Energy-Based Resource Allocation in UAV-Assisted Mobile Networks for Public Safety

Abstract

This paper presents an optimized communication scheduling and prioritization approach for unmanned aerial vehicle (UAV)-assisted disaster networks. The solution leverages a non-preemptive queuing model, unlike the First-Come-First-Served (FCFS) approach, to manage limited resources, such as battery power and bandwidth, based on the energy levels and proximity of available UAVs. The energy-based resource allocation strategy further enhances efficiency by prioritizing ground node (GN) with lower remaining battery levels, maintaining communication links for longer during the disaster response. Additionally, the system employs AI-based algorithms to dynamically select the most suitable master node (MN) from the available MN, further optimizing the communication network. Simulation results demonstrate the effectiveness of the proposed approach in terms of message delivery latency, resource utilization, and overall system performance.