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Trajectory Planning for Data Collection of Energy-Constrained Heterogeneous UAVs.

Zhen Qin1, Chao Dong2, Hai Wang1

  • 1College of Communications Engineering, Army Engineering University of PLA, Nanjing 210042, China.

Sensors (Basel, Switzerland)
|November 14, 2019
PubMed
Summary

This study introduces an optimized Unmanned Aerial Vehicle (UAV) trajectory planning algorithm for efficient data collection in Internet-of-Things (IoT) networks. The novel approach maximizes data utility from heterogeneous UAVs with varying energy constraints.

Keywords:
data collection utilitysensorstrajectory planningunmanned aerial vehicles

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Robotics

Background:

  • Unmanned Aerial Vehicles (UAVs) are increasingly integrated into Internet-of-Things (IoT) networks for sensor data collection.
  • Limited UAV battery capacity and sensor transmission range necessitate efficient data collection strategies.
  • Heterogeneous UAVs with diverse energy constraints present unique challenges in optimizing data collection utility.

Purpose of the Study:

  • To maximize data collection utility in a UAV-enabled IoT network with multiple heterogeneous UAVs.
  • To jointly optimize communication scheduling and UAV trajectories considering data value and energy constraints.
  • To address the NP-hard nature of the sensor data collection problem.

Main Methods:

  • Transforming the problem into a submodular function maximization under energy constraints.
  • Designing a novel trajectory planning algorithm for heterogeneous UAVs.
  • Evaluating performance through extensive simulations under various network settings.

Main Results:

  • The proposed trajectory planning algorithm effectively maximizes data collection utility.
  • The algorithm accounts for varying data values and heterogeneous UAV energy constraints.
  • Simulations demonstrate superior performance compared to existing approaches.

Conclusions:

  • The developed algorithm provides an efficient solution for UAV-enabled data collection in IoT.
  • Optimized trajectories enhance data collection utility and prolong network lifetime.
  • This research contributes to the advancement of intelligent UAV deployment in IoT ecosystems.