Multitask and Multiobjective Joint Resource Optimization for UAV-Assisted Air-Ground Integrated Networks Under Emergency Scenarios

Abstract

To face the challenges in emergency scenarios, a multi-task and multi-objective optimization algorithm for computation offloading and relay communication is investigated for the air-ground integrated networks, composed of unmanned aerial vehicles (UAVs), emergency vehicle users (EVUs) and ground sensor nodes (GSNs). We propose an HFL-DDQN algorithm, which combines horizontal federated learning (HFL) with double deep Q-network (DDQN). First, UAVs are separated into two clusters according to the services they provide, i.e., edge computing or relay communication. Next, the optimization problems are formulated for two types of services respectively. For the computation offloading tasks of EVUs, the optimization objective is to minimize the weighted sum of delay and energy consumption. For the sensor data transmission of GSNs, the optimization objective is to maximize the minimum rate of relay links. We define the total cost of the system as the sum of two types of services. Then, federated aggregation is used to joint training the global neural networks model without sharing raw data. Furthermore, the DDQN is improved by adopting prioritized experience replay to achieve better convergence. The simulation results show that the proposed HFL-DDQN algorithm not only outperforms the state-of-art baselines in terms of the system cost, but also promotes the generalization in execution process, which is especially applicable to the rescue scene under accidents.