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Enabling Artificial Intelligent Virtual Sensors in an IoT Environment.

Georgios Stavropoulos1, John Violos2, Stylianos Tsanakas3

  • 1Department of Informatics and Telematics, Harokopio University of Athens, 17778 Tavros, Greece.

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|February 11, 2023
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Summary

This study introduces virtual sensors, using machine learning (ML) models to replace physical sensors in Internet of Things (IoT) applications. This approach reduces costs and improves performance for smart cities and homes.

Keywords:
Internet of ThingsIoT platformRaspberry Pimachine learningregressionsmart homesvirtual sensors

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Internet of Things (IoT)

Background:

  • Increasing demand for sensors in IoT and smart cities escalates infrastructure costs and maintenance.
  • High sensor density leads to performance degradation in device communication, monitoring, and orchestration.
  • Physical sensors require significant installation and upkeep, posing scalability challenges.

Purpose of the Study:

  • To propose virtual sensors, powered by machine learning (ML) models, as a cost-effective and efficient alternative to physical sensors.
  • To evaluate the accuracy and applicability of various ML models for sensor replacement in IoT environments.
  • To develop and test a lightweight IoT platform capable of running both physical and virtual sensors.

Main Methods:

  • Comparison of fourteen distinct machine learning models to identify optimal candidates for virtual sensor implementation.
  • Design and deployment of virtual sensors (ML models) and physical sensors within a smart home environment.
  • Development of a custom, lightweight IoT platform on a Raspberry Pi to host and manage sensor operations.

Main Results:

  • Extensive simulations demonstrated the feasibility and accuracy of selected ML models as virtual sensors.
  • The custom IoT platform successfully integrated and executed both physical and virtual sensor functionalities.
  • Evaluation in a smart home scenario confirmed the promising performance of the virtual sensor methodology.

Conclusions:

  • Virtual sensors offer a viable solution to mitigate the challenges associated with the proliferation of physical sensors in IoT.
  • Machine learning models can effectively replace physical sensors, reducing costs and enhancing system performance.
  • The proposed lightweight IoT platform provides a practical foundation for deploying virtual sensor technology in smart environments.