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Leveraging Edge Computing for Video Data Streaming in UAV-Based Emergency Response Systems.

Mekhla Sarkar1, Prasan Kumar Sahoo1,2

  • 1Department of Computer Science and Information Engineering, Chang Gung University, Guishan, Taoyuan 33302, Taiwan.

Sensors (Basel, Switzerland)
|August 10, 2024
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Summary
This summary is machine-generated.

This study optimizes unmanned aerial vehicle (UAV) systems for emergency response by using deep reinforcement learning. The goal is to minimize video streaming latency for critical live data during emergencies.

Keywords:
bandwidth allocationedge computingresource managementunmanned aerial vehicle (UAV)video data stream

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Unmanned Aerial Vehicles (UAVs) are increasingly vital for wireless communication and edge computing.
  • Live video streaming for emergency response is critical but highly sensitive to latency.
  • Edge computing offers solutions to latency but UAV mobility adds complexity.

Purpose of the Study:

  • To develop an efficient system optimization strategy for collaborative UAVs in emergency response.
  • To address the challenges of video streaming latency, UAV/user mobility, and bandwidth limitations.
  • To enhance the effectiveness of emergency response systems through intelligent resource management.

Main Methods:

  • A collaborative multi-UAV system architecture for emergency response was designed.
  • Deep reinforcement learning was employed for adaptive resource management.
  • The system simultaneously optimized for video latency, mobility, and bandwidth.

Main Results:

  • The proposed adaptive resource management strategy effectively reduces video streaming latency.
  • The system demonstrates improved performance in dynamic emergency scenarios with mobile users and UAVs.
  • Efficient management of limited UAV resources was achieved.

Conclusions:

  • The study presents a novel approach to enhance emergency response systems using intelligent UAVs and edge computing.
  • Deep reinforcement learning provides a powerful tool for optimizing complex, dynamic communication systems.
  • The findings contribute to more reliable and timely information delivery during critical events.