Resource allocation in unmanned aerial vehicle networks: A review

Currently, resource allocation in Unmanned Aerial Vehicles (UAVs) is a major topic of discussion among industrialists and researchers. Considering the different emerging applications of UAVs, if the resource allocation problem is not addressed effectively, the upcoming UAV applications will not serve their proposed purpose. Although there are numerous and diverse research works addressing the resource allocation in UAVs, there is an evident lack of a comprehensive survey describing and analyzing the existing methods. Addressing this research gap, we present an extensive review of the resource allocation in UAVs. In this work, we classify the existing research works based on four criteria - optimization goal-based classification, mathematical model-based classification, game theory framework-based classification, and machine learning model-based classification. Our findings revealed that the mathematical models are relatively more explored to solve the resource allocation problem in UAVs. Researchers have explored a variety of game theory techniques, like the Stackelberg model, mean-field game theory, cooperative games, etc., for optimized resource allocation in UAVs. The optimization of energy and throughput factors is more seen in the literature compared to the other optimization goals. We also observed that the reinforcement learning technique is a heavily exploited technique for resource allocation in UAVs compared to all other machine learning-based methods. We have also presented several challenges and future works in the field of resource allocation in UAVs.