We simultaneously minimize the latency and improve energy efficiency (EE) of the multi-user multiple-input multiple-output (MU-MIMO) rate splitting multiple access (RSMA) downlink, aided by a reconfigurable intelligent surface (RIS). Our results show that RSMA improves the EE and may reduce the delay to 13% of that of spatial division multiple access (SDMA). Moreover, RIS and RSMA support each other synergistically, while an RIS operating without RSMA provides limited benefits in terms of latency and cannot effectively mitigate interference. Furthermore, increasing the RIS size amplifies the gains of RSMA more significantly than those of SDMA, without altering the fundamental EE-latency trade-offs. Results also show that latency increases with more stringent reliability requirements, and RSMA yields more significant gains under such conditions, making it eminently suitable for energy-efficient ultra-reliable low-latency communication (URLLC) scenarios.

While Vehicular Ad-hoc Networks (VANETs) can potentially improve driver safety and traffic management efficiency (e.g. through timely sharing of traffic status among vehicles), security and privacy are two ongoing issues that need to be addressed. This paper proposes a novel blockchain-based authentication scheme leveraging conditional privacy within temporary platoons. The proposed model is an extended model based on Temporary Blockchain Platoons Formation. Our scheme utilizes blockchain technology to ensure data integrity and prevent unauthorized access, while enabling conditional privacy preservation where data sharing is limited based on pre-defined trust levels within the platoon. This significantly reduces reliance on Roadside Units (RSUs), leading to a cost-effective approach. Compared to traditional Public Key Infrastructure (PKI), our scheme offers improved security, enhanced privacy, and reduced infrastructure costs, promoting wider VANET implementation. The augmented model also incorporates an “Accident Prevention Mechanism,” a critical component absent in the preceding Platoon proposed model, thereby enhancing the overall safety and robustness of the system. Apart from the extra proposed features, rest features and mechanisms will be working as in already present Platoon Model.

Unlike prior works assuming perfect attack detection, this paper investigates the physical layer security of a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided network under a full-duplex active eavesdropper in the more realistic scenario where its jamming remains undetected by the legitimate transmitter. Assuming Nakagami-m fading channels, we first derive the probability density functions (PDFs) of the signal-to-interference ratios (SIRs) at both the legitimate user and the eavesdropper. Then, using the PDFs, we obtain novel closed-form expressions for key secrecy metrics, including the probability of nonzero secrecy capacity (PNSC), secrecy outage probability (SOP), and average secrecy capacity (ASC). Moreover, the asymptotic PNSC, SOP, and ASC at high signal-to noise ratio (SNR) are also derived. Based on those asymptotic expressions, we present compact expressions for several key performance indicators: 1) the secrecy diversity order and the secrecy array gain of the SOP, and 2) the high SNR slope and the high SNR power offset of the ASC. Results demonstrate that, compared to the passive eavesdropper scheme, the active jamming attack carried out by Eve will not cause any loss in secrecy diversity gain and high SNR slope, but only result in the loss of secrecy array gain and high SNR power offset. Finally, we verify our analytical results via simulations.

In this paper, we propose a novel signal processing method for road surface roughness classification exclusively from wheel speed sensor signals. Road surface quality has a significant impact on fuel consumption and driving safety. Traditionally, it has been measured using specially equipped vehicles and, more recently, shared via cloud-based infrastructure; however, such data can be unavailable or quickly become outdated, making onboard solutions essential. We analyzed a large wheel speed sensor dataset from various test maneuvers to determine how road surface roughness influences spectral characteristics under different conditions, including changes in speed, tire pressure, payload, and tire type. The proposed road surface roughness classifier uses only wheel speed sensor signals. It selects signal segments appropriate for processing based on driving conditions and computes their order spectra. The number and relative power of the spectral peaks within the identified interval of interest of the order spectrum are related to road surface roughness. The implemented classifier is capable of distinguishing between rough and smooth road surfaces based on the number of peaks in the interval of interest. The overall accuracy of the implemented road surface roughness classifier was 87.4%.

The rapid electrification of the transportation sector offers a promising avenue for ancillary services through Vehicle-to-Grid (V2G) applications. This is particularly critical for low-inertia systems, such as the UK grid, where the transition toward converter-based renewable generation necessitates very fast frequency response. Therefore, the viability of V2G for high-value frequency markets is constrained by strict latency requirements (e.g. one second)). Existing literature has predominantly focused on high-level economic aggregation models or communication network delays, largely neglecting the stochastic physical response dynamics of the EV On-Board Charger (OBC). This paper addresses this gap by developing a discrete-time Markov chain model that specifically characterizes the internal dynamics and response latency of OBC hardware. We integrate this model into a discrete-event simulation framework to evaluate end-to-end system latency, coupling stochastic OBC constraints with Over-the-Air (OTA) communication delays. We analyze the performance of fleets comprised of three common OBC ratings: 10 kW, 22 kW, and 43 kW. Contrary to the intuition that higher power ratings yield superior agility, our results demonstrate that high-capacity chargers may exhibit lower success rates in fast frequency markets due to insufficient ramp-rate-to-capacity ratios. Furthermore, we demonstrate that the frequency of mode switching events (switching between charging and discharging) is a dominant factor in performance degradation due to hardware hysteresis. These findings underscore that the efficacy of V2G applications requires precise EV-level control logic rather than relying solely on fleet-level optimization. Finally, the proposed models are evaluated against the PJM interconnection's composite score methodology. The results demonstrate high accuracy, suggesting the proposed framework can serve as a preliminary, EV-specific V2G assessment tool for market operators.

With the development of electric vehicles (EVs), accurate prediction of the remaining driving range (RDR) is a crucial issue. However, predicting RDR for EVs still poses significant challenges, particularly under complex traffic scenarios and personalized driving behaviors, where existing methodologies struggle to meet adaptability requirements. This study proposes a novel RDR prediction method for EVs that incorporates personalized driving behavior and emphasizes the influence of future route information on prediction performance. Real-world driving data, encompassing driving patterns, traffic conditions, and road characteristics, serve as the foundation for constructing a precise driving behavior model with Hidden Markov Model (HMM) and Fuzzy Markov Model (FMM). Furthermore, an energy consumption model that integrates the physical model with machine learning techniques is constructed to accurately predict energy consumption rates under future driving cycles. By coupling this consumption rate with battery load in an equivalent circuit model (ECM), the RDR is accurately predicted. To validate the performance of the proposed method, it is compared with baseline models under various driving scenarios. Experimental results demonstrate the accuracy and adaptability of the model, with a relative error within 10% in practical driving validation. This methodology offers insights into energy efficiency optimization and intelligent decision-making for EVs in complex traffic environments.

Data-driven state-of-health (SOH) prediction is increasingly critical for the effective management of lithium-ion batteries; however, challenges remain in practical applications. Traditional methods that rely on a single health indicator often fail to capture the complexity and multi-dimensional nature of battery performance changes. To address these limitations, this paper presents a novel Transformer-based approach for accurate SOH prediction. The correlation between various measured and computed features extracted from battery charge/discharge curves and their impact on battery performance degradation are investigated using Pearson correlation coefficients. Three strongly correlated features are identified as multiple input variables for the Transformer framework. The effectiveness of this Transformer-based SOH prediction method is demonstrated using public datasets, revealing that predictions for internal resistance and capacity closely align with actual values, with most RMSE values falling below 0.01. Furthermore, validation with an additional laboratory dataset confirms the accuracy and adaptability of our proposed approach, highlighting its potential to enhance SOH prediction in real-world applications.