Accurately predicting spatio-temporal network traffic is essential for dynamically managing computing resources in modern communication systems and minimizing energy consumption. Although spatio-temporal traffic prediction has received extensive research attention, further improvements in prediction accuracy and computational efficiency remain necessary. In particular, existing decomposition-based methods or hybrid architectures often incur heavy overhead when capturing local and global feature correlations, necessitating novel approaches that optimize accuracy and complexity. In this paper, we propose an efficient spatio-temporal network traffic prediction framework, DP-LET, which consists of a data processing module, a local feature enhancement module, and a Transformer-based prediction module. The data processing module is designed for high-efficiency denoising of network data and spatial decoupling. In contrast, the local feature enhancement module leverages multiple Temporal Convolutional Networks (TCNs) to capture fine-grained local features. Meanwhile, the prediction module utilizes a Transformer encoder to model long-term dependencies and assess feature relevance. A case study on real-world cellular traffic prediction demonstrates the practicality of DP-LET, which maintains low computational complexity while achieving state-of-the-art performance, significantly reducing MSE by 31.8% and MAE by 23.1% compared to baseline models.

For future Internet of Vehicles (IoV), communications and computing will converge to provide services. Federated learning (FL), as one of the typical distributed computing technologies, needs to be integrated with IoV. For such integration, FL suffers from the straggler effect that the entire learning speed is lowered down, because of the existence of the devices, such as low-powered road side units and vehicles, taking more time to complete their tasks. Although the existing mechanisms reduce straggler effects by adopting asynchronous mechanisms and clustering mechanisms, they lack the detailed analysis of the reasons and the impacts of each cause, leading to inefficiencies in the design of algorithm. Additionally, most of the existing work only considered the impact of a single factor in computation, communication, or data distribution, which lacks comprehensive on research for causes of stragglers effects. The bottleneck is that it is laborious to observe the time delay precisely with the existing high-calculating evaluations. In this article, we elaborately explore the effects of computing power, communication capability, and data distributions on the straggler effects with carefully designing and conducting the extensive experiments. After investigations, we propose a novel learning completion time estimation formula for low computing capability devices with mini-batch stochastic gradient decent (SGD). We compare our proposed estimation formula with the one based on floating operation per second (FLOPs). Through the evaluations, our formula can demonstrate the improvement up to 72.4% at docker and 32.4% at Raspberry Pi device compared to the existing work.

In recent years, with the widespread of the Internet and further big data, E-Commerce (EC) has emerged as a popular medium for users to engage in online transactions of products and services. Generally, Service Providers (SPs) of EC collect users' personal information and utilize advanced big data technologies to enhance their services. However, the price discrimination problem may also arise based on personalized information, where malicious SPs analyze users' historical orders to provide the same products or services at varying prices depending on their characteristics. In this paper, we propose a price auditing system called E-Commerce Price Auditing System (ECPAS) to resolve this problem. ECPAS consists of four smart contracts: User Registration Contract, Product Registration Contract, Insurance Purchasing Contract, and Price Auditing Contract, which realize EC price auditing and financial compensation for price discrimination based on a private blockchain. Meanwhile, ECPAS utilizes InterPlanetary File System (IPFS) to efficiently store product data. Experimental results demonstrate that ECPAS achieves a higher processing speed of 5 million price auditing per day while maintaining low gas and on-chain storage costs based on the IPFS.

The metaverse is at the forefront of the next-generation internet application, where billions of users seamlessly immerse themselves in a hybrid reality of physical-virtual worlds and switch between virtual environments thanks to reliable resource allocation and synchronization. However, the exponential growth of users and computationally intensive applications make joint optimization of multiple indicators challenging. Therefore, predicting user behavior is pivotal in assisting the optimization process. Although graph neural networks have demonstrated remarkable performance in traffic prediction, most existing schemes link nodes based on their distances and require significant computational resources, limiting their generalization and deployment in the metaverse. To solve this problem, we propose an efficient Spatio-temporal Identity Multi-graph convolutional network Framework (SIMF) for application-level traffic prediction in the metaverse. In the SIMF, we design a spatio-temporal embedding layer and multi-graph convolutional module to jointly capture spatio-temporal correlations among nodes (avatars) and reduce the dependence on topology information, which is more consistent with the real relationship between avatars in the metaverse. We conduct extensive experiments to evaluate the SIMF, which show that our proposed framework achieves superior accuracy even without graph information while maintaining low time complexity, making it suitable for traffic prediction in the metaverse.

With the rapid development of the Internet of Things and increasingly rigid communication requirements, the wireless traffic prediction framework is experiencing a transition from edge/cloud server deployment to edge–cloud collaborative deployment. However, it remains a significant challenge to balance prediction accuracy and overall complexity based on edge–cloud collaboration networks. In this article, we propose a multiple seasonal-trend decomposition using loess-based global–local traffic prediction (MSTL-GLTP) framework that assures prediction accuracy while maintaining low complexity. Specifically, we first decompose the cellular traffic into the multiseasonal, trend, and residual components through the MSTL algorithm. Subsequently, multiseasonal components are clustered and fed into the bidirectional long short-term memory (Bi-LSTM) model to capture global tendency. Meanwhile, we exploit a distance-assisted attention mechanism to minimize global loss. Besides, a local network module consisting of the temporal convolutional network (TCN) and Gaussian process regression (GPR) model is deployed in the edge devices to learn the dynamic regional and local traffic. The experimental results demonstrate that MSTL-GLTP outperforms the state-of-the-art baselines by capturing global–local spatiotemporal correlation and achieves accuracy and complexity equilibrium when predicting wireless traffic.