Abstract

In this article, we present a novel redundancy scheme to realize a fault-tolerant IoT structure for application in high-reliability systems. The proposed fault-tolerant structure uses a centralized data fusion block and triplicated IoT devices, along with software-based “digital twins”, that duplicate the function of each of the sensors. In case of a fault in one of the IoT devices, the pertinent digital twin takes over the function of the actual IoT device for some time in the triplicated structure till the faulty device is either replaced or repaired when possible. The use of software-based digital twins as a duplicate for each physical sensor improves the reliability of the operation with minimal increase in the overall system cost.

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Crossref · 10.1109/wf-iot62078.2024.10811168

Cites

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Citations

Cites

Show all 11 references

Research works printed but not linked: 13

  1. Internet of Things (IoT)
  2. Internet of Things (IoT): What it is and why it matters
  3. Kullaa, Detection, identification, and quantification of sensor fault, PROCEEDINGS OF ISMA2010 INCLUDING USD2010
  4. Mohapatra, 2024, Fault-Tolerant Approaches for IoT Integrated Smart City, Academia Engineering Special Issues
  5. 2022, What is a digital twin?
  6. 2024, Triple modular redundancy, Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia
  7. Baranwal, 2023, Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach
  8. Regunath, 2021, 10 Incredibly Useful Time Series Forecasting Algorithms, Advancing Analytics
  9. 2024, Time Series Analysis using Facebook Prophet
  10. Arik, 2021, Interpretable Deep Learning for Time Series Forecasting, Google Research
  11. Gupta, 2022, Time Series Forecasting using Kalman Filter
  12. Agrawal, 2020, Intel Berkeley Research Lab Sensor Data
  13. Lim, 2020, Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

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Metadata

Authors
Baranwal, T., Varada, S., Das, S., & Haider, M. R.
Affiliations
  • DomaniSystems, Inc,Shelton,CT,06484
  • University of Missouri (US)
Venue
2024 IEEE 10th World Forum on Internet of Things (WF-IoT)
Publisher
IEEE
Published
2024-11-10
Pages
828-833
OpenAlex
W4405909035
Cited by
5
References
11
Citations by year
2026: 3 · 2025: 2
FWCI
2.18
Type
conference paper
Language
en
Open access
Locations
  • IEEE World Forum on Internet of Things (WF-IoT) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Keywords
fault tolerance, Internet of Things, digital twin, redundancy, sensor redundancy, high-reliability systems

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