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Related Experiment Video

Updated: Aug 19, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Data Collection in IoT Using UAV Based on Multi-Objective Spotted Hyena Optimizer.

Hamza Mohammed Ridha Al-Khafaji1

  • 1Biomedical Engineering Department, Al-Mustaqbal University College, Hillah 51001, Babil, Iraq.

Sensors (Basel, Switzerland)
|November 26, 2022
PubMed
Summary
This summary is machine-generated.

This study optimizes energy consumption for Internet of Things (IoT) data collection using unmanned aerial vehicles (UAVs). The multi-objective spotted hyena optimizer (MOSHO) effectively determines optimal drone numbers and locations for efficient data gathering.

Keywords:
internet of thingsmulti-objective optimizationspotted hyena optimizerunmanned aerial vehicle

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Robotics

Background:

  • Information and communication technology is crucial for global Internet of Things (IoT) deployment.
  • Unmanned aerial vehicles (UAVs) are increasingly used for data collection from distributed IoT devices worldwide.

Purpose of the Study:

  • To minimize energy consumption in IoT data collection systems by optimizing the number and locations of UAVs.
  • To evaluate the effectiveness of a bio-inspired multi-objective optimization algorithm (MOA) for this task.

Main Methods:

  • Implementation of the multi-objective spotted hyena optimizer (MOSHO), a bio-inspired MOA, for UAV-based IoT data collection.
  • Comparative analysis of MOSHO against other evolutionary MOAs in a geographical area data collection scenario.

Main Results:

  • MOSHO demonstrated significant impact on optimizing energy consumption for the IoT data collection system.
  • The algorithm exhibited high convergence rates, improved diversity, and enhanced robustness compared to other methods.
  • Results indicate MOSHO's effectiveness in determining optimal parameters for energy-efficient UAV deployment.

Conclusions:

  • The MOSHO algorithm is a promising approach for optimizing energy efficiency in UAV-based IoT data collection.
  • This research provides valuable insights for identifying optimal parameters in such systems.
  • The findings contribute to more sustainable and robust IoT data collection strategies.