Low-altitude cellular-enabled Unmanned Aerial Vehicles (UAVs) provide potential supplementary platforms to assist air-to-ground cooperative communication. This paper investigates a joint safety information interaction scheme for a UAV-enabled network, which involves the complex constraints of three-dimensional trajectory planning, average energy efficiency optimization, and physical-layer security. Specifically, by modeling the UAV and the Ground Station (GS) as the transmit sources, we define the secure Energy Efficiency (EE) as the ratio of the total secrecy rate to the energy consumption of the whole system. Then, to achieve secure and energy-efficient communication in eavesdropping scenarios, we formulated the optimization problem as maximizing both the uplink/downlink secure EE of the system, subject to the constraints of the UAV’s mobility and the allowable transmit power. For this highly coupled non-convex problem, a composite solution of joint fractional programming, alternate optimization, the bisection method, and the interior-point method is proposed to obtain the achievable EE. Simulation and performance analysis gave the conclusions that the joint optimization of trajectory and power allocation is capable of maximizing the information secure EE. Additionally, the secure EE increases with the increase of the average transmit power, which finally tends to be stable.

Unmanned aerial vehicle (UAV) swarms offer unique advantages for area search and environmental monitoring applications. For practical deployments, determining the optimal number of UAVs required for a given task and defining key performance metrics for the platforms and payloads are crucial challenges. This study aims to address mission planning and performance optimization for cooperative UAV swarm search scenarios. A new clustering algorithm is proposed, integrating enhanced clustering techniques with ant colony optimization, particle swarm optimization, and crow search optimization. This jointly optimizes and validates the UAV numbers and coordinated trajectories. Sensitivity analysis and indicator optimization further examine specific scenarios to quantify platform and sensor factors influencing search efficiency. Lastly, sensitivity analysis and performance indicator optimization are conducted in specific scenarios. The modular algorithmic components and modeling techniques established in this work lay a foundation for continued research into real−world mission−based swarm optimization.

The near-space communication relay platform has a broad application prospect and development potential in the field of over-the-horizon communication support. In this paper, we investigate a throughput oriented, real-time path planning method for the high-altitude Solar-Powered UAV (SP-UAV) to ensure high quality communication of long-range mission UAVs. Firstly, based on the spatial distribution, the Channel State Information (CSI) and the potential field theory, we design the channel-aware-aided relay SP-UAV which is capable of adaptively changing its position, while maximizing the total throughput of the communication system. Secondly, in the feedback loop design, a cooperative game theory model is introduced to formulate a feedback-aided strategy for adjusting the weighting factors, which can further improve the whole communication performance. Specifically, to ensure the real-time performance, a low complexity algorithm and the real-time updated system conditions are applied in calculating the planning trajectory of the relay UAV. Simulation results show that compared with the relay UAV with a fixed cruise trajectory, our proposed scheme achieves better network throughput and has good tracking adaptability.