## 2 Related Work  
One of the primary applications of TSN is in the industrial sector, where it is anticipated to play a pivotal role in advancing Industry 4.0. In [16], several migration strategies were proposed to transition from existing proprietary industrial networks to a unified TSN-based architecture. The study emphasized that this migration should be implemented gradually to mitigate potential disruptions and accommodate the complexities of the transition. In the automotive sector, ref. [5] proposed several architectures for in-vehicle networks, emphasizing the significant advantages of utilizing TSN in these scenarios. The authors concluded their study by underscoring the necessity of implementing architectural modifications in in-vehicle networks to fully leverage the capabilities of TSN. The authors in [17]proposed a TSN-based SDN architecture for in-vehicle communications and validated its performance on a commercial car. The study highlighted the solution's capability to manage both time-critical and best-effort traffic effectively. The architecture assumes a zoned topology, enabling the coexistence of Ethernet and controller area network (CAN) protocols by implementing gateways at each zone. In this setup, sensors continue to communicate using the CAN protocol, while the core of the network operates on Ethernet, ensuring improved scalability and performance. In [14], a comprehensive review of the technologies enabling the integration of 5G networks with TSN is presented, offering a detailed summary of the state-of-the-art literature on this topic. The integration of 5G and TSN holds significant potential, particularly in scenarios where both in-vehicle communication networks and infrastructure communications are TSN-based. This convergence promises to enhance network performance, ensuring ultra-reliable, low-latency communication critical for emerging automotive and industrial applications. Research on RSUs mainly focuses on three key areas: the optimal placement of RSUs along roads, the optimal resource allocation for autonomous vehicles, and the development of innovative architectures for the IoV. The authors in [10] analyzed the effectiveness of an RSU deployment in a highway scenario, comparing independent RSUs with interconnected RSUs. They concluded that deploying disconnected RSUs offers minimal improvement in reducing message dissemination delays. In contrast, interconnected RSUs significantly enhance performance, reducing delays by orders of magnitude. The researchers in [18] developed an RSU cloud infrastructure that provides computation capabilities, which are utilized by connected vehicles as virtual machines. To optimize the system, they proposed a multi-objective ILP model aimed at minimizing the infrastructure delay, the number of deployed RSUs, and the frequency of virtual machine reconfigurations. The model tries to keep virtual machines as close as possible to the connected vehicle they are serving. Furthermore, a reinforcement learning (RL) agent was implemented to ensure that the number of reconfigurations is minimized over the long term, enhancing the overall system efficiency. In [19], the authors proposed an ILP method for the joint deployment of RSUs and the assignment of vehicles' service tasks to the deployed RSUs. The authors assumed the allocation of RSUs in crossroads and defined service areas along the surrounding roads, ensuring overlapping coverage between neighboring RSUs. The model incorporates deployment and maintenance costs as factors and enforces a maximum end-to-end delay as a constraint. The authors of [20] proposed a 5G-based IoV architecture based on SDN and fog-cloud computing. RSUs are grouped in fog clusters managed by an RSU controller, which is connected to a centralized SDN controller overseeing multiple fog clusters. The researchers proposed leveraging 5G base stations for V2I communications as a fallback when RSUs become congested.