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Air pollution

Power-Efficient IoT Mechanism for Real-Time Urban Air Quality Monitoring

Strategic Insights

— 1 min read

Researchers from Rajalakshmi Engineering College have developed a power-efficient internet of things (IoT) mechanism that utilizes an adaptive recurrent temporal optimized convolutional learning (ARTOCL) scheme for real-time urban air quality monitoring. The system integrates energy-aware IoT nodes and an IoT gateway to create a peer-to-peer network, enabling efficient data collection and analysis. The proposed solution has shown significant improvements in error metrics and accuracy, with a reduction in mean absolute error (MAE), root mean square error (RMSE), and normalized root mean square error (nRMSE) to 0.3, 0.2, and 0.060, respectively.

Key Takeaways:

  • The proposed system incorporates ARTOCL, a combination of long short-term memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and convolutional neural networks (CNN), to achieve energy savings and improved accuracy.
  • The IoT sensors collect PM2.5 data from different regions and transmit it to an IoT server for real-time tracking, control, and logging.
  • The system's energy consumption analysis confirms its robustness and impressive power-saving efficiency.
  • The accuracy metrics improved with the R² value for PM2.5 rising to 0.7 when using the ARTOCL algorithm.
  • The system enables real-time visualization of PM2.5 data through an Android app.
  • A continuous air quality data collection experiment was conducted in an outdoor area to validate the system's functionality.
  • The researchers from Rajalakshmi Engineering College reported that the proposed system has shown significant improvements in error metrics and accuracy compared to existing solutions.

Statistics:

  • Mean absolute error (MAE) reduction: 0.3
  • Root mean square error (RMSE) reduction: 0.2
  • Normalized root mean square error (nRMSE) reduction: 0.060
  • Accuracy metric R² value for PM2.5: 0.7
  • Energy consumption analysis confirms robustness and impressive power-saving efficiency

Sources:

  • A power-efficient IoT mechanism with adaptive recurrent temporal optimized convolutional learning (ARTOCL) scheme for real-time urban air quality monitoring. Discover Internet of Things, 2025, 5(1):1-21. The publisher for Discover Internet of Things is Springer.
  • https://doi-org.sdpl.idm.oclc.org/10.1007/s43926-025-00173-x (free version of the journal article)
  • Internet Weekly News. October 20, 2025; p 2.

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