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Area of Science:

  • Wireless Sensor Networks
  • Robotics
  • Artificial Intelligence

Background:

  • Unmanned aerial vehicles (UAVs) can support ground users (GUs) by relaying sensing information and computational tasks to remote base stations (RBS).
  • Optimizing UAV trajectory, scheduling, and access control is crucial for energy-efficient data collection and transmission in terrestrial wireless sensor networks.
  • The dynamic nature of GU distribution and traffic demands presents challenges for UAV-assisted networks.

Purpose of the Study:

  • To enhance energy efficiency in sensing-data collection and transmission within UAV-assisted wireless sensor networks.
  • To investigate the trade-offs between UAV access control and trajectory planning within a time-slotted frame structure.
  • To develop an efficient learning framework for dynamic network environments.

Main Methods:

  • A multi-agent deep reinforcement learning approach was employed to optimize UAV trajectory, scheduling, and access-control strategies.
  • A hierarchical learning framework was devised to reduce action and state spaces, improving learning efficiency.
  • The study considered a time-slotted frame structure with confined flight, sensing, and information-forwarding sub-slots.

Main Results:

  • UAV trajectory planning combined with access control significantly improves UAV energy efficiency.
  • The hierarchical learning method demonstrates enhanced learning stability and superior sensing performance.
  • Simulation results validate the effectiveness of the proposed optimization strategies in dynamic network conditions.

Conclusions:

  • Optimized UAV trajectory and access control are key to improving energy efficiency in wireless sensor networks.
  • Hierarchical multi-agent deep reinforcement learning offers a stable and effective solution for UAV-assisted sensing networks.
  • The proposed framework effectively addresses the challenges of dynamic environments and uncertain user demands.