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Errors in Global Positioning System01:26

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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Air pollution forecasting based on wireless communications: review.

Muthna J Fadhil1,2, Sadik Kamel Gharghan3, Thamir R Saeed4

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Artificial intelligence (AI) and machine learning enhance air quality monitoring using sensors. A backpropagation neural network system is proposed to improve pollution detection accuracy and overcome sensor limitations.

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Area of Science:

  • Environmental Science
  • Computer Science
  • Sensor Technology

Background:

  • Artificial intelligence (AI) and artificial neural networks (ANNs) offer novel approaches to address climate change and air quality.
  • Environmental sensors provide valuable data on atmospheric pollutants, but face challenges like accuracy, drift, and slow response times.
  • Machine learning enables intelligent, context-aware systems for event prediction and condition monitoring.

Purpose of the Study:

  • To review and classify existing research on environment sensors for air pollution detection using various wireless protocols.
  • To compare studies based on sensors, hardware, algorithms, power consumption, and sensing accuracy.
  • To identify challenges and limitations of using drones for air pollution detection.

Main Methods:

  • Literature review and classification of published articles based on wireless protocols (Wi-Fi, Bluetooth, ZigBee, LoRa, GPS, 4G/5G).
  • Comparative analysis of performance metrics including sensors, hardware, algorithms, power usage, and accuracy.
  • Identification of challenges associated with drone-based air pollution monitoring.

Main Results:

  • The study categorizes research by wireless protocol and evaluates performance metrics of different air pollution detection systems.
  • Identified limitations in current sensor technologies and drone deployment for air quality monitoring.
  • Highlighted the need for advanced algorithms to improve accuracy and reliability.

Conclusions:

  • A backpropagation (BP) neural network system implemented at a base station is proposed to enhance air pollution detection.
  • This intelligent system aims to overcome sensor limitations by providing flexible tracking and prediction of pollutant levels.
  • Drones offer significant advantages for air pollution monitoring, complementing ground-based sensor networks.