Focusing on the task of 3D semantic occupancy prediction for autonomous driving, we address three key issues in existing 3D Gaussian splitting (3DGS) methods: (1) unified feature aggregation that neglects semantic correlations among similar categories and across regions, (2) boundary ambiguities caused by the lack of geometric constraints in MLP iterative optimization, and (3) bias issues in dynamic-static object coupling optimization. We propose the GraphGSOcc model, a novel framework that combines semantic and geometric graph transformers and decouples dynamic-static object optimization for 3D Gaussian splitting-based occupancy prediction. We propose a dual Gaussian graph attention approach, which dynamically constructs dual graph structures: a geometric graph that adaptively calculated KNN search radii based on Gaussian poses, enabling large-scale Gaussians to aggregate features from broader neighborhoods while compact Gaussians focus on local geometric consistency and a semantic graph that retains top-M highly correlated nodes via cosine similarity to explicitly encode semantic relationships within and across instances. Coupled with the multiscale graph attention framework, the fine-grained attention applied at lower layers optimizes boundary details, whereas the coarse-grained attention occurring at higher layers models the object-level topology. Additionally, we decouple dynamic and static objects by leveraging semantic probability distributions and design a dynamic-static decoupled Gaussian attention mechanism to optimize the prediction performance for both dynamic objects and static scenes. GraphGSOcc achieves state-of-the-art performance on the SurroundOcc-nuScenes, Occ3D-nuScenes, OpenOcc, and KITTI occupancy benchmarks. Experiments on the SurroundOcc dataset achieve an mIoU of 25.20%, reducing GPU memory to 6.0 GB, demonstrating a 1.97% mIoU improvement and a 13.7% memory reduction compared with GaussianWorld.

The fast-developing services in internet pose challenges to the efficient transmission of various types of data, so an effective packet scheduling scheme is needed to meet the Qos constraints of heterogeneous services. In this article, the priority-queuing model is used to study the performances of various strategies based on delay sensitivity and packets length. And the non-preemptive short-packet-first strategy is proved to result in the minimization of overall delay. With different strategies for delay-sensitive and non-delay-sensitive services, an optimal priority-queuing model for the scheduling of multiple internet services is proposed based on the above conclusions. The results of simulation experiments in NS-2 proved the superiority of the model. The results also demonstrated the features of packets queuing under different traffic scenarios and the law of variation for such performance indices as packet delivery ratio, throughput and average delay, which can be used to design effective measures for performance optimization.

The nodes near sink in wireless sensor networks are featured with heavy transmission loads, so these performance bottleneck nodes are prone to running out of energy early with shortened network lifetime. Routing tactic plays important role on transmission performance and energy consumption of network. In this paper, we enumerate typical source routing tactics in Grid topology, include 1) Random Equal Probability Forwarding, 2) Line Forwarding, 3) Source-Zigzag Forwarding, 4) Destination-Zigzag Forwarding, and 5) Balance Forwarding. We analysis the traffic load distribution among nodes for these routing tactics, and calculate the load of performance bottleneck nodes and transmission delay in one data collection cycle. Based on the energy consumption model, we further make comparison on the energy consumptions of transmission and standing-by for these tactics. Numerical results show that Balance Forwarding tactic leads to the least overall energy consumption on performance bottleneck nodes, and Line Forwarding and Random Equal-Probability Forwarding lead to high energy consumption.