With the rise of autonomous driving, the performance requirements for In-vehicular networks are continuously increasing. Existing research leverages Frame Replication and Elimination for Reliability (FRER) and Time-Aware Shaper (TAS) mechanisms in Time-Sensitive Networking (TSN) to achieve deterministic transmission. FRER requires transmitting flows over multiple disjoint paths. However, FRER lacks redundancy degree selection strategies, and the delay differences between redundant paths lead to packet disorder, which increases network resource overhead and compromises traffic QoS. In this paper, we propose a Bandwidth-Aware with Frame Replication and Elimination for Reliability (BA-FRER) algorithm dynamically selects redundancy degree based on the current network bandwidth resources, delay, and reliability utility function. Additionally, we propose a Redundant-Aware Order Preservation (RAOP) algorithm configures time slots for each flow based on the TAS mechanism to align the delays of redundant paths. The evaluation results show that the BA-FRER algorithm improves the utilization of network bandwidth resources, the flow access rate, and the reliability, while the RAOP algorithm reduces the probability of packet disorder.

The emergence of new time-sensitive networking (TSN) technologies empowers almost-deterministic ultra-reliable low-latency communications for industrial cloud-fog automation paradigms. However, current heterogeneous networks struggle to balance time-sensitivity and flexibility, particularly in mixed-flow scenarios. Existing scheduling approaches within 5G-TSN integration model either suffer from quality of service (QoS) flow mismatch or microbursts. This paper proposes a class-aware mixed-flow scheduling (CAMFS) strategy to enable domain-specific resource allocation in a hybrid wired-wireless TSN, while meeting the differentiated time-sensitive (TS) requirements. A time-triggered multiple cyclic-queuing (TTMCQ) shaper is designed to effectively align the classified and regulated mixed flows from TSN domains to 5G QoS flows through class-aware mapping. We also introduce an anti-starvation resource optimization method that minimizes the total average idle resources resulting from temporal-spatial resource over-provisioning within reserved TS windows (RTWs). Additionally, we present a CAMFS algorithm aimed at enhancing schedulability and resource utilization by sorting mixed flows based on a combination of flow features. Finally, simulation results show that CAMFS exhibits outstanding scheduling performance in terms of end-to-end service latency, scheduling success ratio, and normalized resource distribution compared to other state-of-the-art methods.

The integration of time-sensitive networking (TSN) and fifth-generation (5G) offers a promising solution for real-time and reliable data transmission in the Industrial Internet of Things (IIoT). However, current research focuses on traffic scheduling in TSN-5G networks to support low latency. New challenges arise when TSN-5G networks leverage time-aware shaper (TAS) and frame replication and elimination for reliability (FRER) to achieve low latency and high reliability. Simply combining TAS and FRER (SCTF) requires scheduling all time-triggered (TT) flows and their replica flows, which substantially increases the computational complexity of gate control lists (GCLs) and severely weakens scheduling capabilities. Moreover, the packet elimination function (PEF) in FRER may induce packet misordering. In this paper, we propose an efficient and fault-tolerant time-aware shaper (EF-TAS) mechanism for TSN-5G networks. EF-TAS only allocates timeslots for TT flows, while replica TT (RT) flows are delivered using a best-effort strategy. Due to the potential violation of deadlines in RT flows, we design an adaptive cyclic GCL window (ACGW)-based hybrid scheduling (AHS) algorithm to schedule TT and RT flows differentially. The AHS algorithm utilizes network calculus to ensure the timely arrival of RT flows without affecting the deterministic transmission of TT flows. In particular, we provide upper bounds on the amount of reordering to quantify the disorder caused by PEF and analyze the impact of introducing the packet ordering function (POF) on EF-TAS performance. The evaluation results show that EF-TAS not only meets the reliability and deadline requirements but also significantly reduces the total number of GCL entries and the computation time of GCLs compared to state-of-the-art methods.

Time-Sensitive networking (TSN) has put forward a series of standards, such as cyclic queuing and forwarding (CQF) and frame replication and elimination for reliability (FRER), to achieve deterministic latency and high reliability. However, most work studies these two mechanisms separately, while directly combining CQF and FRER (DCCF) will inevitably introduce distinct multiple-path delays, seriously impair scheduling capa-bilities and result in a large jitter. In this paper, we propose a Multi-path CQF (MCQF) mecha-nism. Firstly, MCQF enables flexible end-to-end delay calculation by extending the ping-pong queues of CQF to multi-queues. Then, we formulate a joint routing and scheduling mathematical model to maximize the number of schedulable flows and satisfy diverse latency and reliability requirements. Moreover, a hop-by-hop offset scheduling (HOS) algorithm is designed to achieve low jitter by aligning the packet delays on multiple disjoint paths. Evaluation results show that MCQF performs better than CQF on reliability. Compared to DCCF, MCQF greatly reduces the jitter and improves the schedulable flow number by about 31.9 %.

Recent studies leverage time-aware shaper (TAS) and frame replication and elimination for reliability (FRER) techniques to achieve deterministic latency and high reliability for time-triggered (TT) flows. FRER requires transmitting TT flows on$k$disjoint paths to tolerate transient and permanent failures. However, directly allocating timeslot resources for all$k$TT flows will dramatically increase the computational complexity of gate control lists (GCLs), seriously impair scheduling capabilities, and result in a wastage of bandwidth. In this paper, we propose an ultra-reliable time-aware shaper (uTAS). uTAS only allocates timeslots for one TT flow to ensure deterministic transmission. The$k - 1$replica TT (RT) flows are delivered using a best-effort strategy. On this basis, we propose an adaptive window scheduling (AWS) algorithm based on network calculus, which aims to guarantee that RT flows reach their destinations within the deadline. Evaluation results show that uTAS can meet the flow reliability and deadline requirements. Compared to directly combining TAS and FRER (DCTF), uTAS reduces the total number of GCLs by approximately 72.9%.