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Opportunistic Weather Sensing by Smart City Wireless Communication Networks.

Sensors (Basel, Switzerland)·2025
Same author

Data formats and standards for opportunistic rainfall sensors.

Open research Europe·2024
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In-City Rain Mapping from Commercial Microwave Links-Challenges and Opportunities.

Roy Janco1, Jonatan Ostrometzky1, Hagit Messer1

  • 1School of Electrical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel.

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Summary

Accurate urban rainfall mapping is possible using existing smart-city wireless networks. A data-driven approach slightly outperforms an empirical model, especially for light rain, enabling high-resolution 2D rainfall maps.

Keywords:
ISACRNNenvironmental monitoringopportunistic ISACopportunistic sensingsmart city

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Area of Science:

  • Environmental Science
  • Electrical Engineering
  • Computer Science

Background:

  • Accurate rainfall measurement is critical for urban planning and management.
  • Existing wireless networks offer potential for opportunistic rainfall sensing.
  • Integrated Sensing and Communication (ISAC) leverages communication infrastructure for sensing.

Purpose of the Study:

  • To compare a model-based and a data-driven approach for rainfall estimation using smart-city wireless network data.
  • To evaluate the performance of these methods in classifying wet/dry periods and estimating rainfall intensity.
  • To generate and validate high-resolution 2D rainfall maps using opportunistic sensing.

Main Methods:

  • Utilized Received Signal Level (RSL) measurements from an existing smart-city wireless network in Rehovot, Israel.
  • Implemented a model-based method with empirical parameter calibration and wet/dry classification.
  • Developed and trained a data-driven recurrent neural network (RNN) for rainfall estimation and classification.

Main Results:

  • The data-driven RNN approach showed slightly superior performance compared to the empirical model, particularly for light rainfall events.
  • Both methods successfully generated high-resolution 2D ground-level rainfall maps of the urban area.
  • Generated rainfall maps demonstrated good agreement with weather radar data from the Israeli Meteorological Service (IMS).

Conclusions:

  • Existing smart-city wireless networks can be effectively utilized for opportunistic rainfall sensing.
  • Data-driven methods, like RNNs, offer enhanced accuracy for urban rainfall estimation.
  • Opportunistic sensing networks show significant potential for creating detailed, high-resolution 2D rainfall maps.