Vehicular Ad Hoc Networks (VANETs) form the foundational communication framework of intelligent transportation systems, facilitating low-latency, vehicle-to-everything data exchange for enhanced traffic efficiency and safety. Accordingly, ensuring secure, efficient, and scalable authentication is essential to maintain communication trustworthiness, especially in highly dynamic and dense traffic scenarios. While traditional public key cryptography (PKC)-based solutions offer strong security guarantees, they are computationally intensive and struggle to scale under VANET workloads. To address these challenges, this paper proposes a novel lightweight handover authentication scheme that integrates pairing-based cryptography with symmetric key primitives to ensure message integrity, anonymity, and unlinkability. The proposed solution is deployed within a real-world Reconfigurable Intelligent Surface (RIS)-assisted communication environment, enhancing the robustness and feasibility of the authentication process during handover. Furthermore, a comprehensive evaluation is conducted, comparing the computational and communication overhead of the proposed scheme with existing cryptographic protocols. Results demonstrate the superior scalability and efficiency of the proposed approach, making it well-suited for next-generation VANET applications.

This paper introduces a novel strategy for radio resource scheduling in a Coordinated Multi-Point (CoMP) Open Radio Access Network (O-RAN) environment, particularly under the constraints of a non-ideal fronthaul. We propose a comprehensive three-phase scheme, which includes the selection of Cell Edge Users (CEUEs), DPS-based clustering and switching, and a weighted mechanism for Proportional Fair (PF)-based radio resource allocation. Using a modified Vienna 5G system-level simulator, we conducted a series of detailed simulations to validate our approach in a real-world scenario. The simulation results reveal significant enhancements in both the throughput performance of CEUEs and the overall user average throughput. Furthermore, our approach demonstrates substantial improvements in the fairness index, thereby underscoring its potential to significantly improve the efficiency and user experience within 5G networks.

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.

The spatial distribution of cellular networks has made them very promising to use for localization. By knowing the location of a user, cellular networks can provide context-aware services customized to that user. Objects and the dynamic nature of indoor locations result in lots of multipath and non-line-of-sight (NLOS) propagations. In this work, we carry out a novel experimental investigation to improve indoor localization using a grid approach with channel state information (CSI) fingerprinting and artificial intelligence (AI)/ machine learning (ML) methods for determining the location of a mobile device. Experiments are conducted in a standard indoor setting. This paper compares a method for indoor positioning based on received signal strength identifier (RSSI), phase, and CSI using ML to show how the accuracy of indoor localization can be improved. Compared to heuristic approaches like DOA estimation, the precision of ML is superior.

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%.

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.

Industry 4.0 and 5.0 are paradigms of digitalization and intelligentization. The huge available bandwidth and the least spectral interference in the millimeter-wave (mmWave) band can pave the way for a wide range of new industrial automation capabilities. Sophisticated industrial applications such as industrial Internet of Things, time-sensitive networking (TSN), intelligent logistics, product tracking, remote visual monitoring and surveillance, image-guided automated assembly and automatic fault detection require high bandwidth, reliability and low latency which can be ensured using the mmWave band. In this paper, we address the physical layer (PHY) requirements of industrial communication from the viewpoint of Industry 4.0 and beyond, while highlighting the key performance indicators. This paper proposes a 60 GHz mmWave antenna system with high reliability and low latency, making it ideally suited for industrial IoT and communication. The proposed novel antenna system is designed to cover the entire 9 GHz bandwidth of the 60 GHz standard spectrum (57 to 66 GHz) with a single element peak gain of 8.2 dBi. It provides high gain and efficiency across all four channels of 60 GHz communication from 57.24 GHz to 65.88 GHz, each channel with 2.16 GHz of bandwidth. Moreover, the simulated achieved beamforming gain of the proposed antenna system reaches 16.1 dBi, which satisfies the high gain requirement for 60 GHz multi-gigabit industrial communication. The proposed antenna system is a promising physical layer candidate suitable for communication standards such as WiGig IEEE802.11ay, IEEE802.11ad, IEEE802.15.3c, ECMA-387 and WirelessHD to ensure multi-gigabit wireless communication at 60 GHz ISM band for factory automation and industrial applications.

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 $\leq 0.1$.