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Summary

This study optimizes energy harvesting unmanned aerial vehicle (UAV) flight paths to maximize service rewards while minimizing costs and user wait times. A novel foraging-based algorithm achieves peak deployment profitability and reduces operational and service times.

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foraging theorytrajectory designunmanned aerial vehicleswireless communications

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Area of Science:

  • Robotics and Automation
  • Wireless Communications
  • Operations Research

Background:

  • Unmanned aerial vehicles (UAVs) are increasingly used as mobile base stations for ground users.
  • Energy harvesting UAVs require optimized trajectories to balance service provision, energy management, and maintenance.
  • Maximizing deployment profitability necessitates considering rewards from user service and energy collection against operational costs.

Purpose of the Study:

  • To formulate and solve the trajectory design problem for energy harvesting UAVs.
  • To maximize the deployment profitability of UAV operations by balancing multiple objectives.
  • To minimize average user service time alongside profitability.

Main Methods:

  • Modeling service completion and harvested energy as rewards, and energy consumption as cost.
  • Defining deployment profitability as the ratio of reward to cost.
  • Developing a foraging-based algorithm to optimize UAV trajectory.
  • Analyzing the algorithm's time complexity and performance.

Main Results:

  • The proposed foraging-based algorithm achieves maximal deployment profitability.
  • The algorithm demonstrates lower time complexity compared to traditional methods.
  • Simulation results show reduced operation and average user service times compared to Q-learning.

Conclusions:

  • The foraging-based algorithm provides an effective solution for optimizing energy harvesting UAV trajectories.
  • This approach enhances UAV operational efficiency and user satisfaction.
  • The study offers a valuable framework for profitable UAV deployment in wireless communication scenarios.