# AoI Analysis of Satellite-UAV Synergy Real-Time Remote Sensing System
## 1 Introduction  
In recent years, as the space-air-ground integrated network (SAGIN) improves by leaps and bounds, the utilization of platforms such as low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) to provide environmental remote sensing and monitoring services is of significant importance [1]. Specifically, LEO satellites and UAVs are configured with sensing equipment that collects environmental status information such as images and videos. This environmental status information facilitates a variety of sensing and monitoring services for public users, such as surveillance and monitoring, navigation, road traffic analysis, and geographical photography. In scenarios such as disaster response, military operations, and rural areas, due to the non-deployment or destruction of terrestrial networks, it is necessary to employ satellites and UAVs to provide real-time services directly to ground users (GUs) [2]. Due to the different working altitudes, satellites collectlow-resolution environmental status information from large-scale areas, and UAVs collect high-resolution status information from small-scale areas, both sending their collected information to GUs [3]. Consequently, satellites and UAVs can adopt the \"multiscale explanation\" synergy strategy to sense an area. This is a strong synergy strategy that integrates the observation scales of each data source to enhance data interpretation [4; 5; 6]. In such a satellite-UAV synergy sensing system corresponding to a multi-correlated sources status update system, the GUs continuously receive status information from the two sources and then perform the fusion of the information. Furthermore, it is essential to ensure the freshness of the fused status information for GUs [7]. However, traditional performance metrics, such as delay and throughput, do not capture the freshness of status information effectively. Particularly, reducing the delay or increasing the throughput may fail to ensure that the status information at the destination remains as fresh as can be [8; 9]. In recent years, the age of information (AoI) has attracted increasing attention in the field of SAGIN research because of its effectiveness in measuring the freshness of information [10; 11; 12; 13; 14; 15; 16; 17]. The AoI is defined as the time elapsed since the latest status information was received by the devices at the destination. It inherently measures the freshness of the status information from the receiver's perspective [10; 18; 19; 20]. Therefore, an analysis of AoI for satellite-UAV \"multiscale explanation\" synergy sensing and monitoring scenario is necessary. The analysis of state-of-the-art in AoI studies is detailed below to offer a thorough clarification. The current studies of AoI focus on specific status update models. In the previous works, the researchers have made many significant contributions to the studies of AoI in a real-time status update system. The concept of AoI was first proposed in [18]. In Ref. [8], the authors derived the expression of average AoI in M/M/1, D/M/1, and M/D/1 single-source and single-server systems under the first-come-first-served (FCFS) queuing discipline. In Refs. [21; 22], the authors proposed the notion of _packet management_ and identified three types of single-source single-server lossy systems as M/M/1/1 queue, M/M/1/2 queue, and M/M/M/1/2* queue. They also proposed a novel metric termed _peak age_, which has the advantage of simpler mathematical formulation. In Refs. [23; 24; 25], the authors analyzed the AoI in single-source multi-server systems. In these systems, the updates from a single source are parallel served by multi-servers. The AoI in M/M/2 system and M/M/\(\infty\) system are analyzed in Refs. [23; 24], respectively. The authors of [25] analyzed the AoI in single-source M/M/c* last-come-first-served (_LCFS_) system by using the modified SHS approach [26].