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.

Decarbonizing the residential sector is challenging due to the complexities in balancing energy consumption optimisation, occupant comfort, and health. Digitalization has emerged as a critical enabler to address these challenges, which could offer data-driven insights to realize this balance and achieve scalable impact, which many existing studies lack. This paper introduces an integrated Cyber-Physical and AI-driven methodology to digitalize Scottish Social housing and tackle these challenges. Our approach employs a low-power Long Range Wide Area Network (LoRaWAN) based internet of Things (IoT) architecture, combining smart plugs, clamp sensors, and environmental sensors to monitor CO2, temperature, humidity, energy usage at the appliance level, and other critical parameters. Predictive analytics on air quality and energy patterns are also enabled, delivering actionable insights for retrofitting and behavioral optimization. A case study conducted in a Scottish house demonstrated the ability of the proposed system to identify energy inefficiencies. Results show that the system effectively identified energy waste: an average of approximately 30-60% of energy being consumed during unoccupied periods, across multiple appliances. Furthermore, the system achieved over 95 % accuracy in predicting CO2indoor ppm, enabling relevant proactive actions to future unhealthy air quality conditions. Furthermore, data analytics on thermal fluctuations enabled the identification of poorly insulated rooms, providing insights for actionable retrofitting strategies. The findings highlight the system's ability to reduce energy waste, lower operational costs, and optimize retrofit investments, offering a scalable pathway to achieve low-carbon living standards.

Intelligent transportation systems increasingly depend on wireless communication for broadcasting traffic messages and facilitating real-time vehicular communication. In this context, message authentication is crucial for establishing secure and reliable communication. However, security solutions must consider the dynamic nature of vehicular communication links, which fluctuate between line-of-sight (LoS) and non-line-of-sight (NLoS) due to obstructions. This paper proposes a lightweight cross-layer authentication scheme that employs public-key infrastructure (PKI)-based authentication for initial legitimacy detection/handshaking while using key-based physical-layer re-authentication for message verification. This approach reduces signature generation and signaling overheads associated with each transmission, thereby enhancing network scalability. However, the receiver operating characteristic (ROC;Pd: detection vs.PFA: false alarm probabilities) of the latter decreases with lower signal-to-noise ratio (SNR). To address this, we investigate the use of reconfigurable intelligent surfaces (RISs) to strengthen the SNR directed toward the designated vehicle in shadowed areas (i.e., NLoS scenarios), thereby improving the ROC. Theoretical analysis and practical implementation are conducted using a 1-bit RIS consisting of 64×64 reflective metasurfaces. Experimental results show a significant improvement inPd, increasing from 0.82 to 0.96 at SNR = −6 dB for an orthogonal frequency-division multiplexing (OFDM) system with 128 subcarriers. We also conducted informal and formal security analyses using Burrows-Abadi-Needham (BAN) logic to prove the scheme’s ability to resist passive and active attacks. Furthermore, the proposed scheme reduces computational and communication overheads by 43% and 13%, respectively, compared to traditional cryptographic methods, demonstrating its superiority for real-time, challenging communication scenarios.

Contactless vital signs detection has the potential to advance healthcare by offering precise and convenient patient monitoring. This groundbreaking approach not only streamlines the monitoring process, but also allows continuous, real-time assessment of vital signs, allowing early detection of anomalies and prompt intervention. This paper presents a novel framework for contactless vital sign s detection using continuous-wave (CW) radar and advanced signal processing techniques. We achieved unprecedented precision in capturing 1,261 samples for radar based heart sound waveforms compared to the ground truth ECG signal. Further, our heart sounds method yields highly accurate human heart pulse readings, surpassing previous benchmarks with a mean absolute percentage error (MAPE) of 0.0129 and mean absolute error (MAE) below one (0.8712). In addition, we derive d heart rates from the heart sound waveforms and compare them with conventional radar-derived heart rates and ground truth ECG signal. Through analysis, we identifi ed regions where conventional radar based methods exhibit limitations. Our approach demonstrates minimal errors and superior accuracy across all heart rate states, which can potentially set new standards for noninvasive vital sign monitoring.

Intelligent transportation systems require a reliable exchange of information between network terminals in different vehicular communication environments. Making effective use of the dedicated spectrum is crucial to maximizing communication performance. This requires optimising the modulation order according to different channel conditions. This paper proposes a lightweight spectrum awareness methodology that uses wideband spectrum monitoring and deep learning-based modulation classification techniques to optimise the modulation order. We introduce a channel quality indicator block in which the classifier’s accuracy of detection is used as a forward indicator for the choice of the best modulation type for transmission. By using a 3D stochastic vehicular channel, we evaluate the classification performance at different channel parameter settings, including, speed, variance, and signal-to-noise ratio in urban and rural areas. The experimental analyses demonstrate the capability of the proposed approach to supporting a high detection probability for acceptable false decision-making ≤ 20%.

The increasing demand for considering multisensor data fusion technology has drawn attention for precise human activity recognition (HAR) over standalone technology due to its reliability and robustness. This article presents a framework that fuses data from multiple sensing systems and applies neuromorphic computing to sense and classify human activities. The data is collected by utilizing inertial measurement unit (IMU) sensors, software-defined radios, and radars, and feature extraction and selection are performed on the data. For each of the actions, such as sitting and standing, an activity matrix is generated, which is then fed into a discrete Hopfield neural network as a binary feature pattern for one-shot learning. Following the Hopfield network neurons’ feedback output, the conformity to the standard activity feature pattern is also determined. Following the Hopfield network neurons’ feedback output, the training of neurons is completed after two steps under the Hebbian learning law, and the conformity to the standard activity feature pattern is also determined. According to the probabilistic statistics on inference predictions, the proposed method, that is the neuromorphic computing of the three data fused framework, achieved the box plot for the highest lower quartile output of 95.34%, while the confusion matrix classification accuracy of the two activities was 98.98%. The results have shown that neuromorphic computing is most capable of multisensor data-fusion-based HAR. Furthermore, the proposed method can be enhanced by incorporating additional hardware signal processing in the system to enable the flexible integration of human activity data.

Intelligent transportation systems are an emerging technology that facilitates real-time vehicle-to-everything communication. Hence, securing and authenticating data packets for intra- and inter-vehicle communication are fundamental security services in vehicular ad-hoc networks (VANETs). However, public-key cryptography (PKC) is commonly used in signature-based authentication, which consumes significant computation resources and communication bandwidth for signatures generation and verification, and key distribution. Therefore, physical layer-based secret key extraction has emerged as an effective candidate for key agreement, exploiting the randomness and reciprocity features of wireless channels. However, the imperfect channel reciprocity generates discrepancies in the extracted key, and existing reconciliation algorithms suffer from significant communication costs and security issues. In this paper, PKC-based authentication is used for initial legitimacy detection and exchanging authenticated probing packets. Accordingly, we propose a blockchain-based reconciliation technique that allows the trusted third party (TTP) to publish the correction sequence of the mismatched bits through a transaction using a smart contract. The smart contract functions enable the TTP to map the transaction address to vehicle-related information and allow vehicles to obtain the transaction contents securely. The obtained shared key is then used for symmetric key cryptography (SKC)-based authentication for subsequent transmissions, saving significant computation and communication costs. The correctness and security robustness of the scheme are proved using Burrows–Abadi–Needham (BAN)-logic and Automated Validation of Internet Security Protocols and Applications (AVISPA) simulator. We also discussed the scheme’s resistance to typical attacks. The scheme’s performance in terms of packet delay and loss ratio is evaluated using the network simulator (OMNeT++). Finally, the computation analysis shows that the scheme saves ∼99% of the time required to verify 1000 messages compared to existing PKC-based schemes.

In recent years, research has focused on exploiting the inherent physical (PHY) characteristics of wireless channels to discriminate between different spatially separated network terminals, mitigating the significant costs of signature-based techniques. In this paper, the legitimacy of the corresponding terminal is firstly verified at the protocol stack’s upper layers, and then the re-authentication process is performed at the PHY-layer. In the latter, a unique PHY-layer signature is created for each transmission based on the spatially and temporally correlated channel attributes within the coherence time interval. As part of the verification process, the PHY-layer signature can be used as a message authentication code to prove the packet’s authenticity. Extensive simulation has shown the capability of the proposed scheme to support high detection probability at small signal-to-noise ratios. In addition, security evaluation is conducted against passive and active attacks. Computation and communication comparisons are performed to demonstrate that the proposed scheme provides superior performance compared to conventional cryptographic approaches.

Securing data exchange between intercommunicating terminals, e.g., vehicle-to-everything, constitutes a technological challenge that needs to be addressed. Security solutions must be computationally efficient and flexible enough to be implemented in any wireless propagation environment. Recently, physical layer security has gained popularity, which exploits the randomness of wireless channel responses for extracting high entropy secret cryptographic keys. The current state-of-the-art relies on the independently varying channel sources of randomness, e.g., received signal strength (RSS) and phase. However, the limited capability of RSS-based extraction techniques has motivated researchers to investigate alternative approaches. Although phase-based approaches have emerged in many studies, optimising the extraction performance by adapting the algorithm to the non-reciprocal components of static and dynamic channels remains a challenge. In this paper, we propose an adaptive multilevel quantisation approach that adjusts the size of the quantisation region to the channel responses’ non-reciprocity parameters, thus optimising the trade-off between the bit generation rate (BGR) and the bit mismatch rate (BMR). The probability of error has been theoretically formulated. Accordingly, the order of the quantisation process is adapted for acceptable mismatching probability. Moreover, simulation analysis is conducted to prove the ability of the proposed approach to provide flexible adaptation of the quantisation order at different signal-to-noise ratios (SNRs), achieving fast secret bit generation rates 1. 1$\sim$2.85bits/packet at SNRs of 10$\sim$25 dB for acceptable BMR $\leq 0.1$.