Road transport accounts for approximately 75% of emissions within the transportation sector, highlighting the need not only for cleaner vehicles but also for intelligent, connected infrastructure. Cyber-physical infrastructure (CPI) enables emerging technologies such as intelligent transport systems (ITS) and digital twins (DT), providing a foundation for enhanced planning, decision-making, and real-time optimisation. The effectiveness of DT-enabled ITS depends on reliable, low-latency communication networks like 5G and beyond, which face challenges such as beam blockage due to urban mobility and obstructions. To address this challenge, we propose a configurable simulation framework that models realistic urban scenarios, including connected autonomous vehicles (CAVs) dynamics, traffic congestion, and roadside units (RSUs) deployment strategies. Through three case studies, we examine the influence of traffic density, RSU height, and RSU count on beam blockage events and received signal strength (RSS). Our findings highlight key trade-offs: while taller RSUs reduce beam blockages, they incur greater propagation losses; likewise, denser RSU deployments improve connectivity up to a point, beyond which additional units result in marginal improvements. These insights provide practical guidance for designing resilient, low-latency communication infrastructures and highlight the need for intelligent, adaptive solutions to proactively mitigate blockage events in real time for sustainable and time-sensitive ITS applications.

In the past decade, Digital Twins (DTs) have emerged as a key enabler of industry digitalisation. As digital representations of physical objects and processes, DTs integrate a range of technologies to support applications from process monitoring to policymaking. However, increased system integration expands their attack surface, heightening cybersecurity risks. Efforts to integrate DTs into larger ecosystems have further intensified these concerns. Cybersecurity is inherently a socio-technical challenge influenced by various organisational and governance considerations. However, cybersecurity research on DTs has remained predominantly technical. As such, the current understanding of how organisational cybersecurity challenges emerge in DT contexts is limited. This work addresses this gap through a critical analysis of literature, examining how cybersecurity is conceptualised in DT implementation and the extent to which organisational cybersecurity challenges are addressed. Our analysis demonstrates that cybersecurity is widely acknowledged as a challenge in DT implementation. Nevertheless, most research offers limited in-depth analysis of specific cybersecurity challenges in real-world contexts. When addressed, cybersecurity was primarily framed around confidentiality and privacy, while other elements including data integrity and system availability are overlooked. Where cybersecurity has been the primary focus, works have been overwhelmingly technical, overlooking organisational complexities that affect cybersecurity in practice. This highlights a clear gap in understanding of how organisational cybersecurity challenges emerge in DTs. We conclude by outlining an agenda for future research to support more effective and secure approaches to DT implementation.

Semantic communication (SemCom) shifts the focus from bit-level accuracy to the preservation of meaning, enabling more efficient and robust transmission. To achieve a high utilization of the wireless channel in SemCom, in this paper, we propose a channel assignment approach for polar code-based SemCom that allocates polarized channels according to semantic importance. Specifically, by combining eye-tracking data with semantic segmentation, we define two metrics that capture the contribution and correlation of semantic entities within an image. Leveraging these semantic metrics and polarized channel reliabilities, we formulate a constrained 0-1 optimization problem for polarized channel assignment and develop a priority-based algorithm that dynamically prioritizes semantically important content. Simulation results demonstrate that our method significantly outperforms the traditional channel allocation policy, especially under harsh channel conditions, by preserving critical visual information while reducing overall transmission redundancy.

With the significant advances in generative AI (GAI) and the proliferation of mobile devices, providing high-quality AI-generated content (AIGC) services via wireless networks is becoming the future direction. However, the primary challenges of AIGC service delivery in wireless networks lie in unstable channels, limited bandwidth resources, and unevenly distributed computational resources. In this paper, we employ semantic communication (SemCom) in diffusion-based GAI models to propose a resource-aware workload-adjustable transceiver (ROUTE) for AIGC delivery in dynamic wireless networks. Specifically, to relieve the communication resource bottleneck, SemCom is utilized to prioritize semantic information of the generated content. Then, to improve computational resource utilization in both edge and local and reduce AIGC semantic distortion in transmission, modified diffusion-based models are applied to adjust the computing workload and semantic density in cooperative content generation. Simulations verify the superiority of our proposed ROUTE in terms of latency and content quality compared to conventional AIGC approaches.

With the significant advances in AI-generated content (AIGC) and the proliferation of mobile devices, providing high-quality AIGC services via wireless networks is becoming the future direction. However, the primary challenges of AIGC services provisioning in wireless networks lie in unstable channels, limited bandwidth resources, and unevenly distributed computational resources. To this end, this paper proposes a semantic communication (SemCom)-empowered AIGC (SemAIGC) generation and transmission framework, where only semantic information of the content rather than all the binary bits should be generated and transmitted by using SemCom. Specifically, SemAIGC integrates diffusion models within the semantic encoder and decoder to design a workload-adjustable transceiver thereby allowing adjustment of computational resource utilization in edge and local. In addition, aresource-aware workloadtrade-off (ROOT) scheme is devised to intelligently make workload adaptation decisions for the transceiver, thus efficiently generating, transmitting, and fine-tuning content as per dynamic wireless channel conditions and service requirements. Simulations verify the superiority of our proposed SemAIGC framework in terms of latency and content quality compared to conventional approaches.

Emergency communication is critical but challenging after natural disasters when the ground infrastructure is devastated. Unmanned aerial vehicles (UAVs) have enormous potential for agile relief coordination in such scenarios. However, effectively leveraging UAV fleets poses additional challenges, in terms of security, privacy, and efficient collaboration across response agencies. This paper presents a robust blockchain-enabled framework to address these challenges by integrating a consortium blockchain model, smart contracts, and crypto-graphic techniques to securely coordinate UAV fleets for dis-aster response. Specifically, we make two key contributions: a consortium blockchain architecture for secure and private multi-agency coordination and an optimized consensus protocol balancing efficiency and fault tolerance using a delegated proof of stake practical Byzantine fault tolerance (DPoS-PBFT). Com-prehensive simulations show the framework's ability to enhance transparency, automation, scalability, and cyber-attack resilience for UAV coordination in post-disaster networks.

To accommodate the growing number of heterogeneous radios with limited wireless resources, symbiotic communication (SC) inspired by biology has been recently proposed to establish a symbiotic radio (SR) ecosystem. In this SR ecosystem, through collaboratively optimizing service/resource exchange policies, radios can coevolve like organisms, thus enabling various radio resources (such as spectrum, energy, and computing power) to complement each other. However, one critical challenge is securing a trusted coevolution environment in an SR ecosystem since the SRs with different network operators should coevolve under unreliable wireless links with complex electromagnetic interference. Moreover, multidimensional resources participated and a wide array of service requirements pose additional challenges to service/resource exchange decision-making across massive SRs. In this paper, we propose a Blockchain-empowered Intelligent cOevolution scheme for SRs, named BIO-SR. Specifically, BIO-SR exploits the digital acyclic graph (DAG) blockchain consensus in securing a trusted environment of SRs and applies deep reinforcement learning (DRL) in service exchange decision-making. The simulation results show that the BIO-SR scheme outperforms conventional solutions in terms of transmission rate and latency under both non-attack and malicious attack scenarios.

Cache-enabled device-to-device (D2D) communication is a potential approach to tackle the resource shortage problem. However, public concerns of data privacy and system security still remain, which thus arises an urgent need for a reliable caching scheme. Fortunately, federated learning (FL) with a distributed paradigm provides an effective way to privacy issue by training a high-quality global model without any raw data exchanges. Besides the privacy issue, blockchain can be further introduced into the FL framework to resist the malicious attacks occurred in D2D caching networks. In this study, we propose a double-layer blockchain-based deep reinforcement FL (BDRFL) scheme to ensure privacy-preserved and caching-efficient D2D networks. In BDRFL, a double-layer blockchain is utilized to further enhance data security. Simulation results first verify the convergence of the BDRFL-based algorithm, and then demonstrate that the download latency of the BDRFL-based caching scheme can be significantly reduced under different types of attacks when compared to some existing caching policies.

Cache-enabled device-to-device (D2D) communication has been widely deemed as a promising approach to tackle the unprecedented growth of wireless traffic demands. Recently, tremendous efforts have been put into designing an efficient caching policy to provide users better quality of service. However, public concerns of data privacy still remain in D2D cache sharing networks, which thus arises an urgent need for a privacy-preserved caching scheme. In this study, we propose a double-layer blockchain-based federated learning (DBFL) scheme with the aim of minimizing the download latency for all users in a privacy-preserving manner. Specifically, in the sublayer, the devices within the same coverage area run a federated learning (FL) to train the caching scheme model for each area separately without exchange of local data. The model parameters for each area are recorded in sublayer chains with Raft consensus mechanism. Meanwhile, in the main layer, a mainchain based on practical Byzantine fault tolerance (PBFT) mechanism is used to resist faults and attacks, thus securing the reliability of FL updates. Only the reliable area models authorized by the mainchain are utilized to update the global model in the main layer. Numerical results show the convergence, as well as the gain of download latency of the proposed DBFL caching scheme when compared with several traditional schemes.