This paper presents a Digital Twin-based teaching platform that supports the main hardware courses for undergraduate education. It is part of “The 101 Plans” initiative, which aims to unify and improve computer hardware experimental courses. We develop a Digital Twin platform and a series of RISC-V-based courses on computer organization and system architecture. The platform supports hybrid teaching with physical experiment board and a Digital Twin client when the physical boards are not in hands. This setup overcomes equipment constraints, allowing for centralized hardware management and improved maintenance and efficiency. The platform can support hardware courses from digital circuits to CPU design and CPU performance optimization and finally covers System-on-Chip (SoC) development and operation system booting. We have written a series of detailed experimental guides for these courses. It combines clear instructional guidance with carefully designed problems. Some engineering challenges are intentionally required to encourage independent problem-solving and deeper hands-on engagement. The platform has been granted three patents and serves as the official competition platform for the RISC-V Cup track of the 2025 China College IC Competition. A pilot run of the Computer Organization course at Beijing Jiaotong University this year received positive student feedback and demonstrated effective learning outcomes.

The integration of 5G-TSN interworking with Low Earth Orbit (LEO) non-terrestrial networks (NTN) offers a promising approach to extend deterministic services to global and remote areas. However, the high mobility of LEO satellites induces rapidly time-varying link delays and forward-reverse asymmetry, which biases the two-way delay inference of standard IEEE 1588 Precision Time Protocol (PTP). Additionally, sparse ground gateway coverage creates connectivity voids where satellites temporarily lose access to the ground master clock. To address these challenges, we propose Ephemeris-Assisted One-Way Network Synchronization (EA-ONS), a synchronization framework designed to operate without requiring atomic clocks on every LEO satellite. EA-ONS predicts the propagation delay in advance, injecting the correction into a single Sync message, thereby avoiding bidirectional message exchange and mitigating motion-induced asymmetry. It further employs a stratum-based distributed state machine to maintain synchronization continuity via inter-satellite links (ISLs) during ground-blind intervals. System-level simulations based on a Starlink-scale constellation and public gateway distributions show that EA-ONS reduces the end-to-end synchronization error from above 1 ms (standard PTP) to below 115 ns over a 6-hop chain, reduces synchronization signaling overhead by 75%, and remains robust under high packet loss compared to standard two-way-exchange-based PTP.

IEEE standard 802.1Qav evolves from Ethernet Audio Video Bridging (AVB) to support real-time transmission by performing Credit-Based Shaping (CBS) of flows at each hop. Prior work primarily focuses on achieving a tighter upper bound of the End-to-End (E2E) delay using network calculus, given the CBS configuration of flows. However, it is important to adjust the CBS configuration at each hop to meet the deadlines for various data flows. To this end, we address the CBS scheduling problem to enhance the satisfiability of E2E delay requirements. First, we formulate a linear constraint model and propose a Double Deep Q-Network (DDQN) reinforcement learning method, called CBS-DDQN, to solve the model. Second, to improve model training and scheduling, we present a fast method using equivalence reduction to calculate the E2E upper bounds of data flows for the evaluation of E2E delay satisfiability. Finally, we conduct an extensive evaluation of CBS-DDQN, demonstrating its ability to efficiently schedule thousands of flows and increase the number of flows meeting the delay requirements by 18-26% over prior methods.

Low-Earth Orbit (LEO) satellite networks are a key enabler for the 6G Non-Terrestrial Network (NTN) architecture. However, supporting time-sensitive services in LEO networks is challenging due to highly dynamic topologies and the difficulty of maintaining precise global time synchronization. Existing Time-Sensitive Networking (TSN) mechanisms largely rely on static topologies and strict synchronization, which makes them ill-suited to dynamic LEO environments. To address this issue, we propose CRT, a deterministic transmission framework tailored for LEO networks. CRT regulates per-hop residence time using local clocks, thereby compensating for link-delay variations without requiring strict global synchronization. To handle asynchronous collisions, CRT adopts a collision-tolerant scheduling strategy that maximizes the number of schedulable flows while bounding collision-induced jitter. We formalize the corresponding scheduling problem and show that it is NP-hard. We further develop CRT-Fast, an efficient heuristic algorithm. It combines iterative layering with path continuity to control collision intensity and improve path stability under topology changes. Simulations on Iridium and Starlink constellations show that the proposed method achieves lower delay jitter and high schedulability under heavy traffic loads.

Time-Sensitive Networking (TSN) enables deterministic transmission by requiring end systems to support 802.1Qbv and 802.1AS standards, which makes practical deployment challenging, especially for asynchronous end systems (AES) such as various legacy devices already deployed in industry. We present a novel deterministic transmission scheme for asynchronous time-triggered flows from AES. First, we design an Asynchronous Deterministic Access (ADA) mechanism with two components: "Async to Sync" and "Latency Adaptation." These components allow an AES flow to wait for its scheduled period, ensuring a theoretical end-to-end (E2E) deterministic delay. Second, we propose an ADA scheduling model to optimize the scheduling periods for AES flows. However, latency adaptation may cause AES flows to conflict with other flows at the final hop switch. To address this problem, we propose a conflict resolution mechanism that determines safety distances between flows to reduce delay jitter. Finally, we implement ADA in our switches and evaluate its performance in terms of E2E delay and jitter. ADA is shown to achieve E2E delay and jitter reductions of 57.1-83.5% and 60.7-85.4%, respectively, over SOTA methods.

Time-triggered (TT) networks, such as Time-Sensitive Networking (TSN) and Time-Triggered Ethernet (TTEthernet), rely on tight time synchronization to provide deterministic message transmission for industrial real-time control. When synchronization fails, as can happen with any hardware/software (HW/SW) component, precomputed global transmission times become unreliable and deterministic transmission may be disrupted. To address this problem, we propose UT-DFT (Unsynchronization-Tolerant Deterministic Frame Transmission), a fallback mechanism that preserves deterministic transmission for selected flows upon synchronization failure. It uses local timestamps and precomputed residence times instead of global transmission instants. Based on this mechanism, we formulate the selection of flows that can remain deterministic as a Non-Overlapping Flow Problem and prove it is NP-hard. We further develop an integer linear programming (ILP) model and incremental scheduling algorithms for mixed-criticality flows, including an ILP-based algorithm and a polynomial-time approximation algorithm. A case study on the Orion Crew Exploration Vehicle (CEV) network shows that the proposed solution preserves deterministic transmission for 32% of flows on average and reduces the worst-case delay jitter of the remaining flows by 7.6–22.5x compared to existing asynchronous traffic shapers.

Time-Sensitive Networking (TSN) is crucial for deterministic communication in various critical applications. Time-Triggered (TT) scheduling, a fundamental aspect of TSN, faces scalability challenges due to its NP-complete nature. To simplify scheduling, dividing transmission time into equal time slots and each slot for once transmission, so-called time-slot strategy, has become a basic principle since the early stages of TT scheduling research. However, searching for feasible solutions based on fixed time slots weakens the schedulability of TT flows and wastes limited bandwidth, due to the mismatch between uniform slot sizes and variable frame lengths. In this paper, we break the time-slot strategy to schedule TT flows of variable lengths directly. First, we propose and theoretically prove the Conflict-Oriented Search Principle (COS), which demonstrates that the candidates for a flow’s feasible offset on a link are determined by searching occupied transmission intervals instead of searching unoccupied time slots within the period and the searching process can be optimized according to conflict in scheduling attempts. Based on this principle, we design COS Scheduler, a novel polynomial-time heuristic scheduling algorithm that operates without fixed time slots. Finally, compared with the state-of-the-art methods, extensive evaluations show that COS improves 60% schedulability and reduces 67% bandwidth cost in average by eliminating slot-induced fragmentation, while also keeping the competitive and even faster performance for thousands of TT flows.

The integration of EtherCAT and TSN has been proposed to enhance performance of EtherCAT networks in industrial automation. EtherCAT over TSN transforms a traditional EtherCAT ring into multiple shorter rings interconnected via TSN switches, enabling concurrent data transmission across segments and reducing cycle time. However, the use of multiple segments introduces contention for the master in the return path, potentially leading to scheduling failures and an increase in cycle time. We study the impact of network topology, i.e., the number of segments and slave node distribution, on schedulability, and formulate the topology optimization problem for the converged network based on schedulability analysis. We evaluate our methodology and optimal solution using SMT and Integer Programming (LIP) solvers, respectively. Numerical results demonstrate the effectiveness of our method, and our solution outperforms baselines.

With the rapid growth of Industry 4.0, time-sensitive networking (TSN) has emerged as the new infrastructure for future industrial Internet of Things (IoT) communication. Ensuring the compatibility between TSN and legacy networks is inevitable. The ideal compatibility is to achieve deterministic interconnection and interoperability without changes in hardware and communication protocols, in other words, only using standard devices with software management. This paper targets the ideal compatibility of TSN and Profinet Isochronous Real Time (IRT). First, we propose an inter-domain Multiple Transmission Opportunity Mechanism (MTOM) to enable the asynchronous converged network of TSN and Profinet. The mechanism reserves multiple transmission time slots for cross-domain data flows to reduce their end-to-end delay and jitter. Second, we formulate an asynchronous scheduling model (ASM) based on MTOM to coschedule flows in inter-and-intra domains. Finally, a case study is performed on a typical industrial network. The experiment results demonstrate that the proposed MTOM can only use standard devices to achieve deterministic transmission of Profinet and TSN converged networks. Compared with previous asynchronous converged networks, the delay and jitter are reduced by 86% and 80% on average, respectively.

Time-Sensitive Networking (TSN) has emerged as a promising network paradigm for time-critical applications, such as industrial control, where flow scheduling is crucial to ensure low latency and determinism. As production flexibility demands increase, network topology and flow requirements may change, necessitating more efficient TSN scheduling algorithms to guarantee real-time and deterministic data transmission. In this work, we present FastScheduler, a polynomial-time, deterministic TSN scheduler, which can schedule thousands of Time-Triggered (TT) flows within arbitrary network topologies. The key innovations of FastScheduler include an Equivalent Reduction Technique to simplify the generic model while preserving the feasible scheduling space, a Deterministic Heuristic Strategy to ensure a consistent and reproducible scheduling process, and a Polynomial-Time Scheduling Algorithm to perform dynamic and real-time scheduling of periodic TT flows. Extensive experiments on various topologies show that FastScheduler can effectively simplify the model, reducing variables/constraints by 35%/62%, and schedule 1,000 TT flows in subsecond time. Furthermore, it runs 2/3 orders of magnitude faster and improves the schedulability by 12%/20% compared to heuristic/deep reinforcement learning-based methods. FastScheduler is well-suited for the dynamic requirements of industrial control networks.

Time-sensitive networking (TSN) is an important research direction for the transformation and upgrading of industrial internet infrastructure. In future industrial sites, TSN and traditional industrial networks will coexist in the same network, and this integration will be inevitable. Ensuring reliable and deterministic transmission of data flows in the converged network of Profinet and TSN will be a key research topic. This paper presents a compatible way for the Cyclic Queuing and Forwarding (CQF) queuing model of TSN and the Isochronous Real-Time (IRT) communication of Profinet. Firstly, we propose a Delay Reservation Mechanism based on CQF (DRM-CQF). This mechanism achieves reliable and deterministic transmission by delaying the sending time of cross-domain data flows in the Profinet and reserving transmission opportunities for cross-domain data flows in TSN. Secondly, we construct a mathematical optimization model based on DRM-CQF to schedule data flows in the converged network to seek the optimal schedule. Experimental results show that DRM-CQF can ensure the reliable transmission of cross-domain data flows in the Profinet and TSN converged network, and the end-to-end average delay is reduced by 49% compared with other CQF scheduling methods.

Time-sensitive networking (TSN) has been widely used in industrial automation and automotive applications by precisely opening and closing the gates of packet queues. However, both clock drift and network congestion will likely result in timing misalignment when the clock synchronization protocol gPTP (802.1 AS) is used. This timing misalignment may, in turn, cause failure in forwarding packets at scheduled times and hence unexpected delay jitters, or even miss application deadlines. To address this acute problem, we propose a novel TSN scheduling algorithm, called SDT-TSN (Synchronization-Deviation-Tolerant TSN), to ensure that packets can still arrive on time and be transmitted deterministically even in the presence of inexact time synchronization. First, we formalize the linear constraint model of flow scheduling to maximize the tolerance of inexact time synchronization. Then, we propose an optimal algorithm based on SMT (Satisfiability Modulo Theories) and a fast heuristic algorithm to solve the packet scheduling problem under inexact network synchronization. SDT-TSN is the first to derive the maximum tolerable time-synchronization deviation. Finally, we evaluate SDT-TSN, demonstrating its capability of eliminating packet-forwarding failures due to the commonly-used/assumed constant time-synchronization deviation and increasing the tolerable synchronization deviation from 140µs to 480µs.

The convergence of Controller Area Network with Flexible Data-Rate (CAN-FD) and Time-Sensitive Networking (TSN) presents a critical pathway to enable deterministic cross-domain communication in next-generation intelligent vehicles. However, different transmission mechanisms are raising significant challenges in maintaining low jitter and guaranteed latency. This paper proposes a low jitter framework based on a CANFD-TSN gateway to address these limitations through three key innovations: 1) A CQF-based gateway architecture integrating cyclic queuing with deadline-aware traffic scheduling, 2) An ILP model optimizing queue switching cycles, and 3) A bidirectional phase alignment mechanism that compensates asymmetric queuing delays through gateway timestamp synchronization, achieving microsecond-level jitter suppression. Extensive OMNeT++ simulations demonstrate the framework’s effectiveness, 18% higher scheduling success rates compared to conventional methods (RCSF, SPs, EDF) under 160-flow scenarios, while reducing end-to-end jitter by 42% through alignment time compensation.

Ad Hoc networks, as a flexible type of wireless sensor network, find wide applications in disaster relief, and industrial scenarios. In industrial applications, there is a growing demand for deterministic communication to support time-critical business flows. However, previous research mainly focused on aspects like routing and re-routing, and few studies have addressed the issue of ensuring determinism. This paper proposes a novel Time-Triggered Ad Hoc (TTA) network framework. It uses a Time Division Multiple Access (TDMA)-based time-triggered transmission mechanism to achieve self-organized, deterministic communication. The framework includes time synchronization and offline scheduling to optimize transmission performance. Experimental results show that the TTA framework outperforms traditional methods in terms of network capacity, latency, and jitter, demonstrating its effectiveness in solving the determinism problem in Ad Hoc networks.

With the advancement of 5G technologies, the integration of millimeterwave, multiple-input multiple-output and non-orthogonal multiple access (mmWave MIMO-NOMA) has attracted increasing attention in the field of the industrial internet of things (IIoT). This paper focuses on the three settings of 5G mmWave MIMO-NOMA downlink transmission system, i.e., user clustering, beam selection, and power allocation, for users with diverse quality of service (QoS) requirements in industrial scenarios. The goal is to maximize the weighted sum rate of different IIoT users by joint user clustering, beam selection and power allocation. We formulate the problem and divide it into integer programming and continuous programming sub-problems, which are respectively solved by a hierarchical clustering-based user clustering and QoS-based beam selection algorithm and a QoS-based deep deterministic policy gradient (DDPG) power allocation algorithm for sub-optimal solutions. Simulation results show that compared with baselines our proposed method can greatly improve the system weighted sum rate, while meeting QoS requirements of different users.

Emerging industrial applications involving robotic collaborative operations and mobile robots require a more reliable and precise wireless network for deterministic data transmission. To meet this demand, the 3rd Generation Partnership Project (3GPP) is promoting the integration of 5th Generation Mobile Communication Technology (5G) and Time-Sensitive Networking (TSN). Time synchronization is essential for deterministic data transmission. Based on the 3GPP's vision of the 5G and TSN integrated networking with interoperability, we improve the time synchronization of TSN to conquer the multi-gNB competition, re-transmission, and mobility problems for the integrated 5G time synchronization. We implemented the improvement mechanisms and systematically validated the performance of 5G+TSN time synchronization. Based on the simulation in 500m x 500m industrial environments, the improved time synchronization achieved a precision of 1 microsecond with interoperability between 5G nodes and TSN nodes.

The vehicle's Electrical/Electronic Architecture (EEA) serves as the foundation for implementing various functions of intelligent vehicles. This paper analyzes and summarizes the layout of the distributed vehicle EEA and discusses the advantages of Time-Sensitive Network (TSN) in the field of in-vehicle communications. Building upon the analysis, this paper combines the advantages of function-oriented and zone-oriented EEA and proposes a function-zone EEA for TSN. Four EEA network topology forms are evaluated based on communication delay, network load, cost and flexibility. Based on the OMNET++ framework, this paper conducts simulation verification of autonomous driving and body control data flows. The results show that the ring and mesh network topology can significantly reduce communication delays, enabling high-speed and scalable transmission for invehicle communication.

The integration of 5G and Time-Sensitive Networking (TSN) is a hot topic for Industrial 4.0 but faces the challenge that the end-to-end transmission may experience huge jitter due to the inadaptation of the two systems. The asynchronous access mechanism (AAM) and the asynchronous traffic scheduling model (ATSM) have been proposed to solve this problem. However, periodic resources and fixed-route scheduling used by the ATSM limit the solution space of scheduling. In this paper, we propose an extended scheduling model named the NPMR-ATSM for asynchronous traffic in 5G-TSN networks, which introduces non-periodic resources and multi-route scheduling to the ATSM to extend the solution space with better schedulability. Experiments show that the NPMR-ATSM could significantly improve traffic schedulability compared to the ATSM.

Time-triggered (TT) network provides a low-cost service to meet the strong demand of modern industry networks for real-time communication. Both simulation and reachability analysis provide effective research methods for TT networks. This article presents a verification framework for the TT network based on timed colored Petri nets (TCPNs). We propose an automatic formal modeling method for the behavior of message transmissions in the TT network. We harness timed multisets of TCPN to model and analyze TT message transmission latencies. For a holistic system evaluation, we characterize the critical system properties, such as boundedness and liveness. We substantiate the properties by the reachability analysis. We demonstrate the effectiveness through a case study by simulating the message transmission and reachability analysis in the state space. Finally, we analyze the influencing factors of the state space in the automotive scenario. The proposed automatic modeling method can effectively reduce the state space scale.

In order to achieve stable and reliable industrial manufacturing, wireless networks must meet the stringent communication requirements of industrial automation, particularly the need for deterministic low latency communication. The limited wireless resources and time-varying fading channel contribute to the random fluctuations of transmission delay, making it challenging to realize delay-deterministic wireless networks. An open challenge in this context is to model delay determinism, also known as jitter, and analyze delay performance. In this paper, we model jitter as the variance of delay and conduct a comprehensive analysis of delay performance. Specifically, we consider two transmission regimes: infinite blocklength (IBL) and finite blocklength (FBL). In the IBL regime, the distribution of the transmission delay is analyzed, and the closed-form expressions for the average delay, jitter, and delay violation probability are derived. In the FBL regime, an upper bound on the transmission delay is first approximated at a high signalto-noise ratio. Based on this upper bound, the delay distribution, delay violation probability, average delay, and jitter are derived. Finally, simulation results are provided to validate the accuracy of the analysis and derivations. Additionally, the impact of system parameters on jitter is analyzed to gain further insights.