Future 6G-enabled industrial systems will rely on distributed sensing and control over communication networks to manage concurrent processes, collaboratively achieving system-level operational objectives. However, various physical constraints, including excessive communication delays, complex interprocess dependencies, and dynamic system objectives, inevitably cause deteriorated operational outcomes compared to ideal conditions. To minimize this operational loss, we propose a goal-oriented digital twin (GDT) framework that overcomes these physical constraints through system orchestration in the virtual domain for dynamic objective fulfillment. Based on operational goals, the proposed GDT selectively integrates distributed sensing information into system digital twins, which then map system-level objectives into executable control tasks for individual devices. Specifically, by continuously evaluating the goal relevance of sensing data from individual devices, distributed observations are selected and prioritized, enabling control-aware communication resource allocation that balances control performance and communication efficiency. Moreover, delay-compensated control commands are accurately derived within the GDT framework, where the sensed temporal synchrony and interprocess dependencies are intentionally considered for coordinated task execution across distributed devices. Through this cohesive joint sensing and control design in the virtual domain, system-level objectives are fulfilled with minimized operational loss. Extensive simulations validate that GDT significantly improves control accuracy and resource efficiency in large-scale industrial systems.

Wireless backhauling is envisioned to play a pivotal role in 6G non-terrestrial networks (NTNs) due to its ability to deliver cable-free connectivity between edge nodes and gateways. However, the dynamic network topology and time-varying channels inherent to NTNs pose significant challenges for real-time network status monitoring. To address these challenges, we propose PhyINT, a novel in-band network telemetry approach that collects telemetry data at the physical layer for time-slotted NTNs. PhyINT allows network nodes to encode telemetry data onto resource elements (REs) in a distributed manner. Since REs are consistently available in every time slot, regardless of wireless channel variability, the encoding process can be made highly predictable and faithfully reconstructed at the gateway for decoding. Moreover, we formulate a multi-objective optimization problem that jointly minimizes the resource consumption and the telemetry collection completion latency. Extensive simulations across NTNs demonstrate that PhyINT significantly outperforms existing methods in reliability, latency, and goodput.

Achieving both a holistic and in-depth understanding of network dynamics through accurate modeling is essential for orchestrating future 6G networks, considering their increasing complexity and service diversity. However, traditional situation-agnostic data collection and network modeling approaches often undermine the efficacy and timeliness of network orchestration in such complex environments. Furthermore, temporal misalignments caused by varying modeling delays across distributed networks further impair centralized decision-making. To address these challenges, this paper proposes a hierarchical digital twin framework with an adaptive layered architecture designed for problem-oriented 6G network modeling and orchestration. At higher layers, we introduce an adaptive attribute selection mechanism that efficiently evaluates network situations and identifies problematic areas. This mechanism prioritizes critical attributes by jointly considering their relevance to current network objectives and modeling complexity. At lower layers, these prioritized attributes and critical users are selectively incorporated into scalable network modeling. More detailed digital twins are then created to deliver targeted solutions for optimizing user association and power allocation. Additionally, we implement a multi-level synchronization mechanism to ensure temporal alignment among the digital twins, thereby enhancing the effectiveness of model-based orchestration. Extensive simulations validate the efficient identification of pressing operational issues and the effective orchestration of complex 6G networks.

Wireless key generation is a promising security solution for Internet-of-Things (IoT) networks to share identical secret keys between communication pairs, whose foundation is wireless channel randomness and reciprocity. Its security, however, affects not only the generated keys but more importantly, the security of the IoT networks. So far, a number of major attacks threatening the wireless key generation have been studied in the literature, but not the replay attack. In this paper, we reveal the replay attack can penetrate conventional defense measures and invade the wireless key generation through both analysis and experiments, which can deteriorate the channel measurement correlation and result in a high key disagreement rate. We propose a wireless key generation approach named Kerra where we integrate a synchronized time measurement to defend against the replay attack on it. On a real IoT testbed composed of Long Range (LoRa) nodes, we implement the proposed Kerra and evaluate it in terms of both key generation performance and replay attack defense. Experimental results demonstrate first the impact of the replay attack on both channel measurement correlation and key disagreement rate, then the effects of quantization and pre-processing on key disagreement rate under replay attacks, and finally, the effectiveness of the proposed Kerra whose key disagreement rates under replay attacks are maintained to a similar level as without attacks.

Accurate and efficient digital twin construction through real-time multi-attribute sensing and remote concurrent data analysis is essential in supporting complex connected industrial applications. Given the unsynchronized nature and heterogeneous sampling rates of distributed sensing processes, the varying time misalignment among different attributes will inevitably deteriorate the remote correlation analysis and digital twin construction. Furthermore, application-agnostic digital twin construction approaches could potentially involve high communication and computation overhead for comprehensive digital twin construction. In this article, a concurrent end-to-end time synchronization and multi-attribute data resampling scheme is proposed to enable accurate and efficient digital twin construction at the remote end. Specifically, digital clocks are concurrently established at the remote end, with each of them associated with a sampling rate of a unique sensing attribute. To tackle the temporal misalignment among multiple sensing attributes, raw data are accurately resampled according to the same reference frequency, with attribute-specific synchronized digital clocks providing cohesively aligned time information. An edge-centric platform is established to efficiently guide the multidimensional data processing during digital twin construction. Simulation results demonstrate that the proposed scheme can achieve more accurate and efficient digital twin construction than existing modeling methods. In the end, the digital twin-driven predictive maintenance is presented as a case study, aiming at illustrating the potential applications and benefits expected of the proposed scheme in industrial environments.

Dynamically associating distributed mobile users with proper base stations in 6G heterogeneous networks (HetNets) becomes critical to achieve both diverse quality of service (QoS) requirements of all users and entire network performance. However, the significantly increased complexity of matching the irregularly distributed users and base stations as well as highly dynamic network traffic often cause unbalanced spatial-temporal loads for multi-tier base stations during user association. To overcome this challenge, we propose a new virtual network topology-based digital twin to reduce the complexity of load-balanced user association in 6G HetNets. During the digital twin construction stage, instead of using highly dynamic low-level physical layer attributes (e.g., channel conditions and SINR), we intentionally consider more stable and relevant communication performance indicators and physical statistics to effectively reflect both real-time link quality and overall network dynamics. To assist overall network operation, fast update of the digital twin for HetNets is achieved by adopting principal component analysis to discover specific network areas with changes. To improve the overall QoS provisioning and network performance, the proposed virtual topology-based digital twin is further utilized to predict the spatial-temporal dynamics of HetNets for more balanced user association by bipartite graph matching. Simulation results show that the proposed method can construct effective digital twins and support load-balanced user association with maximized network-wide QoS satisfaction.

Timestamping accuracy is of the utmost importance to achieve accurate time synchronization of large-scale connected systems. However, the heterogeneity and complexity inherent to Internet of Things (IoT) systems lead to multi-source timestamping uncertainties and significantly deteriorate performance of traditional inflexible synchronization methods. In this paper, a situation-aware hybrid time synchronization protocol is designed based on multi-source timestamping uncertainty modeling and integrated time information exchange mechanism for heterogeneous IoT systems. More specifically, the multi-source timestamping error inherent to the overall synchronization process are accurately modeled by exploring the impact of the multi-faceted operating conditions. By analyzing the real-time timestamping uncertainties, a hybrid time synchronization scheme is actualized, which can achieve optimal synchronization strategy for clock parameters estimation. In addition, an integrated time information exchange mechanism is designed to reduce timestamping redundancy during time synchronization. Simulation results show that the proposed scheme can enhance the synchronization accuracy for heterogeneous operating scenarios.

Accurate network synchronization is crucial to orchestrate distributed infrastructures in Industrial Internet of Things (IIoT) systems for accomplishing network-wide tight temporal collaboration. Traditional clock synchronization can be achieved with extensive exchanges of explicit timestamps for estimating clock offsets, which becomes impractical due to high overhead with the expansion of the network scale. The performance of conventional synchronization will also be dramatically deteriorated due to various uncertainties of IIoT networks. In this article, we propose a passive network synchronization scheme based on concurrent passive observations to calibrate the distributed clocks in IIoT systems while significantly reducing the explicit interactions and network resource consumption during synchronization. By processing the physical phenomena observed concurrently by a group of selected IIoT devices, the local clock offsets of the passive observing devices can be efficiently estimated according to the common time reference linked to the event observed. Multiple relay nodes are further coordinated by the cloud center to disseminate the reference time information throughout the IIoT system. Simulation results demonstrate that by utilizing a series of concurrent observations with efficient coordination, the proposed scheme can achieve accurate and reliable network synchronization for large-scale IIoT systems with significantly reduced network overhead.

Tight cooperation among distributively connected equipment and infrastructures of an Industrial-Internet-of-Things (IIoT) system hinges on low latency data exchange and accurate time synchronization within sophisticated networks. However, the temperature-induced clock drift in connected industry facilities constitutes a fundamental challenge for conventional synchronization techniques due to dynamic industrial environments. Furthermore, the variation of packet delivery latency in IIoT networks hinders the reliability of time information exchange, leading to deteriorated clock synchronization performance in terms of synchronization accuracy and network resource consumption. In this article, a digital-twin-enabled model-based scheme is proposed to achieve an intelligent clock synchronization for reducing resource consumption associated with distributed synchronization in fast-changing IIoT environments. By leveraging the digital-twin-enabled clock models at remote locations, required interactions among distributed IIoT facilities to achieve synchronization is dramatically reduced. The virtual clock modeling in advance of the clock calibrations helps to characterize each clock so that its behavior under dynamic operating environments is predictable, which is beneficial to avoiding excessive synchronization-related timestamp exchange. An edge-cloud collaborative architecture is also developed to enhance the overall system efficiency during the development of remote digital-twin models. Simulation results demonstrate that the proposed scheme can create an accurate virtual model remotely for each local clock according to the information gathered. Meanwhile, a significant enhancement on the clock accuracy is accomplished with dramatically reduced communication resource consumption in networks with different packet delay variations.

Accurate clock synchronization in the industr-ial-Internet-of-Things systems forms the cornerstone of distributed interaction and coordination among various infrastructures and machines in an industrial environment. However, due to the widespread use of wireless networks in industrial applications, constraints inherent to wireless networks including uncertain propagation delays, random packets losses, and unguaranteed communication resources are unavoidable, leading to dramatically increased clock synchronization error and unreliable or even outdated information. Meanwhile, time information transmissions are vulnerable to suffer from malicious attacks, causing unreliable timestamps and insecure synchronization. In this article, we proposed a distributed clock synchronization protocol based on an intelligent clustering algorithm to achieve accurate, secure, and packet-efficient clock synchronization. The varying rate of skew of every clock is collected and utilized for cluster formation as well as malicious node detection. According to established clusters, various synchronization frequencies are assigned, which can avoid excessive network access contention, reduce overall communication resource consumption, and improve synchronization accuracy. Meanwhile, a two-tier fault detection algorithm consists of outlier detection and second-order regressive model prediction is applied to determine potential malicious nodes. The simulation results demonstrate that the proposed protocol overwhelms simultaneous synchronization protocols in terms of synchronization performance and faulty node detection.