Time Sensitive Networking (TSN) has been widely considered as a promising networking technology in industrial fields as its capibility of deterministic transmission. One of the main challenges in TSN application is the complexity of network planning, including topology design, flow routing and scheduling schemes. In these three tasks, topology design plays a vital role in reducing costs and supporting the feasibility of routing and scheduling schemes. While recent researchers make some progress in routing and scheduling algorithms, most studies lack effective approaches for optimizing network topology, limiting the practical applicability of TSN. This paper addresses this problem by presenting a joint design method (JDM) for low-cost TSN topology design while also ensuring the feasibility of flow routing and scheduling. A unified mathematical model is developed to integrate TSN topology, routing and scheduling into a joint optimization problem, minimizing the overall network cost. On this basis, a one-hot vectorization technique is applied to linearize scheduling constraints to enhance the computational efficiency. Simulation results show that compared with other methods, the proposed JDM generates TSN topology at the lowest cost, ensuring flows deterministic transmission within minutes.

The increasing scale of industrial production is leading to a greater demand for communication and computing capabilities, thereby increasing the likelihood of resource competition and conflict. This can cause stochastic overall task latency (including communication and computing latency), resulting in the occurrence of overdue tasks. To guarantee deterministic delays, the integration of edge computing (EC) with time-sensitive networking (TSN) emerges as a promising technology. However, the deterministic feature of TSN increases vulnerabilities to attacks within this integration. Particularly, uncertain denial-of-service (DoS) attacks can exhaust system resources and disrupt determinacy, causing prolonged delays, or communication failures. To this end, this article proposes an attack-tolerant TSN-based edge computing (TSN-EC) architecture to guarantee task determinacy. Based on the architecture, a task-level no-wait scheduling mechanism of TSN is proposed under packet switching mode, which ensures deterministic communication delays. A robust and deterministic task offloading scheduling (RDTOS) strategy is developed to minimize the number of overdue tasks by identifying the worst-case scenario of uncertain DoS attacks, considering each task's importance. To reduce computational complexity, a two-layer decomposition algorithm is proposed by further decomposing the master problem of the conventional C-CG algorithm. Experimental results conducted on a TSN-EC testbed demonstrate the superiority of the RDTOS strategy in enhancing security and providing overall task determinacy compared to related algorithms.

The development of the Industrial Internet of Things (1IoT) has given rise to massive information from the networked controllers, sensors and actuators, leading to the increasing demands for real-time and reliable transmission. Time-Sensitive Networking (TSN) provides the deterministic mechanism guar-antee for these demands, but with an open scheduling problem. For the low-latency and low-jitter traffic, it is hard to schedule in a scalable way, that is, increasing the scheduling speed under the Quality-of-Service (QoS) requirements. Therefore, this paper constructs a no-wait forwarding (NW-TAS) model with a time-aware shaper to eliminate the queuing delay and jitter, and further converts it into a flow-aware model by divisibility theory for scheduling simplification. With the converted model, an interval transformation-based method is proposed to get the analytical expression of feasible scheduling for each flow. Then, a flow-aware NW- TAS scheduling algorithm (FANS) with cyclic interval searching is designed to compress invalid search spaces. The evaluation results show that our method decreases the transmission latency of 1000 flows by more than 23 % while increasing the scheduling speed by 43x than the existing works.

The interoperability of heterogeneous networks, including hybrid industrial wired/wireless protocols, is a vital aspect of the Industrial Internet of Things (IIoT) since various industrial protocols have coexisted. Therefore, it is hard to obtain a multiple protocol conversion solution with the flexible configuration. Moreover, the previous works do not adapt to the Time Sensitive Networking (TSN), which is considered as the potential technology of IIoT. To tackle the above problems, we proposed an architecture of the multiple conversion system supporting the conversion of multiple protocols. Then, we proposed a protocol conversion mechanism to improve the flexibility of multiple protocol conversion by separating into data and configuration planes. Moreover, we propose a TSN-compatible frame with dynamic priority mapping algorithm, which adjusts the priority in the VLAN tag by the processing delay. Finally, we develop a hardware module and system supporting multiple protocol conversion, which verify that the proposed architecture could satisfy the flexible conversion requirement through the experiments.

As the global industrial upgrading requires higher reliability and real-time performance of data communication, Time-sensitive Networking (TSN) has been widely studied. Al-though many TSN scheduling algorithms are designed, there is no standardized analysis report after scheduling and comprehensive scheduling performance evaluation. This paper presents a complete automatic report generation system to analyze the scheduling performance. To standardize various data in TSN-based manufacturing, a uniform auto-generated report model is defined based on the Open Platform Communication Unified Architecture (OPC UA). A learning-based performance evaluation (LPE) method is established to comprehensively analyze the performance of TSN scheduling. In LPE, analytical hierarchy process (AHP) and entropy weight method (EWM) is adopted to optimize the weight distribution of performance indexes objectively, and convolutional neural network (CNN) is used to get the final evaluation result rapidly. Compared with the previous evaluation methods, simulations show the training time of the evaluation method is significantly reduced.

Time-Sensitive Networking (TSN) is a series of standards designed to enhance reliable and real-time transmission. Switching architecture is the essential component in TSN switches to guarantee the different quality of service (QoS) requirements. However, most existing TSN switching architectures are based on the fixed queue scheduling such as Input Queue or Output Queue, which leads to complex schedule processes and inefficient memory utilization. Therefore, a flexible and efficient switching architecture is needed in terms of heterogeneous traffics requirements. This paper proposes a Virtual-Queue Switching (VQS) architecture with Parallel Shared Memory (PSM) for TSN switches. First, we develop a virtual queue scheduler using the queue ID and the flow rank. By configuring the queue ID and the flow rank, the VQS architecture supports multiple scheduling strategies. Second, parallel shared memory (PSM) management is designed to ensure that storage of TS flows takes precedence over other flows. All kinds of flows share storage resources in PSM, which improves memory utilization. At last, a scheduling algorithm for IEEE 802.1Qbv based on VQS architecture is designed. Compared From the comparison results with the previous switching