Optimization of Secure Offloading Data for Space–Air–Ground Integrated Networks Oriented to Mobile-Edge Computing

In the Mobile Edge Computing (MEC)-oriented Space-Air-Ground Integrated Network (SAGIN), High-Altitude Platforms (HAPs) and Unmanned Aerial Vehicles (UAVs) exhibit superior line-of-sight communication probability and enhanced mobility. Compared to terrestrial eavesdroppers (Eves), these aerial platforms operate under more advantageous conditions for eavesdropping, thereby increasing the susceptibility of user-offloaded data to interception. To effectively address this issue, we formulate an optimization framework for secure data offloading in SAGIN, particularly addressing the threat posed by aerial Eves. Within this architecture, Internet of Things (IoT) devices offload data to UAV-assisted aerial networks and Low Earth Orbit (LEO) satellite networks. To enhance system security, HAP and UAV can be equipped with sensing and ranging modules to locate the approximate direction and position of Eve. Meanwhile, HAP sends Artificial Noise (AN) signal to confuse potential Eve. By applying optimization techniques such as Block Coordinate Descent (BCD) and Successive Convex Approximation (SCA), the original non-convex problem is decoupled into four sub-problems, and a Joint Optimization of Transmit Power from IoT Devices to UAV, AN Power, and UAV Trajectory (JOPAQ) algorithm is proposed, which holistically optimizes IoT device transmit power, HAP interference power, UAV flight trajectory, and sensing offload variables under energy consumption and flight constraints. The simulation results demonstrate the effectiveness of the JOPAQ algorithm, significantly enhancing the confidentiality and data security of the SAGIN-MEC framework.