A Survey on Age of Information in UAV-Enabled Networks: Theory, Optimization, and Applications

The age of information (AoI) has emerged as a performance metric that quantifies the freshness of information delivered over communication networks. Unlike conventional metrics such as delay, packet loss, and throughput, AoI measures the time elapsed since the generation of the most recently received update, which makes it well-suited for real-time applications in which decisions must rely on the latest information. This paper presents a structured survey of AoI research with a particular focus on unmanned aerial vehicle (UAV)-enabled networks. The survey is conducted as a structured narrative review, in which peer-reviewed studies published from 2011 to 2026 are selected according to explicit inclusion and exclusion criteria and organized along the three levels of theory, optimization methodology, and application. We first introduce the basic concept of AoI together with nonlinear and generalized penalty functions and review queueing theory and the Markov decision process (MDP) as the analytical foundations of AoI analysis. We then classify AoI optimization approaches into queueing theory-based and learning-based methods, compare the two classes in terms of their assumptions, optimality guarantees, scalability, and computational cost, and identify from the reviewed studies the conditions under which each is preferable. Building on these foundations, we review AoI optimization in energy harvesting and vehicle-to-everything (V2X) systems as two representative application domains and then examine UAV-enabled networks in depth, organizing the studies into trajectory and scheduling optimization, multi-UAV cooperation, and wireless power transfer, and discussing when each category is the appropriate design choice. UAV-enabled networks combine the energy constraints of energy harvesting systems with the time-critical requirements of V2X communications while adding a spatial optimization dimension, which makes AoI a central design metric for UAV-aided data collection and aerial sensing. Finally, we discuss open challenges and future research directions driven by emerging technologies such as sixth-generation (6G) mobile networks, integrated sensing and communication, and artificial intelligence.