Unmanned aerial vehicle (UAV)-assisted networks ensure agile and flexible solutions based on the inherent attributes of mobility and altitude adaptation. These features render them suitable for emergency search and rescue operations. Emergency networks (ENs) differ from conventional networks. They often encounter nodes with vital information, i.e., critical nodes (CNs). The efficacy of search and rescue operations highly depends on the eminent coverage of critical nodes to retrieve crucial data. In a UAV-assisted EN, the information delivery from these critical nodes can be ensured through quality-of-service (QoS) guarantees, such as capacity and age of information (AoI). In this work, optimized UAV placement for critical nodes in emergency networks is studied. Two different optimization problems, namely capacity maximization and age of information minimization, are formulated based on the nature of node criticality. Capacity maximization provides general QoS enhancement for critical nodes, whereas AoI is focused on nodes carrying critical information. Simulations carried out in this paper aim to find the optimal placement for each problem based on a two-step approach. At first, the disaster region is partitioned based on CNs' aggregation. Reinforcement learning (RL) is then applied to observe optimal placement. Finally, network coverage over optimal UAV(s) placement is studied for two scenarios, i.e., network-centric and user-centric. In addition to providing coverage to critical nodes, the proposed scheme also ensures maximum coverage for all on-scene available devices (OSAs).
Keywords: age of information; capacity; coverage; critical nodes; emergency network; reinforcement learning; unmanned aerial vehicle.