Search Results (50)

Named Data Networking (NDN) represents a promising Information-Centric Networking architecture that addresses limitations of traditional host-centric Internet protocols by emphasizing content names rather than host addresses for communication. While NDN offers advantages in content distribution, mobility support, and built-in security features, its stateful forwarding plane introduces significant vulnerabilities, particularly Interest Flooding Attacks (IFAs). These IFA attacks exploit the Pending Interest Table (PIT) by injecting malicious interest packets for non-existent or unsatisfiable content, leading to resource exhaustion and denial-of-service attacks against legitimate users. This survey examines research advances in IFA detection and mitigation from 2013 to 2024, analyzing seven relevant published detection and mitigation strategies to provide current insights into this evolving security challenge. We establish a taxonomy of attack variants, including Fake Interest, Unsatisfiable Interest, Interest Loop, and Collusive models, while examining their operational characteristics and network performance impacts. Our analysis categorizes defense mechanisms into five primary approaches: rate-limiting strategies, PIT management techniques, machine learning and artificial intelligence methods, reputation-based systems, and blockchain-enabled solutions. These approaches are evaluated for their effectiveness, computational requirements, and deployment feasibility. The survey extends to domain-specific implementations in resource-constrained environments, examining adaptations for Internet of Things deployments, wireless sensor networks, and high-mobility vehicular scenarios. Five critical research directions are proposed: adaptive defense mechanisms against sophisticated attackers, privacy-preserving detection techniques, real-time optimization for edge computing environments, standardized evaluation frameworks, and hybrid approaches combining multiple mitigation strategies. Full article

The dynamic characteristics of super-large-diameter twin tunnels under train vibration loads have become a critical issue affecting not only the engineering safety of their own tunnels but also adjacent tunnels. A numerical model of super-large-diameter (D = 15.2 m) twin tunnels was established by the discrete element method (DEM) to analyze the static and dynamic responses of adjacent tunnel structures and surroundings under train-induced vibrations. Three parameters were considered: internal walls, absolute and relative spacing, and water pressure. The results indicate that internal walls in super-large twin tunnels can significantly reduce the static and dynamic responses in both the structures and surroundings of the adjacent tunnel. The vehicular lane board (wall2) plays a determinative role, followed by the smoke exhaust board (wall1), while the left and right partition walls (wall3 and wall4) exhibit the least effectiveness. The static–dynamic responses of the liners and surroundings of adjacent tunnels in super-large twin tunnels are significantly greater than those in smaller twin tunnels when the absolute spacing is identical. Moreover, the significant differences in displacement and velocity between the liners and surroundings can lead to cracks, leakage, or even instability. Appropriate water pressure (149 kPa) can effectively mitigate dynamic responses in adjacent tunnel structures and surroundings. The dynamic characteristics of super-large-diameter twin tunnels differ markedly from those of small-diameter twin tunnels, with internal walls, twin tunnel spacing, and water pressure all influencing their static and dynamic behaviors. This study provides theoretical guidance for the design and operation of super-large-diameter twin tunnels. Full article

Evaluating the impact on roadside environments of NH₃ from vehicle emissions is important for protecting the ecosystem from air pollution by fine particulate matter and nitrogen deposition. This study used passive samplers to measure NH₃ and NO_X at multiple points near a major road to observe the distribution of these gases in the area. The impact of NH₃ emitted from vehicles on a major road on the environmental concentration of NH₃ at different distances from the roadside was found to be similar to that of NO_X and NO₂. The concentration of NH₃ rapidly decreased due to dilution and diffusion within approximately 50 m of the road, and after 100 m the concentration remained almost the same or decreased slowly. Furthermore, CO₂ observations taken in the same period along the roadside and in the background yielded a vehicular emission factor of 4–50 mg/km for NH₃, which is comparable with previous research. This emission factor level contributes 4–11 ppb to the NH₃ concentrations in roadside air through the dilution and diffusion process. A correlation was found between the emission factors of NH₃ and NO_X that was different from the trade-off relationship seen when single-vehicle exhaust is measured. Full article

Air pollution, particularly from vehicular emissions, has emerged as a critical environmental health concern, contributing to a global estimated 7 million premature deaths annually. Diesel exhaust, a major component of urban air pollution, contains fine particulate matter and gases that evade respiratory filtration, penetrating deep into the lungs and triggering oxidative stress, inflammation, and immune dysregulation. Epidemiological and in vitro studies have linked diesel exhaust exposure to respiratory diseases such as asthma, chronic obstructive pulmonary disease, pulmonary fibrosis, and lung cancer, with immunological mechanisms playing a central role. Diesel exhaust particles induce oxidative stress, impair macrophage phagocytosis, and skew T-cell polarization toward pro-inflammatory Th2 and Th17 responses, exacerbating chronic inflammation and tissue damage. Despite these insights, significant gaps remain in understanding the precise immunomodulatory pathways and long-term systemic effects of diesel exhaust exposure. While animal models and in vitro studies provide valuable data, they often fail to capture the complexity of human exposure and immune responses. Further research is needed to elucidate the mechanisms underlying diesel exhaust-induced immune dysregulation, particularly in vulnerable populations with pre-existing respiratory conditions. This review focuses on summarizing the current knowledge and identifying gaps that are essential for developing targeted interventions and policies to mitigate the adverse health impacts of diesel exhaust and improve respiratory health outcomes globally. Full article

Urban stormwater may contain pollutants from different traffic vehicular sources including brake and tire wear, exhaust emissions, and atmospheric deposition. In this research, we took advantage of COVID-19 restrictions to evaluate the effects of historically low vehicular circulation on stormwater quality (metal concentrations and mass loads) generated from an urban watershed in Denver (Colorado). The analysis was performed at different hydrograph stages, i.e., first flush, peak flow, and recession stages during and after the imposition of the COVID-19 restrictions. Metal concentrations were compared with the maximum contaminant levels (MCLs) defined by the US Environmental Protection Agency (EPA) for drinking water as an indicator of water quality degradation. The results indicate that the Fe and Mn levels were constantly above the MCLs in stormwater, while then level of Pb occasionally surpassed the limits. Additionally, the highest pollutant mass loads generally occurred during peak flow conditions. Importantly, there was a clear effect of COVID-19 restrictions, suggesting that more stormwater pollution occurred after the restrictions were lifted, as a result of more vehicles circulating. Considering local climate, the mass loads of Fe, Mn, and Pb (the pollutants of concern) were estimated to be 0.4489, 0.0772, and 0.00032 MT/year, respectively, which are similar to loads reported in the literature for cities with similar climates and development levels. Full article

Particulate matter (PM) is a major component of ambient air pollution. PM exposure is linked to numerous adverse health effects, including chronic lung diseases. Air quality guidelines designed to regulate levels of ambient PM are currently based on the mass concentration of different particle sizes, independent of their origin and chemical composition. The objective of this study was to assess the relative hazardous effects of carbonaceous particles (soot), ammonium nitrate, ammonium sulfate, and copper oxide (CuO), which are standard components of ambient air, reflecting contributions from primary combustion, secondary inorganic constituents, and non-exhaust emissions (NEE) from vehicular traffic. Human epithelial cells representing bronchial (BEAS-2B) and alveolar locations (H441 and A549) in the airways, human lung fibroblasts (HFL-1), and rat precision-cut lung slices (PCLS) were exposed in submerged cultures to different concentrations of particles for 5–72 h. Following exposure, cell viability, metabolic activity, reactive oxygen species (ROS) formation, and inflammatory responses were analyzed. CuO and, to a lesser extent, soot reduced cell viability in a dose-dependent manner, increased ROS formation, and induced inflammatory responses. Ammonium nitrate and ammonium sulfate did not elicit any significant cytotoxic responses but induced immunomodulatory alterations at very high concentrations. Our findings demonstrate that secondary inorganic components of PM have a lower hazard cytotoxicity compared with combustion-derived and indicative NEE components, and alveolar epithelial cells are more sensitive to PM exposure. This information should help to inform which sources of PM to target and feed into improved, targeted air quality guidelines. Full article

Airborne particulate matter (PM) is of great concern in the modern-day atmosphere owing to its association with a variety of health impacts, such as respiratory and cardiovascular diseases. Of the various size fractions of PM, it is the finer fractions that are most harmful to health, in particular ultrafine particles (PM_0.1; UFPs), with an aerodynamic diameter ≤ 100 nm. The smaller size fractions, of ≤2.5 µm (PM_2.5; fine particles) and ≤0.1 µm (PM_0.1; ultrafine particles), have been shown to have numerous linkages to negative health effects; however, their collection/sampling remains challenging. This review paper employed a comprehensive literature review methodology; 200 studies were evaluated based on the rigor of their methodologies, including the validity of experimental designs, data collection methods, and statistical analyses. Studies with robust methodologies were prioritised for inclusion. This review paper critically assesses the health risks associated with fine and ultrafine particles, highlighting vehicular emissions as the most significant source of particulate-related health effects. While coal combustion, diesel exhaust, household wood combustors’ emissions, and Earth’s crust dust also pose health risks, evidence suggests that exposure to particulates from vehicular emissions has the greatest impact on human health due to their widespread distribution and contribution to air pollution-related diseases. This article comprehensively examines current sampling technologies, specifically focusing on the collection and sampling of ultrafine particles (UFP) from ambient air to facilitate toxicological and physiochemical characterisation efforts. This article discusses diverse approaches to collect fine and ultrafine particulates, along with experimental endeavours to assess ultrafine particle concentrations across various microenvironments. Following meticulous evaluation of sampling techniques, high-volume air samplers such as the Chem Vol Model 2400 High Volume Cascade Impactor and low-volume samplers like the Personal Cascade Impactor Sampler (PCIS) emerge as effective methods. These techniques offer advantages in particle size fractionation, collection efficiency, and adaptability to different sampling environments, positioning them as valuable tools for precise characterisation of particulate matter in air quality research and environmental monitoring. Full article

As assisted driving technology advances and vehicle entertainment systems rapidly develop, future vehicles will become mobile cinemas, where passengers can use various multimedia applications in the car. In recent years, the progress in multimedia technology has given rise to immersive video experiences. In addition to conventional 2D videos, 360° videos are gaining popularity, and volumetric videos, which can offer users a better immersive experience, have been discussed. However, these applications place high demands on network capabilities, leading to a dependence on next-generation wireless communication technology to address network bottlenecks. Therefore, this study provides an exhaustive overview of the latest advancements in video streaming over vehicular networks. First, we introduce related work and background knowledge, and provide an overview of recent developments in vehicular networking and video types. Next, we detail various video processing technologies, including the latest released standards. Detailed explanations are provided for network strategies and wireless communication technologies that can optimize video transmission in vehicular networks, paying special attention to the relevant literature regarding the current development of 6G technology that is applied to vehicle communication. Finally, we proposed future research directions and challenges. Building upon the technologies introduced in this paper and considering diverse applications, we suggest a suitable vehicular network architecture for next-generation video transmission. Full article

Total OH reactivity, an index utilized to evaluate the overall effect of atmospheric reactive species on hydroxyl radicals, has been assessed over the past half century, particularly in ambient air. The direct measurement of OH reactivity for vehicular sources has also been conducted, further enhancing our understanding of chemical compounds and processes in source emissions. However, the current summary on OH reactivity dominantly focuses on ambient, and the review of OH reactivity measurements and characteristics for vehicular sources was lacking. Herein, we comprehensively reviewed and compared the measurement techniques, values of total OH reactivity, reactive chemical species, and missing OH reactivity for ambient air and vehicular sources involving exhaust and evaporation. The OH reactivity values for ambient air are comparable to those for evaporative emission (around 0–10² s⁻¹), whereas they are all lower by 2–3 orders of magnitude than exhaust emission. In areas dominated by anthropogenic emissions, inorganic reactivity dominates the OH reactivity, while in biogenic-dominated areas, organic reactivity is the main contributor. For vehicular sources, inorganic reactivity dominates the calculated OH reactivity for exhaust emissions, while volatile organic compound reactivity (especially alkene reactivity) can almost explain all the calculated OH reactivity for evaporative emissions. The missing reactivity for ambient air and vehicular emission might derive from unmeasured, even unknown, organic species. We finally discussed possible new directions for future studies of total OH reactivity. Full article

Particulate matter (PM) pollution poses a severe threat to the environment and health worldwide. This study aimed to evaluate the mass concentration, physicochemical characteristics, and emission sources of aerodynamic diameters of ≤1 µm (PM₁) within an urban sprawl situated in the eastern Indo-Gangetic basin over three years (2017–2019). The study encompassed the monitoring of PM₁ using an ambient PM₁ sampler; physicochemical characteristics were determined through scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and inductively coupled plasma-optical emission spectrometry (ICP-OES). Possible emission sources were analysed through principal component analysis (PCA) and enrichment factor (EF) analyses. The results showed that the PM₁ concentrations were consistently high throughout the research period, even exceeding the national standards for PM_2.5 and PM₁₀, especially during the post-monsoon period. Significant seasonal fluctuations were confirmed by the elemental and inorganic ion analyses, highlighting the dominance of elements like Al, Ca, Fe, K, and Mg and inorganic ions like NH₄⁺, SO₄²⁻, and NO₃⁻. Vehicular exhaust and non-exhaust (47%), sea salt and biomass burning (26%), and industrial activities (10.3%) are the dominant sources of PM₁. Therefore, the findings are thought-provoking and could inspire policymakers to formulate reduction policies in India. Full article

Industrialization, explosive population growth, anthropogenic activities, and vehicular exhaust deteriorate ambient air quality across the world. The current study aims at assessing the impacts on ambient air quality patterns and their co-relations in one of the world’s most polluted cities, i.e., Lahore, Pakistan, during a strict, moderate, and post-COVID-19 period of 28 months (March 2020–June 2022). The purpose of this study is to monitor and analyze the relationship between criteria air pollutants (SO₂, particulate matter (PM 10 and 2.5), CO, O₃, and NO₂) through a Haz-Scanner 6000 and mobile van (ambient air quality monitoring station) over nine towns in Lahore. The results showed significantly lower concentrations of pollutants during strict lockdown which increased during the moderate and post-COVID-19 lockdown periods. The post-COVID-19 period illustrates a significant increase in the concentrations of SO₂, PM₁₀, PM_2.5, CO, O₃, and NO₂, in a range of 100%, 270%, 500%, 300%, 70%, and 115%, respectively. Major peaks (pollution concentration) for PM₁₀, PM_2.5, NO₂, and SO₂ were found during the winter season. Multi-linear regression models show a significant correlation between PM with NO₂ and SO₂. The ratio of increase in the PM concentration with the increasing NO₂ concentration is nearly 2.5 times higher than SO₂. A significant positive correlation between a mobile van and Haz-Scanner was observed for CO and NO₂ data as well as ground-based observation and satellite data of SO₂, NO₂, and CO. During the strict COVID-19 lockdowns, the reduction in the vehicular and industrial exhaust significantly improved the air quality of nine towns in Lahore. This research sets the ground for further research on the quantification of total emissions and the impacts of vehicular/industrial emissions on human health. Full article

In order to illustrate pollution characterization, source apportionment, and risk assessment of VOCs in Beijing, Baoding, and Shanghai, field observations of CO, NO, NO₂, O_3, and volatile organic compounds (VOCs) were conducted in 2019. Concentrations of VOCs were the highest in Beijing (105.4 ± 52.1 ppb), followed by Baoding (97.1 ± 47.5 ppb) and Shanghai (91.1 ± 41.3 ppb). Concentrations of VOCs were the highest in winter (120.3 ± 61.5 ppb) among the three seasons tested, followed by summer (98.1 + 50.8 ppb) and autumn (75.5 + 33.4 ppb). Alkenes were the most reactive VOC species in all cities, accounting for 56.0%, 53.7%, and 39.4% of ozone formation potential in Beijing, Baoding, and Shanghai, respectively. Alkenes and aromatics were the reactive species, particularly ethene, propene, 1,3,5-trimethylbenzene, and m/p-xylene. Vehicular exhaust was the principal source in all three cities, accounting for 27.0%, 30.4%, and 23.3% of VOCs in Beijing, Baoding, and Shanghai, respectively. Industrial manufacturing was the second largest source in Baoding (23.6%) and Shanghai (21.3%), and solvent utilization was the second largest source in Beijing (25.1%). The empirical kinetic modeling approach showed that O₃ formation was limited by both VOCs and nitric oxides at Fangshan (the suburban site) and by VOCs at Xuhui (the urban site). Acrolein was the only substance with an average hazard quotient greater than 1, indicating significant non-carcinogenic risk. In Beijing, 1,2-dibromoethane had an R-value of 1.1 × 10⁻⁴ and posed a definite carcinogenic risk. Full article

Autonomous vehicles in highway driving scenarios are expected to become a reality in the next few years. Decision-making and motion planning algorithms, which allow autonomous vehicles to predict and tackle unpredictable road traffic situations, play a crucial role. Indeed, finding the optimal driving decision in all the different driving scenarios is a challenging task due to the large and complex variability of highway traffic scenarios. In this context, the aim of this work is to design an effective hybrid two-layer path planning architecture that, by exploiting the powerful tools offered by the emerging Deep Reinforcement Learning (DRL) in combination with model-based approaches, lets the autonomous vehicles properly behave in different highway traffic conditions and, accordingly, to determine the lateral and longitudinal control commands. Specifically, the DRL-based high-level planner is responsible for training the vehicle to choose tactical behaviors according to the surrounding environment, while the low-level control converts these choices into the lateral and longitudinal vehicle control actions to be imposed through an optimization problem based on Nonlinear Model Predictive Control (NMPC) approach, thus enforcing continuous constraints. The effectiveness of the proposed hierarchical architecture is hence evaluated via an integrated vehicular platform that combines the MATLAB environment with the SUMO (Simulation of Urban MObility) traffic simulator. The exhaustive simulation analysis, carried out on different non-trivial highway traffic scenarios, confirms the capability of the proposed strategy in driving the autonomous vehicles in different traffic scenarios. Full article

Emission control regulations have been essential in reducing vehicular exhaust emissions. However, the contribution of exhaust and non-exhaust emissions to ambient particulate matter (PM) has not yet been accurately quantified due to the lack of standardized sampling and measurement methods to set regulations. The identified sources and the source profiles generated have not been comparable as none of the emission data collection techniques and the receptor models applied in the literature have produced a standard or reference method to simultaneously identify and quantify the non-exhaust emission sources. This study utilized and thoroughly characterized PM samples including 32 major and trace elements from a mixed fleet in a mountain highway tunnel atmosphere in Bolu, Türkiye. This work proposed a two-stage, simple, and robust method based on road dust enrichment factor (EF) and elemental carbon (EC) tracer methods (EFECT) for the identification and prediction of the exhaust (exh), and non-exhaust (n-exh) emissions in PM. The indicated method revealed that road dust resuspension emissions are the most significant contributor to the concentrations of crustal elements. This method was used successfully to determine the real-world elemental contributions of road dust resuspension (rdrs), emissions (em), exhaust (exh), and non-exhaust (n-exh) emission sources to the elemental concentrations in PM samples. This study provided significant insights into generating actual source profiles, source-specific emission factors, and the source apportionment results for vehicular emission sources worldwide. Considering this, PM data of any particle size fraction (PM₁₀, PM_10-2.5, and PM_2.5, for example) can be used as input for the EFECT, provided that the data include the analytical results of elemental carbon in both the atmospheric PM and road dust samples having similar PM sizes. Full article

A challenging problem in millimeter wave (mmWave) communications for the fifth generation of cellular communications and beyond (5G/B5G) is the beam selection problem. This is due to severe attenuation and penetration losses that are inherent in the mmWave band. Thus, the beam selection problem for mmWave links in a vehicular scenario can be solved as an exhaustive search among all candidate beam pairs. However, this approach cannot be assuredly completed within short contact times. On the other hand, machine learning (ML) has the potential to significantly advance 5G/B5G technology, as evidenced by the growing complexity of constructing cellular networks. In this work, we perform a comparative study of using different ML methods to solve the beam selection problem. We use a common dataset for this scenario found in the literature. We increase the accuracy of these results by approximately 30%. Moreover, we extend the given dataset by producing additional synthetic data. We apply ensemble learning techniques and obtain results with about 94% accuracy. The novelty of our work lies in the fact that we improve the existing dataset by adding more synthetic data and by designing a custom ensemble learning method for the problem at hand. Full article