This paper investigates the outage performance of simultaneous wireless information and power transfer (SWIPT)-enabled relay networks with the decode-and-forward relaying protocol, where the effect of the energy triggering threshold at the relay on the system performance is considered. The closed-form expressions of the system outage probability and throughput are derived in Rician channel fading. Monte Carlo Simulation method is used to verify the accuracy of the derived closed-form expressions. The effects of some system parameters on the system performances are discussed via simulations, which show that the system outage probability increases with the increase of the minimum transmission rate required by the users and also decreases with the increase of the energy conversion efficiency. Besides, the system throughput increaseswith the increment of the transmit power of the source node, as well as the energy conversion efficiency. Additionally, the outage performance of the system with the equal two-hop distance is better than that of the system with unequal two-hop distance.

Due to the necessity to realize “building a strong transportation nation”, the construction of intermodal transportation is based on the information resource integration of diverse transport systems. To ensure the data security during the interaction between different transport modes, as well as the effect of data application, the status of entities and data flow in the network should be supervised throughout. Therefore, an evaluation framework of comprehensive trust is proposed in this paper. With feature analysis of transportation big data, a quality assessment is conducted by three-dimensional metric sets, which is considered as a significant factor of trust measurement. Furthermore, a hierarchical trust structure is put forward to assess the trust of entities in different levels, in terms of the static and dynamic evidence. Furthermore, the visualization of a dynamic global information security state is discussed, based on temporal knowledge graphs. As shown in practical application and simulation analysis, this framework can meet the requirements of data security supervision and lay the foundation of further intelligent management. This research is of great significance to improve the data security level in intermodal transport, and to promote the utilization and sharing of public information resources.

With the widespread deployment of industrial systems and Internet of Things (IoT) devices, multivariate time series anomaly detection has become increasingly important for ensuring system reliability and operational safety. However, accurately detecting anomalies in complex industrial environments remains challenging because existing approaches often fail to jointly model evolving inter-variable dependencies and multi-scale temporal patterns. To address these challenges, this paper proposes WaveGraphFormer (WGF), a unified framework for multivariate time series anomaly detection. The proposed method introduces a lightweight dynamic graph learning module to capture time-varying dependencies among variables and employs discrete wavelet transform (DWT) to extract multi-scale temporal-frequency features. In addition, a graph-guided residual modulation mechanism is designed to facilitate joint spatio-temporal representation learning. Experiments conducted on five public benchmark datasets (SWaT, WADI, SMAP, SMD, and MSL) demonstrate that WGF consistently achieves competitive performance in terms of F1-score and AUC compared with several state-of-the-art baselines. Ablation studies further validate the effectiveness of each component in the proposed framework. These results highlight the potential of WGF to provide reliable anomaly detection for complex industrial monitoring systems and establish a foundation for future research on adaptive spatio-temporal-frequency modeling.