Reliability Analysis of the Proactive Transmission of Replicated Frames Mechanism over Time-Sensitive Networking

The Time-Sensitive Networking (TSN) Task Group has standardised different mechanisms to provide Ethernet with hard real-time guarantees and reliability in layer 2 of the network architecture. Specifically, TSN proposes using space redundancy to increase the reliability of Ethernet networks, but using space redundancy to tolerate temporary faults is not a cost-effective solution. For this reason, we propose to use time redundancy to tolerate temporary faults in the links of TSN-based networks. Specifically, in previous works we proposed the Proactive Transmission of Replicated Frames (PTRF) mechanism to tolerate temporary faults in the links. Now, in this work we present a series of models of TSN and PTRF developed using PRISM, a probabilistic model checker that can be used to evaluate the reliability of systems. After that, we carry out a parametric sensitivity analysis of the reliability achievable by TSN and PTRF and we show that we can increase the reliability of TSN-based networks using PTRF to tolerate temporary faults in the links of TSN networks. This is the first work that presents a quantitative analysis of the reliability of TSN networks.

Deep Learning to Predict the Feasibility of Priority-Based Ethernet Network Configurations

Machine learning has been recently applied in real-time systems to predict whether Ethernet network configurations are feasible in terms of meeting deadline constraints without executing conventional schedulability analysis. However, the existing prediction techniques require domain expertise to choose the relevant input features and do not perform consistently when topologies or traffic patterns differ significantly from the ones in the training data. To overcome these problems, we propose a Graph Neural Network (GNN) prediction model that synthesizes relevant features directly from the raw data. This deep learning model possesses the ability to exploit relations among flows, links, and queues in switched Ethernet networks and generalizes to unseen topologies and traffic patterns. We also explore the use of ensembles of GNNs and show that it enhances the robustness of the predictions. An evaluation on heterogeneous testing sets comprising realistic automotive networks shows that ensembles of 32 GNN models feature a prediction accuracy ranging from 79.3% to 90% for Ethernet networks using priorities as the Quality-of-Service mechanism. The use of ensemble models provides a speedup factor ranging from 77 to 1,715 compared to schedulability analysis, which allows a far more extensive design space exploration.

Post-quantum MACsec in Ethernet Networks

The demand on MACsec in Ethernet is increasing substantially since MACsec fits well for industrial applications which require strong security as well as efficiency. To provide a long-term security, the MACsec protocol should be resistant to future attacks including quantum attacks. In this paper, MACsec is analysed under a quantum attack scenario. To achieve 128-bit quantum security, AES (Advanced Encryption Standard) algorithms defined in MACsec should mandate to use 256-bit keys. On the other hand, classical public-key cryptosystems in MKA are not secure at all against quantum attacks so that they need to be replaced by post-quantum crypto schemes in a quantum world. We propose an authenticated post-quantum key establishment protocol which is suitable for long-term secure MACsec. The proposed protocol is used in the hybrid mode, an ephemeral key exchange, and an end-to-end encryption. We verified by experiments that the proposed protocol can be deployed in existing a MACsec-enabled Ethernet network.

Time-Synchronization Method for CAN–Ethernet Networks with Gateways

Time-synchronization technology can provide a common notion of time among the participating nodes on a network. This is essential not only for protocol operation for time-critical services but also for the time stamp for information included in the message. Precise time information can be very crucial for such things as autonomous driving because there are various sensor measurements from multiple cameras, and radio detection and ranging (radar) and light detection and ranging (LiDAR) are used for perceiving the current situation via sensor fusion. A well-known synchronization method, IEEE 1588, denoted as the precision time protocol (PTP), can be used for various applications. For in-vehicle networks of autonomous cars, we have to consider that the network may be comprised of subnetworks based on different protocols such as controller area network (CAN) and Ethernet. However, implementing PTPs on such heterogeneous vehicle networks causes several problems. First, the PTP procedure must be modified to be implement on a CAN network. Second, to calculate the delay and offset for PTP, the processing delay that occurs during message conversion must be considered. In this paper, we propose a synchronization method for CAN–Ethernet networks to solve these problems. The performance of the proposed synchronization method is evaluated by experiments on a real CAN–Ethernet network.