Industry 5.0 envisions the seamless integration of automation, human-machine collaboration, and intelligent systems to enable highly flexible and adaptive manufacturing. However, this puts stringent requirements on the communication systems extending beyond the capabilities of current 5G technologies. This gap motivates the adoption of the digital twin (DT) paradigm, where a digital replica of physical assets and networks enables continuous monitoring, simulation, and optimization of industrial processes. This paper investigates the optimization of network access protocols, which are critical to maintaining uninterrupted industrial production. Specifically, we develop a reinforcement learning (RL) algorithm embedded within the DT framework to ensure reliable communication and production flow continuity. In a congested factory environment, an autonomous guided vehicle (AGV) must transmit data to a base station (BS) using an ALOHA-like protocol at terahertz (THz) frequencies. The DT-enabled RL model dynamically learns traffic patterns and adaptively selects backoff (BO) times for network access, maximizing reliability without requiring prior knowledge of system topology. Extensive evaluations across diverse operating scenarios–ranging from static to mobile settings, varying traffic loads, and different data and action space sizes–confirm the robustness, scalability, and adaptability of the proposed solution in highly dynamic industrial environments against conventional and advanced baselines.

Precise clock synchronization underpins deterministic operation in wireless systems spanning industrial automation, vehicular networks, distributed extended reality (XR), smart infrastructure, and wide-area precision agriculture. Wireless links introduce variable propagation delays, channel asymmetry, interference, clock drift, and scalability constraints that make sub-microsecond alignment difficult. This article provides a comprehensive survey and tutorial on wireless clock synchronization. We introduce a five-dimension taxonomy covering: system architecture, synchronization mechanism, correction strategy, delay and uncertainty modeling, and resource and deployment class, and apply it to eight canonical protocol families and to synchronization as realized across IEEE 802.15.4, ZigBee, Bluetooth low energy (BLE), LoRa, Wi-Fi, Ultrawide band (UWB), and 4G/5G/6G systems. We examine solutions across five application domains: industrial automation and Industrial Internet of Things (IIoT), vehicular V2X, distributed XR and metaverse, infrastructure monitoring, and wide-area Internet of Things (IoT) and precision agriculture; alongside tools, testbeds, and datasets supporting evaluation. Open challenges addressed include scalability and mobility, ultralow-jitter determinism, robust clock parameter estimation under non-Gaussian delay distributions, distributed and consensus-based synchronization for infrastructure-free networks, secure and resilient synchronization against wireless-specific threats, and cross-domain convergence encompassing time-sensitive networking (TSN)–5G/6G interoperability and joint communication, sensing, and timing as an emerging 6G design paradigm. Together, these contributions provide the first unified cross-technology framework connecting fundamentals, protocol families, application domains, and open research challenges in wireless clock synchronization.

This paper highlights the value of Digital Twins (DTs) in optimizing and maintaining Cyber-Physical Systems (CPSs) across domains like manufacturing and communication. It proposes the interaction between an industry DT and a 6G DT and identifies Situational Awareness (SA) as a key step toward supporting critical services. A use case involving 6G communication with mobile User Equipment (UE) in manufacturing is analyzed to identify critical operating scenarios and how SA can predict and mitigate the risk of failure in these scenarios. Based on this use case, relevant parameters for improving the SA of the entire CPS are identified in both DTs and an example of interaction is given for optimizing the joint task planning and execution. Additionally, the benefits of cross-domain SA are demonstrated through 5G testbed measurements. The use case illustrates the broader applicability of our proposal.

Reliable time synchronization is of utmost importance within 6G networks, particularly when envisioning their role in future industrial automation scenarios that demand deterministic communication. In particular, we focus on the integration of 6G with time-sensitive networking (TSN). Traditionally, the best timeTransmitter clock algorithm (BTCA) is used to dynamically select a grandmaster (GM) for TSN nodes in a 6G-TSN network. However, BTCA’s performance in case of link or device failures is categorized to be slow. The IEEE 802.1ASdm standard addresses this limitation by introducing a hot standby GM in the network. In this paper, we extend the hot standby amendment to 6G-TSN networks to support continuous time synchronization. For the analysis, we develop a simulation framework in OMNeT++. We analyze the performance in terms of clock drift and out-of-sync time for different time synchronization scenarios in case of failures. Having two synchronized GMs in the network reduces the out-of-sync time in case of failure when compared to BTCA.

We demonstrate a private 5G network integration with precise ultra-wideband (UWB)-based localization for enabling a collaborative robots use case in an industrial environment. The results confirm 5G’s ability to provide high throughput, low latency, and reliable connectivity, even in complex industrial environments. UWB provides real-time decimeter-level wireless localization accuracy, critical for reliable and precise object localization. These combined capabilities are essential for real-time monitoring, automation, and operational efficiency. The developed demonstrator serves as proof of concept, showcasing how 5G, when coupled with advanced localization technologies, can optimize industrial processes and improve safety. This study offers a valuable system performance evaluation under real-world conditions for researchers and industry, underscoring the transformative potential of 5G in future industrial systems and its integration with precise wireless localization technology.

Digital Twin (DT) technology has emerged as a transformative paradigm across various domains, offering powerful capabilities to monitor and optimize processes prior to real-world deployment. DTs are well-suited for next-generation deployment, as well as industrial applications, in which the dynamicity and complexity of processes pose significant challenges. This study proposes a novel DT-based framework that integrates network infrastructure with shop-floor industrial operations, where robotic arms execute pick-and-place tasks and communicate with a base station (BS) using a contention-based medium access control (MAC) protocol (i.e., ALOHA and carrier sense multiple access (CSMA)) at THz frequencies. The framework aims to optimize closed-loop production systems by enabling continuous and bidirectional data exchange between the robotic arms and the BS. A centralized reinforcement learning (RL) model enables joint optimization of backoff (BO) selection and task allocation, enhancing production efficiency while preserving workflow continuity. The results prove that the proposed framework significantly outperforms state-of-the-art (SoTA) solutions for network and industrial operation performance, achieving a 55.2% reduction in average latency, a 9.8% improvement in success probability, and a 27.96% increase in processed product rate. These findings highlight the robustness and effectiveness of the proposed framework across varying operational scenarios and MAC protocols.

Industry 5.0 marks a transition from the digitalization focus of Industry 4.0 to a paradigm emphasizing resilience, sustainability, and human-centric processes. In such dynamic networks, reinforcement learning (RL) algorithms can play a crucial role in enhancing performance. The paper proposes a novel approach using a centralized RL algorithm to optimize the medium access control for a moving autonomous guided vehicle (AGV) on an industrial shop floor. This ensures uninterrupted production flow by dynamically managing the network traffic. The use case scenario considers a mobile AGV transmitting data to a base station (BS) within a harsh industrial environment. It uses the RL algorithm to dynamically select an optimal backoff (BO) time for an ALOHA-like channel access protocol. This enables accurate data transmission without prior knowledge of the industrial environment. The results show an improvement of up to 34.6% in success probability compared to traditional BO design approaches. The RL model achieves outstanding performance, guaranteeing a minimum success probability of 99.46%.

The rise of industrial applications such as autonomous systems, industrial automation, and precision manufacturing has increased the need for resilient and continuous time synchronization. Time-sensitive networking (TSN) addresses these requirements using the IEEE 802.1AS standard, which implements the generalized precision time protocol (gPTP) for sub-microsecond accuracy. However, best timeTransmitter clock algorithm (BTCA) used for grandmaster (GM) selection is slow to recover from failures and cannot detect transient faults, resulting in unstable synchronization. The IEEE 802.1ASdm standard addresses these limitations by disabling BTCA and introducing a static configuration with a hot standby GM. This paper presents a first analysis of time synchronization in TSN networks with hot standby. Using OMNeT++, we compare BTCA and hot standby performance in terms of out-of-sync time and clock offset during failures. The results demonstrate a close to zero out-of-sync time with hot standby as compared to BTCA.

Industry 4.0 promises the increase of productivity and efficiency within manufacturing and industrial processes. A key milestone in this evolution is the 3GPP specification on time-sensitive communication and integration of 5G with time-sensitive networking (TSN), thus enabling the required flexibility for future time-sensitive industrial applications. Although 3GPP introduces ultra-reliable low latency communication (URLLC) features to meet stringent timing requirements, the notable challenge persists in resource overprovisioning, limiting the capacity of the next-generation radio access network (NG-RAN). Our work addresses this by focusing on the sources of packet delay and packet delay variation within the 6G system. Through analysis and proposed enhancements, supported by simulation and measurement results, we aim to minimize queuing delay and optimize 6GS capacity. Our study not only explores standardized 5GS features but also proposes extensions to support periodic deterministic traffic, offering insights to enhance 6GS performance and capacity in real-world scenarios.