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RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning.

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Summary

This study presents an Internet of Things (IoT) system for museum preservation, using radio frequency energy harvesting to power sensors and deep learning for optimized environmental control and security.

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Internet of Things (IoT)RF energy harvestingambience monitoringantenna arraydeep learningrectennatime series prediction

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Area of Science:

  • Cultural Heritage Preservation
  • Internet of Things (IoT)
  • Energy Harvesting

Background:

  • Museum artifacts are susceptible to environmental degradation and vandalism.
  • Existing monitoring systems often lack autonomous control and efficient power solutions.
  • Preserving cultural heritage is a critical global responsibility.

Purpose of the Study:

  • To develop an IoT-based system for autonomous museum monitoring and control.
  • To enable remote management of environmental conditions and security.
  • To address power limitations of sensors in remote monitoring applications.

Main Methods:

  • Implementation of an Internet of Things (IoT) system with always-on sensors.
  • Utilization of Radio Frequency (RF) energy harvesting via rectenna arrays for sensor power.
  • Integration of deep learning algorithms for trend analysis and system optimization.

Main Results:

  • The system autonomously adjusts museum ambience for artifact preservation.
  • Alarms are triggered for detected vandalism attempts.
  • Remote control of environmental parameters is enabled via internet-connected devices.
  • RF energy harvesting significantly extends sensor node operational lifetime.

Conclusions:

  • The developed IoT system offers a robust and sustainable solution for museum preservation.
  • The integration of RF energy harvesting and deep learning enhances monitoring precision and system resilience.
  • This approach provides a novel method for powering energy-intensive sensors in IoT applications.