On Coverage of Critical Nodes in UAV-Assisted Emergency Networks

Area of Science:

• Wireless Communication Networks
• Network Optimization
• Emergency Response Systems

Background:

• Unmanned Aerial Vehicles (UAVs) offer flexible mobility and altitude adaptation for emergency networks (ENs).
• Critical nodes (CNs) within ENs contain vital information essential for search and rescue (SAR) operations.
• Quality-of-service (QoS) metrics like capacity and age of information (AoI) are crucial for effective data retrieval from CNs.

Purpose of the Study:

• To investigate optimized UAV placement strategies for critical nodes in emergency networks.
• To formulate and address two distinct optimization problems: capacity maximization and AoI minimization.
• To ensure effective data delivery and comprehensive network coverage in disaster scenarios.

Main Methods:

• The study partitions disaster regions based on CN aggregation.
• Reinforcement learning (RL) is employed to determine optimal UAV placement.
• Network coverage is analyzed for both network-centric and user-centric scenarios.

Main Results:

• Optimal UAV placement strategies were identified for both capacity maximization and AoI minimization.
• The proposed method ensures enhanced QoS for critical nodes.
• Maximum coverage was achieved for all on-scene available devices (OSAs) alongside critical node coverage.

Conclusions:

• Optimized UAV placement significantly improves the efficiency of emergency networks.
• The dual objectives of capacity maximization and AoI minimization can be effectively addressed through RL-based placement.
• The proposed scheme provides robust network coverage, crucial for successful search and rescue missions.