Shihui Duan

With the rapid evolution of the Industrial Internet of Things (IIoT), industrial networks are required to support massive industrial devices with bounded low-latency transmission. To address these requirements, integrating the fifth-generation (5G) with time-sensitive networking (TSN) has been proposed. However, existing joint resource allocation methods struggle to achieve seamless low-latency deterministic scheduling between the 5G system (5GS) and TSN networks. Focusing on large-scale uplink transmission scenarios, a base station (gNB)-driven joint time-frequency resource allocation architecture is proposed. Within this architecture, the 5GS is modeled as a TSN bridge seamlessly integrated with the TSN cyclic queuing and forwarding (CQF) mechanism. Existing joint resource allocation algorithms suffer from local optima, low computational efficiency, and inability to capture global time-triggered (TT) flow interactions for globally optimal solutions. Accordingly, a gNB-driven multi-agent proximal policy optimization (MAPPO)-based joint time-frequency resource allocation algorithm, termed gNB-DMJRA, is further proposed. This algorithm adopts the centralized training and distributed execution (CTDE) framework, leveraging global information to better coordinate the scheduling of multiple TT flows and avoid convergence to local optimal solutions. In addition, the periodicity of TT flows is exploited to reduce computational complexity and the action space, thereby further improving learning efficiency and convergence. Simulation results demonstrate that under 1000 TT flows, the proposed algorithm reduces maximum latency by up to 74.27%, improves the scheduling success rate by 82.15%-331.75%, and achieves faster convergence than benchmarks, confirming its effectiveness and efficiency for large-scale TT flow scheduling.

The computing power network (CPN) serves as a key infrastructure for future networks, facilitating the connection of ubiquitous computing resources distributed across various locations. The continuous emergence of computation-intensive and delay-sensitive applications highlights the crucial need to fully utilize limited computing resources and the importance of building a resilient communication network. Primary-backup (PB) based transmission is a commonly used technique to enhance network reliability. However, implementing this approach in CPN with a consideration of load balancing introduces significant complexity and has received limited research attention. In this paper, we designed a deterministic computing power network (Det-CPN) architecture based on segment routing over IPv6 (SRv6). On top of the above architecture, we proposed a best computing node selection method based on a comprehensive index calculation and ranking (CICR) algorithm to determine the optimal computing node for task transmission. Subsequently, we developed a bandwidth sharing-based multi-path transmission (BSMT) algorithm to realize the maximization of the system efficiency. Simulation results demonstrate that in adverse network conditions (overloaded with a failure rate of 0.02), compared to the traditional dual-path redundant forwarding mechanism, the proposed solution achieves an average reduction of 22.3% in transmission latency, an average improvement of 39.94% in task success rate, a decrease of 19.4% in bandwidth occupation rate, and an increase of 31.05% in computing resource utilization rate.

In time-sensitive networking (TSN), precise clock synchronization is an important guarantee for the normal operation of the network. The collection of timestamps plays a crucial role in common centralized clock synchronization, directly affecting synchronization accuracy. However, the exchange of timestamps is susceptible to the impact of packet loss. To address this issue, this letter proposes a matrix completion scheme based on random matrix approximation to recover the lost timestamp information. First, we formulate the clock synchronization problem based on incomplete timestamp information as a low-rank matrix completion problem. By utilizing the principle of random matrix approximation, we can efficiently recover complete timestamp information using partial timestamp information. Furthermore, the recovered timestamp information is then used to estimate the clock skew and clock offset with a maximum likelihood estimator (MLE). Numerical results confirm the effectiveness and accuracy of the proposed approach.

Ship cyber-physical system (SCPS) is a complex system that integrates advanced information and communication technology to achieve efficient energy management and navigation plans. Thus, SCPS is susceptible to cyber attacks due to increased exposed attack surfaces, which poses significant risks to power and navigation management. The interdependence among the navigation, power, and communication management might amplify the impacts of cyber attacks to induce cascading failures. However, the existing research lacks a robust systematic strategy against cyber attacks considering interdependence features. Therefore, this paper proposes a transportation-energy-communication integrated architecture to collaboratively optimize navigation planning, power management, and networking scheduling. The network model integrates time-sensitive networking (TSN) to improve data transmission reliability and determinacy against cyber attacks. Based on the architecture, the impacts of two typical types of cyber attacks are modeled, including data availability and control signal attacks. A robust cyber-physical defense strategy is formulated into a tri-level MINLP problem to minimize load shedding and voyage distance reduction while saving operation costs by identifying the worst case. To reduce computation complexity, the problem after linearization is decomposed based on the time scale of cyber and physical layers. Case studies indicate that the proposed strategy improves system resilience to conventional strategies.

Industrial time-sensitive networking (TSN) is pivotal for ensuring real-time and reliable flow transmission. There is a growing focus on its scalable scheduling for time-critical flows pursuing ultralow latency and jitter. Its time-aware shaper protocol tackles uncertain delay and frame loss but introduces high scheduling complexity. However, existing works lack a scheduling feature mining mechanism. They impose unnecessarily tight rules to simplify the problem but sacrifice scheduling optimality. To address this, especially in industrial networks with large-scale complex flows, we propose a flow-overlap graph based distributed scheme to improve scheduling scalability concerning schedulability, scheduling efficiency, and latency and jitter. The distributed framework is established with the pipeline-parallelism pattern and verified superior in scalability. It first incorporates the deterministic feature into the distributed TSN configuration standard. Under this, specific scheduling is refined by building a so-called flow-overlap graph that efficiently characterizes flow-based scheduling features and further designing a hierarchical scheduling algorithm GFD. This scheme Pareto dominates the three scalability criteria theoretically and simulatively.

This paper presents a comprehensive analysis of IEEE 1588v2 (Precision Time Protocol version 2, PTPv2) and IEEE 802.1AS (Generalized Precision Time Protocol, gPTP), two major protocols used for high-precision time synchronization in industrial networks. We first outline the technical principles of PTPv2 and gPTP, and then identify several common factors that affect their synchronization accuracy, including asymmetric transmission delay, path delay, timestamp sampling points, clock frequency and resolution, measurement methods for path delay, clock drift and jitter, control algorithms, and sync message transmission frequency. Through a comparative analysis, we highlight key differences between these two protocols and discuss their implications for synchronization accuracy, fault tolerance, concurrency, and scalability. We also introduce a time synchronization testing environment that was built using existing laboratory equipment and elaborate on its construction and testing setup. The paper concludes with a thorough experimental comparison of PTPv2 and gPTP, offering valuable insights into their performance in real-world scenarios. The results of our study can guide network engineers in choosing the most suitable time synchronization protocol for their specific industry requirements.