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Summary
This summary is machine-generated.

This study introduces an optimized multi-Unmanned Aerial Vehicle (UAV) strategy for efficient data collection in Internet of Things (IoT) networks. The approach balances task volume and reduces completion time, improving overall system performance.

Keywords:
UAVdata collectionequalize data volumeroute planningtime minimization

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Area of Science:

  • Robotics and Automation
  • Computer Science
  • Network Engineering

Background:

  • Escalating demands for enhanced communication quality and data collection in evolving network technologies.
  • The need for efficient data gathering from numerous terminals in Internet of Things (IoT) environments.

Purpose of the Study:

  • To develop an advanced multi-Unmanned Aerial Vehicle (UAV) data collection and path planning approach for extensive terminal accessibility.
  • To optimize the balance between task completion time and task volume for multiple UAVs.
  • To minimize overall task completion duration in IoT data collection scenarios.

Main Methods:

  • A novel k-means algorithm for balanced sensor node partitioning based on data volume.
  • Discrete modeling of UAV flight trajectory paths.
  • A grouped, improved genetic algorithm with a 2-opt optimization operator to solve the Multiple Traveling Salesman Problem (MTSP).

Main Results:

  • Significant reduction in task completion times for multi-UAV data collection.
  • Effective balancing of diverse task volumes among multiple UAVs.
  • Demonstrated superior convergence rate and computational efficiency compared to conventional genetic algorithms.

Conclusions:

  • The proposed approach offers an innovative and efficient paradigm for IoT data collection using multiple UAVs.
  • The strategy enhances efficiency and balance in task distribution.
  • Tailored algorithm solutions can achieve optimal outcomes in complex engineering scenarios.