With the rapid development of space–air–ground integrated networks (SAGIN), the synergy between the satellite and unmanned aerial vehicles (UAVs) in sensing environmental status information reveals substantial potential. In SAGIN, applications such as disaster response and military operations require fresh status information to respond effectively. The freshness of information, quantified by the age of information (AoI) metric, is crucial for an effective response. Therefore, it is urgent to investigate the AoI in real-time remote sensing systems leveraging satellite–UAV synergy. To this end, we first establish a comprehensive system model, corresponding to the satellite–UAV “multiscale explanation” synergy remote sensing system in SAGIN, in which we focus on the typical information transmission and fusion strategies of the system, the analysis framework of AoI, and the temporal evolution of AoI. Subsequently, the time-varying process of the system model is transformed into a corresponding finite-states continuous-time Markov chain, enabling a precise analysis of its stochastic behavior. By employing the stochastic hybrid system (SHS) approach, the moment generating functions (MGFs) and mean AoI, offering quantitative insights into the freshness of status information, are derived. Following this, a comparative analysis of AoI under different queuing disciplines, highlighting their respective performance characteristics, is conducted. Furthermore, considering transmit power and bandwidth constraints of the system, the AoI performances under full frequency reuse (FFR), and frequency division multiple access (FDMA) strategies are analyzed. The energy advantage and spectrum advantage associated with AoI are also examined to explore the superior AoI-related performance of the FFR strategy in SAGIN.

According to different mission scenarios, the UAV swarm needs to form specific topology shapes to achieve more robust system capability. The topology shaping, which will guide the UAVs autonomously to form the desired topology shape, is considered one of the most basic procedures in the UAV swarm field operations. The traditional optimization model of UAV swarm topology shaping proposed in most studies roughly represents the energy consumption by the squared Euclidean distances from initial positions to target positions of nodes. However, in practice, UAVs flying in different directions (vertical or horizontal) usually exhibits different energy consumption even though under the same moving distance. This paper proposes a more precise energy consumption model for UAV swarm topology shaping while taking the energy consumption for a UAV flying vertically upward, vertically downward, and horizontally into account. Simulation results show that the global energy consumption of the topology shaping modeled by the proposed energy consumption model is reduced by more than $38\%$ on average compared with that using the traditional energy consumption model. Furthermore, to further reduce the global energy consumption, a translation vector is introduced in the optimization model to obtain the optimal topology shaping position of the UAV swarm system. Newton’s method is employed to derive the translation vector which exhibits good convergence. Simulation results show that the global energy consumption of optimal topology shaping position is reduced by $9.8\%$ on average compared with that without translation.

Integrated Sensing and Communication (ISAC) has been enhanced to serve as a pivotal enabler for next-generation communication systems. In the context of target area detection, a UAV-SAR (Unmanned Aerial Vehicle–Synthetic Aperture Radar) based ISAC system, which shares both physical infrastructure and spectrum, can enhance the utilization of spectrum and hardware resources. However, existing studies on UAV-SAR-based ISAC systems for target imaging remain limited. In this study, we first established an ISAC mechanism to enable SAR imaging and communication. Then, we analyzed the energy consumption model, which includes both UAV propulsion and ISAC energy consumption. To maximize system energy efficiency, we propose an optimization method based on sequential convex optimization with linear state-space approximation. Furthermore, we propose a plan with general constraints, including the initial and final positions, the signal-to-noise ratio (SNR) constraint for SAR imaging, the data transmission rate constraint, and the total power limitation of the UAV. To achieve maximum energy efficiency, we jointly optimized the UAV’s trajectory, velocity, communication beamforming, sensing beamforming, and power allocation. Numerical results demonstrate that compared to existing benchmarks and PSO algorithms, the proposed method significantly improves the energy efficiency of UAV-SAR-based ISAC systems through optimized trajectory design.

Functional topology shaping is crucial for unmanned aerial vehicles (UAVs) swarm applications, such as remote sensing, precision agriculture, and emergency wireless communication. However, the current research on topology shaping is mostly based on the assumption that the target positions of the nodes are known or have been pre-defined. Moreover, the computational complexity of existing shaping methods is still high. In this paper, a topology shaping method based on a relative coordinate system is proposed to solve the problem of UAV swarm topology shaping with no external source of localization information. Based on the relative coordinates of nodes and target topology shape of the swarm, the topology shaping is transformed into a problem of optimal coordinate mapping from initial relative coordinates to target relative coordinates of nodes with minimized global energy consumption. The Jonker–Volgenant algorithm is employed to solve the optimization problem. As verified by simulations, the proposed method can achieve UAV swarm topology shaping with no external localization information. Furthermore, simulation results show that the proposed method has an average reduction in computation time of $94\%$ in the case of 1000 nodes compared with existing methods with the same level of global energy consumption.

Mobile edge computing (MEC) has become an effective framework for latency-sensitive and computation-intensive applications by deploying computing resources at network edge. The unmanned aerial vehicle (UAV)-assisted MEC leverages UAV mobility and communication advantages to enable services in dynamic environments, where frequent adjustments to flight trajectories and user association are required due to dynamic factors such as time-varying task requirements, user mobility, and communication environment variation. This paper addresses the joint optimization problem of UAV flight trajectory control and user association in dynamic environments, which explicitly incorporates the constraints governed by UAV flight dynamics. The joint problem is formulated as a non-convex optimization formulation that involves continuous–discrete hybrid decision variables. To overcome the inherent complexity of this problem, a novel proximal policy optimization-based dynamic control (PPO-DC) algorithm is developed. This algorithm aims to reduce the weighted combination of delay and energy consumption by dynamically controlling the UAV trajectory and user association. The numerical results validate that the PPO-DC algorithm successfully enables real-time UAV trajectory control under flight dynamics constraints, ensuring feasible and efficient flight trajectory. Compared to the state-of-the-art hybrid-action deep reinforcement learning (DRL) algorithms or metaheuristics, the PPO-DC achieves notable improvements in system performance by simultaneously lowering system delay and energy consumption.