Redundancy-based routing and scheduling optimization of in-vehicle Time-Sensitive Networking

Time-Sensitive Networking (TSN) would be beneficial to enhance the communication performance of the in-vehicle network (IVN) and other industrial networks. To improve the reliability of the routing and the efficiency of the scheduling in the in-vehicle TSN, a redundancy-based routing and scheduling optimization framework is investigated in this paper. In the proposed methodology, the Non-dominated Sorting Genetic Algorithm II (NSGA-Ⅱ) generates the main routing strategy to ensure the balanced load and low delay of the routing. Considering the in-vehicle functional safety, the Graphical Evaluation and Review Technique (GERT) model is constructed to evaluate and optimize the redundancy routing strategy. Subsequently, an improved bat algorithm with an adaptive genetic algorithm is established to increase the efficiency of the TSN scheduling. The high-level autonomous driving scenario in the case study demonstrates the applicability of the methodology. The results show that the proposed method can determine efficient routing and scheduling strategies of the in-vehicle TSN. Eventually, we discuss the optimization suggestion of the electronic and electrical (E/E) architecture design through link failures analysis and sensitivity analysis. The hybrid framework is serviceable to optimize the routing and scheduling strategy of complex networks, which is an important task in industrial practice.