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.

With the rapid expansion of internet-of-things (IoT) networks, ensuring efficient data collection has become a key challenge, especially in remote and hard-to-reach areas. Unmanned aerial vehicles (UAVs) offer a flexible solution for IoT networks. However, UAV communications face challenges such as signal attenuation, interference, and line-of-sight (LoS) constraints. To address these limitations, reconfigurable intelligent surfaces (RISs) are proposed as a promising solution to enhance UAV communications. In this paper, we propose a novel federated learning (FL)-based framework for optimising RIS-assisted UAV networks. Our approach integrates deep reinforcement learning (DRL) for UAV trajectory planning and IoT device scheduling while leveraging FL to train UAV models collaboratively without sharing raw data. Additionally, a block coordinate descent (BCD) algorithm is employed to optimise RIS phase shifts. This framework enhances communication reliability, energy efficiency, and scalability, making UAV-assisted IoT networks more adaptive to dynamic environments.

Millimeter wave (mmWave) bands come with a deployment challenge of signal degradation when an obstacle blocks the line of sight (LOS) link. This paper proposes an intra-cell proactive handover (PHO) framework utilizing links from an intelligent reflective surface (IRS) to assist a 60GHz mmWave transmitter. Empowered by vision-aided wireless communication (VAWC) the PHO is aiming to replace a blocked LOS link with an IRS-assisted link. Evaluation of IRS-assisted link performance during the HO scenario is conducted to establish a solid understanding of deployment requirements and limitations. Estimations of the received signal strength indicator (RSSI) were performed to compare IRS-assisted links in the blocked area and LOS links in the absence of a blockage event. Results showed that for an IRS to provide a comparable signal level to the original LOS link, beam focusing must be the operating mode. IRS-assisted PHO scenarios were evaluated based on a range of IRS elements (64, 100 and 1000) to compare between the two links. Signal drop was between 30 to 15 dBm depending on the number of IRS elements and user location. The gap in signal level was further reduced to 10–5 dBm by increasing the number of antenna elements in the uniform linear array (ULA) sector transmitting to the IRS. Finally, the results showed that a HO to a 30GHZ IRS-assisted link with 64 ULA antenna elements and 1000 IRS elements will perform comparably to the LOS signal strength.

This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided Dynamic blockage Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model capable of simultaneously predicting blockage status and time. This enables determining the optimal point for proactive handover (PHO) or beam switching, thereby reducing the latency introduced by 5G new radio procedures and ensuring high quality of experience (QoE). The framework employs radar sensors to monitor and track object movement, generating range-angle and range-velocity maps that are useful for scene analysis and predictions. Moreover, FL provides additional benefits such as privacy protection, scalability, and knowledge sharing. The framework is assessed using an extensive real-world dataset comprising mmWave channel information and radar data. The evaluation results show that RaDaR substantially enhances network reliability, achieving an average success rate of 94% for PHO compared to existing reactive HO procedures that lack proactive blockage prediction. Additionally, RaDaR maintains a superior QoE by ensuring sustained high throughput levels and minimising PHO latency.

Line of sight (LoS) links that use high frequencies are sensitive to blockages, making it challenging to scale future ultra-dense networks (UDN) that capitalise on millimetre wave (mmWave) and potentially terahertz (THz) networks. This paper embraces two novelties; Firstly, it combines machine learning (ML) and computer vision (CV) to enhance the reliability and latency of next-generation wireless networks through proactive identification of blockage scenarios and triggering proactive handover (PHO). Secondly, this study adopts federated learning (FL) to perform decentralised model training so that data privacy is protected, and channel resources are conserved. Our vision-aided PHO framework localises users using object detection and localisation (ODL) algorithm that feeds a multiple-output neural network (NN) model to predict possible blockages. This involves analysing images captured from the video cameras co-located with the base stations (BSs) in conjunction with wireless parameters to predict future blockages and subsequently trigger PHO. Simulation results show that our approach performs remarkably well in highly dynamic multi-user environments where vehicles move at different speeds, and achieves 93.6% successful PHO. Furthermore, the proposed framework outperforms the reactive-HO methods by a factor of 3.3 in terms of latency while maintaining a high quality of experience (QoE) for the users.

Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can be used to train machine learning (ML) models for predicting energy needs. However, sharing energy consumption information to perform centralised learning may compromise data privacy and make it vulnerable to misuse, in addition to incurring high transmission overhead on communication resources. This study addresses these issues by utilising federated learning (FL), an emerging technique that performs ML model training at the user/substation level, where data resides. We introduce FedraTrees, a new, lightweight FL framework that benefits from the outstanding features of ensemble learning. Furthermore, we developed a delta-based FL stopping algorithm to monitor FL training and stop it when it does not need to continue. The simulation results demonstrate that FedraTrees outperforms the most popular federated averaging (FedAvg) framework and the baseline Persistence model for providing accurate energy forecasting patterns while taking only 2% of the computation time and 13% of the communication rounds compared to FedAvg, saving considerable amounts of computation and communication resources.

The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users’ location and speed. In addition,this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE).

Cognitive radio (CR) is envisioned as an enabling technology for the Internet-of-Things (IoT) networking by providing spectrum opportunities to the huge number of communicating IoT devices (the so-called CR-IoT networking). Due to the nature of its wireless operating media, CR-IoT networks are vulnerable to jamming attacks. In CR-IoT networks with delay-sensitive applications, such attacks can incur longer packet transmission delay, resulting in received obsolete packets and service unavailability. Therefore, an important challenge in this domain is how to perform a secured channel assignment that can provide soft guarantees on the timeline of the packet delivery while satisfying a predefined Quality-of-Service (QoS) requirements. In this article, we propose a security-aware batch-based MAC protocol for delay-sensitive CR-IoT networks that attempts at improving the network performance by exploiting the parallel transmission capabilities of the CR-IoT devices while being jamming aware. Our protocol performs simultaneous assignment decisions for contending CR-IoT devices. Specifically, we formulate the channel assignment problem that attempts at maximizing the number of served CR-IoT users subject to user-rate demand, number of transceivers per CR-IoT device, and delay (packet invalidity) constraints as an optimization problem. This problem is shown to be a binary linear programming problem, which is, in general, NP-hard. Hence, we use a sequential fixing linear programming technique, which provides a near-optimal solution. To implement our channel assignment in a distributed manner, we adopt an access window (AW)-based access protocol that allows the users to exchange their control information and conduct the spectrum assignment. Compared to reference protocols, simulation results show that our protocol significantly improves spectrum efficiency and CR-IoT network performance.