Unmanned Aerial Vehicles (UAVs) are widely used in Internet of Things (IoT) scenarios, but factors like circuit malfunctions and network issues can lead to data loss, disconnections, and service interruptions, causing UAVs to go offline and fail to meet user needs. This problem can be effectively solved using UAV resource migration techniques. However, traditional methods lead to resource redundancy and cannot ensure effective migration decisions or the continuity of services. Therefore, we proposed a new UAV framework with a high-availability migration strategy to improve UAV swarm resource utilization. By combining Software Defined Network and Mobile Edge Computing technologies, we designed a UAV collaboration architecture model and introduced large language model agents in the edge layer for better task execution. Additionally, we created a two-tier collaborative UAV high-availability mechanism for handling failures in edge scenarios during UAV collaborative tasks. Our experiments on the container cloud platform showed a 42% reduction in failure recovery time and higher overall resource utilization compared to baseline algorithms. While our focus is on UAVs, our findings can potentially be applied to broader IoT and mobile edge computing scenarios such as traffic management systems and healthcare IoT systems.

Masked autoencoders (MAEs) have emerged as a powerful self-supervised approach for point cloud analysis. Nevertheless, existing methods often separately focus on global structures or multi-scale features, ignoring their complementary potential. In this paper, we propose a novel dual-Siamese masked autoencoder (DS-MAE) framework that explores integrating global and hierarchical feature learning in a unified architecture for point cloud analysis. In particular, we introduce a consistent dual-branch patch embedding strategy to partition the point cloud into patches using shared group centers, ensuring both global and hierarchical branches process point patches centered at the same spatial locations. Each branch employs dual-branch Siamese encoders to process original and augmented point patches, learning representations that capture both local details and global context. In addition, we have designed cross-attention Siamese decoders to reconstruct masked point patches and align features both within and between branches with crossattention mechanisms. Comprehensive experiments demonstrate our method consistently achieves superior results to prior methods. Code is available at https://github.com/shaoandy1211/DS-MAE.git.

The diversified development of the service ecosystem, particularly the rapid growth of services like cloud and edge computing, has propelled the flourishing expansion of the service trading market. However, in the absence of appropriate pricing guidance, service providers often devise pricing strategies solely based on their own interests, potentially hindering the maximization of overall market profits. This challenge is even more severe in edge computing scenarios, as different edge service providers are dispersed across various regions and influenced by multiple factors, making it challenging to establish a unified pricing model. This paper introduces a multi-participant stochastic game model to formalize the pricing problem of multiple edge services. Subsequently, an incentive mechanism based on Pareto improvement is proposed to drive the game towards Pareto optimal direction, achieving optimal profits. Finally, an enhanced PSO algorithm was proposed by adaptively optimizing inertia factor across three stages. This optimization significantly improved the efficiency of solving the game model and analyzed equilibrium states under various evolutionary mechanisms. Experimental results demonstrate that the proposed pricing incentive mechanism promotes more effective and rational pricing allocations, while also demonstrating the effectiveness of our algorithm in resolving game problems.