A Joint Scheduling Scheme for WiFi Access TSN.

In the context of Industry 4.0, industrial production equipment needs to communicate through the industrial internet to improve the intelligence of industrial production. This requires the current communication network to have the ability of large-scale equipment access, multiple communication protocols/heterogeneous systems interoperability, and end-to-end deterministic low-latency transmission. Time-sensitive network (TSN), as a new generation of deterministic Ethernet communication technology, is the main development direction of time-critical communication technology applied in industrial environments, and Wi-Fi technology has become the main way of wireless access for users due to its advantages of high portability and mobility. Therefore, accessing WiFi in the TSN is a major development direction of the current industrial internet. In this paper, we model the scheduling problem of TSN and WiFi converged networks and propose a scheme based on a greedy strategy distributed estimation algorithm (GE) to solve the scheduling problem. Compared with the integer linear programming (ILP) algorithm and the Tabu algorithm, the algorithm implemented in this paper outperforms the other algorithms in being able to adapt to a variety of different scenarios and in scheduling optimization efficiency, especially when the amount of traffic to be deployed is large.

Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories.

Over recent years, traditional manufacturing factories have been accelerating their transformation and upgrade toward smart factories, which are an important concept within Industry 4.0. As a key communication technology in the industrial internet architecture, time-sensitive networks (TSNs) can break through communication barriers between subsystems within smart factories and form a common network for various network flows. Traditional routing algorithms are not applicable for this novel type of network, as they cause unnecessary congestion and latency. Therefore, this study examined the classification of TSN flows in smart factories, converted the routing problem into two graphical problems, and proposed two heuristic optimization algorithms, namely GATTRP and AACO, to find the optimal solution. The experiments showed that the algorithms proposed in this paper could provide a more reasonable routing arrangement for various TSN flows with different time sensitivities. The algorithms could effectively reduce the overall delay by up to 74% and 41%, respectively, with promising operating performances.

Design Methodology of Automotive Time-Sensitive Network System Based on OMNeT++ Simulation System.

Advances in automotive technology require networks to support a variety of communication requirements, such as reliability, real-time performance, low jitter, and strict delay limits. Time-Sensitive Network (TSN) is a keyframe transmission delay-guaranteed solution based on the IEEE 802 architecture of the automotive Ethernet. However, most of the existing studies on automotive TSN performance are based on a single mechanism, lacking a complete and systematic research tool. At the same time, the design method should be considered from a global perspective when designing an automotive TSN system, rather than only considering a single mechanism that TSN applies to. This paper discusses the correspondence between traffic types and automotive scenarios and proposes a methodology to target the delay constraint of traffic types as the design goal of automotive TSN networks. To study the performance of automotive TSN under different mechanisms such as time-aware shaper (TAS), credit-based shaper (CBS), cyclic queuing and forwarding (CQF), etc., this paper also develops a systematic automotive TSN simulation system based on OMNeT++. The simulation system plays a crucial role in the whole methodology, including all applicable TSN standards for the automotive field. Lastly, a complex automotive scenario based on zonal architecture provided by a major motor company in Shanghai is analyzed in the simulated system; verifying TSN can guarantee real-time performance and reliability of the in-vehicle network.

Guest Editorial Special Issue on Time-Sensitive Networks for Unmanned Aircraft Systems.

In this special issue, we explored swarming, network management, routing for multipath, communications, service applications, detection and identification, computation offloading, and cellular network-based control in time-sensitive networks of unmanned aircraft systems.

Time-Sensitive Network (TSN) Experiment in Sensor-Based Integrated Environment for Autonomous Driving.

Recently, large amounts of data traffic from various sensors and image and navigation systems within vehicles are generated for autonomous driving. Broadband communication networks within vehicles have become necessary. New autonomous Ethernet networks are being considered as alternatives. The Ethernet-based in-vehicle network has been standardized in the IEEE 802.1 time-sensitive network (TSN) group since 2006. The Ethernet TSN will be revised and integrated into a subsequent version of IEEE 802.1Q-2018 published in 2018 when various new TSN-related standards are being newly revised and published. A TSN integrated environment simulator is developed in this paper to implement the main functions of the TSN standards that are being developed. This effort would minimize the performance gaps that can occur when the functions of these standards operate in an integrated environment. As part of this purpose, we analyzed the simulator to verify that the traffic for autonomous driving satisfies the TSN transmission requirements in the in-vehicle network (IVN) and the preemption (which is one of the main TSN functions) and reduces the overall End-to-End delay. An optimal guard band size for the preemption was also found for autonomous vehicles in our work. Finally, an IVN model for autonomous vehicles was designed and the performance test was conducted by configuring the traffic to be used for various sensors and electronic control units (ECUs).