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Powering UAV with Deep Q-Network for Air Quality Tracking.

Alaelddin F Y Mohammed1, Salman Md Sultan2, Seokheon Cho3

  • 1School of Computing, Gachon University, Seongnam 13120, Korea.

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|August 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a Deep Reinforcement Learning (Deep RL) approach using Unmanned Aerial Vehicles (UAVs) for rapid air pollution plume detection. The developed system significantly reduces the time needed to identify unhealthy areas during emergencies.

Keywords:
Air Quality IndexDeep Q-networkIoTUAVunhealthy polluted area

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Area of Science:

  • Environmental Science
  • Computer Science
  • Robotics

Background:

  • Real-time air quality monitoring is vital for emergency response and evacuation planning.
  • Fixed Internet of Things (IoT) sensors have limitations in covering vast areas during emergencies.
  • Unmanned Aerial Vehicles (UAVs) offer a potential solution for wider air quality monitoring but face navigation challenges.

Purpose of the Study:

  • To develop an efficient UAV-based system for rapid detection of air pollution plumes.
  • To reduce the time required for identifying unhealthy air quality zones in large areas.
  • To enhance decision-making for emergency evacuation plans through timely pollution source tracking.

Main Methods:

  • Utilized Deep Reinforcement Learning (Deep RL), specifically a Deep Q-network (DQN), for UAV navigation.
  • Integrated Long Short-Term Memory (LSTM) with the Q-network to optimize navigation patterns for minimal time consumption.
  • Developed a UAV Pollution Tracking (DUPT) system to guide UAVs in locating pollution plumes within a grid space.

Main Results:

  • The proposed DUPT system demonstrated rapid identification of unhealthy polluted areas.
  • The DUPT solution achieved time efficiency, taking approximately 28% less time compared to existing methods.
  • Validated the system's performance using simulated air pollution environments based on Gaussian distribution and kriging interpolation.

Conclusions:

  • The Deep RL-based DUPT system effectively guides UAVs for swift air pollution plume detection.
  • This approach significantly improves the speed and efficiency of air quality monitoring during emergency events.
  • The findings support the use of intelligent UAV systems for enhanced environmental monitoring and disaster response.