Time-aware shaper (TAS) is key to enabling deterministic guarantees in time-sensitive networks (TSN), but it requires precise configuration for specific traffic scenarios. Dynamic traffic scenarios are increasingly commonplace with the rise of emerging applications, necessitating TAS reconfiguration to adapt to the changes in traffic. However, existing mechanisms primarily reconfigure TAS by generating a new gate control list (GCL) and transitioning to it, which may lead to temporary violations of bounds on delay or jitter, providing no persistently deterministic guarantees. In this paper, we propose a novel TAS reconfiguration mechanism with the virtual GCL (VGCL) to satisfy the demands of dynamic traffic while guaranteeing deterministic transmission. It implements TAS reconfiguration for dynamic traffic by embedding different VGCLs into the GCL, avoiding the need for the GCL transition. Thus, the reconfiguration problem is modeled as an embedding problem by using the VGCL and we develop algorithms to solve it. Experimental results demonstrate that our mechanism can well reconfigure TAS for dynamic traffic without the GCL transition, and increase the reconfiguration success rate in various scenarios compared with the existing approaches.

The development of immersive video service and large-scale cluster computing technology further expand the potential application scope of time-sensitive networks (TSN). In the delivery network for these emerging services, the Ultra-Service Flow (USF), which is characterized by ultra-high bandwidth and deterministic latency has become the most representative traffic type. Therefore, the route scheduling for hybrid deployment of Regular Service Flow (RSF) and USF has become an unavoidable issue within a deterministic domain. To resolve this issue, a multi-objective optimization model for joint routing of hybrid service flows is studied in this paper. Subsequently, an effective algorithm is designed to discover feasible routing solutions using a deep reinforcement learning approach with a transformer framework, followed by optimization utilizing NSGA-II. The simulation results indicate that, our proposed algorithm exhibits superior overall performance and enhanced generalization capabilities. It effectively reduces the overall latency of RSF by 10.526% and the path blocking degree of USF by 14.10256%, while significantly increasing the available bandwidth rate by 14.286%.

Time-sensitive networking (TSN) is very appealing to industrial networks due to its support for deterministic transmission based on Ethernet. The implementation of determinism typically demands for precise configuration on each output port of a TSN switch, which is complex and time-consuming. Moreover, many emerging industrial applications bring dynamic scenarios (e.g., in real-time Internet of Things), thus the configurations should change adaptively as application requirements change to provide continued determinism. In this paper, we propose a deep reinforcement learning (DRL) based adaptive configuration scheme in Software-defined time-sensitive networking (SD-TSN). The SD-TSN is a network architecture that integrates the determinism guarantees of TSN and flexible network management of software-defined networking (SDN). Based on the capability of SD-TSN, the proposed configuration scheme exploits DRL to learn from interacting with the environment for adaptive configuration. Experimental results demonstrate the effectiveness of our scheme in dynamic scenarios.

Time-aware shaper (TAS) is key to enabling deterministic transmission guarantees in Time-Sensitive Networks (TSN), but requires precise configuration for a specific traffic scenario. Traffic dynamics change scenarios are gradually increasing with the development of Industry 4.0, necessitating reconfiguring TAS to guarantee persistence determinism. However, previous reconfiguration mechanisms are mostly reconfiguring TAS by modifying the gate control lists (GCLs) to adapt to changes in traffic, which may incur a temporary violation of bounds on delays or jitter with undesirable consequences. In this paper, we propose a novel reconfiguration mechanism with the virtual GCL (VGCL) to satisfy new traffic requirements by embedding different VGCLs into the GCL, such that implements TAS reconfiguration while avoiding the GCL modification. Then we develop an incremental VGCL embedding (IVE) algorithm to determine reconfiguration details. Experimental results show that our approach can reconfigure TAS well for dynamic traffic without modifying the GCL while guaranteeing high schedulability in different scenarios.

An increasing number of Industry Internet of Things (IIoT) applications put forward the requirements for strict time synchronization and accurate time service from providers. In order to indicate the time service performance of the Internet Time Service Providers (TSPs) on user sides, we build an Internet time service monitoring system which can obtain the real-time data of time service through the Internet. The monitoring system records the time service’s performance from three major categories: NTP pool projects, National Metrology Institutes and Commercial Organizations. After that, we propose three service metrics, in terms of Availability, Stability and Accuracy, to evaluate the performance of time service provided by TSPs. On the other hand, a novel anomaly detection algorithm is proposed to gather the statistics of abnormal data and then remove the abnormal data. Experimental results show that 56 TSPs’ availability are more than 95%. It indicates that a longer transmission path results in a lower availability. The further analyzed results also denote that the link hops have no correlations of Availability, Stability and Accuracy. Moreover, the relationship between Stability and Accuracy is positively correlated.

User-generated content on social media platforms are major forces in the shaping and diffusion of popular topics. Online rumors among regular user-generated content have increased considerablely, and stimulate the diffusion of fake popular topics in the social network or even panic among people. The existing researches on rumor detection, such as the detection mechanisms based on Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM), suffer from their rough feature extraction processes, and thus need to be improved in terms of contextual feature extraction. This paper proposes a dynamic slide-window based text feature scoring and extraction mechanism, which facilitates accurate semantic structure representation. In addition, we design an effective rumor detection scheme by incorporating the proposed feature scoring and extraction with CNN. Extensive experiments on real-world datasets demonstrate that the proposed feature scoring and extraction mechanism can extract the important text features by considering their roles in on-line texts and the relations of these features, and thus the detection scheme can distinguish rumors from regular messages more accurately.