High altitude platforms (HAPs) are emerging as the critical enabler for the next generation communication system, due to their broad coverage, long endurance and high payload. However, the open and broadcast nature of wireless links makes HAP system susceptible to information eavesdropping. This paper investigates the secure downlink transmission for an HAP network, where one unmanned aerial vehicle (UAV) jammer is utilized against multiple mobile eavesdroppers (Eves) with imperfect location information. We aim to maximize the minimum average secrecy rate (MASR) by jointly optimizing subcarrier scheduling, subcarrier power allocation, UAV trajectory and HAP deployment. To address the formulated mixed-integer non-convex optimization problem, we first derive the optimal HAP deployment and utilize an estimation error model to resolve the locational uncertainty of the Eves. Subsequently, a low-complexity iterative algorithm is proposed, which decomposes the problem into three manageable subproblems and iteratively solves them to obtain a near-optimal solution. The convergence and complexity of the algorithm are further analyzed. Numerical results demonstrate that the proposed scheme significantly improves not only the MASR but also the fairness between terminal devices, which outperforms the baselines without resource allocation and/or trajectory optimization.

Marine Internet of Things devices (MIoTDs) are inherently constrained by limited computation resources, while conventional mobile edge computing implementation faces significant challenges in offshore environments due to unstable shore-based communications and widespread device distribution. To this end, this paper proposes an integrated offshore-aerial edge computing and caching architecture where multiple unmanned aerial vehicles (UAVs), capable of dynamically adjusting their three-dimensional positions for optimal service coverage, serve as edge servers collaborating with a shore-based base station to provide computing and caching services for MIoTDs. The objective is to minimize the total system latency by jointly optimizing task offloading ratios, user association decisions, and service caching decisions, subject to MIoTD energy, UAV energy, and cache capacity constraints. To solve the resulting mixed-integer nonlinear programming problem, we develop an efficient iterative algorithm that decomposes it into tractable subproblems, strategically employing coalition game, linear relaxation, and Lagrangian dual methods to address their distinct mathematical structures. Extensive numerical results demonstrate that the proposed scheme reduces the total latency by 15.18%, 8.90%, and 8.38% compared to local-only computing, random caching, and fixed offloading baselines, respectively, while ensuring fair service provision across MIoTDs.

This letter proposes a secure edge computing scheme for multi-layer aerial-offshore networks where source unmanned aerial vehicles (SUAVs) with limited onboard resources offload computation-intensive tasks to either a high-altitude platform or a shore-based station. To safeguard wireless transmissions against multiple surface eavesdroppers with uncertain locations, a mobile jammer UAV (JUAV) dynamically emits artificial noise. The JUAV trajectory, power allocation, and offloading decisions are jointly optimized to minimize the total system latency subject to secrecy rate, mobility, and power constraints. An iterative algorithm combining successive convex approximation and the cross-entropy method is developed to solve the resulting mixed-integer nonlinear programming problem. Simulation results demonstrate that the proposed scheme reduces latency by 16.6%-28% compared to baselines while incurring only an 11% performance penalty for security protection.

With the rapid advancement of Internet of Things applications, the demand for integrated sensing, communication, computing, and caching (ISC3) functions has surged. However, existing systems optimize these functions independently, leading to suboptimal resource utilization and performance bottlenecks. In this article, we propose a multilayer aerial ISC3 architecture where a versatile unmanned aerial vehicle (UAV) provides edge computing and caching services to ground wireless devices (WDs) alongside its radar sensing capabilities. A high-altitude platform maintains the complete service library, delivering required services to the UAV when cache misses occur. Partial data compression is employed to reduce uplink communication overhead, where WDs partially compress their offloaded task data before transmitting to the UAV. The objective is to minimize total system energy consumption by jointly optimizing time scheduling ratios, task offloading ratios, compression selection ratios, service caching decisions, and UAV trajectory, subject to task latency, sensing quality, energy budgets, and cache capacity constraints. An efficient iterative algorithm utilizing specialized optimization techniques, such as Lagrangian duality and successive convex approximation is developed to solve the resulting mixed-integer nonlinear programming problem. Extensive simulations demonstrate fast convergence under diverse network configurations, with the proposed scheme consistently outperforming all baselines by 22.5%-67.0% in total energy consumption.

To fulfill computing demands of numerous Internet of Things (IoT) devices in infrastructure-free regions, low earth orbit (LEO) satellite edge computing has been proposed in recent years, to circumvent the latency arising from long backhaul and link congestion in traditional cloud computing mode. This article proposes a novel time-varying graph-based collaborative task offloading strategy for LEO satellite IoT to reduce task computing latency. To this end, a computing coordinate graph (CCG) is designed to characterize the time-varying topology and resource distribution of LEO satellite networks. When a task is offloaded to LEO satellite networks because local computing capability is unable to meet latency constraint, the position of the task access satellite in the CCG is determined first. Then, the expanded hop counts from all satellite nodes to the access satellite are calculated, which informs the partitioning of different node sets. Afterwards, considering both link and on-board computing resources, with the access satellite as the reference node, the minimum total task computing latency for each node set is obtained in an ascending order of the expanded hop counts. Finally, the minimum one among obtained latency values is the anticipated total task computing latency. Simulation results demonstrate the effectiveness of the proposed task offloading strategy in reducing task computing latency.