Road Side Research Articles

Software Escalation Prediction Based on Deep Learning in the Cognitive Internet of Vehicles

In the Cognitive Internet of Vehicles (CIoV), vehicles, road side units (RSU) and other key nodes have been equipped with more and more software to support intelligent transportation system (ITS), vehicle automatic control and intelligent road information services. Additionally, technological innovation forces the software in the CIoV to update and upgrade in time. However, escalation is critical to the safety, stability, and maintenance cost of transportation systems. It can be assumed that when the intelligent services supporting CIoV can realize self-perception and escalation, the cognitive ability and coordination ability of the entire CIoV will be greatly improved. To address this, we first propose a deep learning-based method for Software Escalation Prediction (SEP) in CIoV. Specifically, the pretraining mechanism of transformers in the field of natural language processing is combined with software upgrade-related events to dynamically model software sequence activities. To capture the event association in the software activities, we use graph modeling software’s state log and utilize a graph neural network (GNN) to learn the complex life activity rule of software. Finally, the above characteristics are deeply integrated. The proposed method has a 6%–8% improvement over the RoBERTa methods.

Formally Robust and Safe Trajectory Planning and Tracking for Autonomous Vehicles

In this paper, a safe trajectory planning and tracking algorithm for autonomous vehicles is proposed. Specially, the safety problem considers the geometric constraints including the obstacle avoiding and the road side constraints, and the non-convex input constraints defined from the sideslip angle of the wheels and input boundedness. Control barrier function (CBF) is adopted to deal with the state and input constraints and generate nominal trajectory. For this purpose, the nominal dynamics of the autonomous vehicle is defined as the virtual dynamics, from which the CBF safety certificates are derived. By constructing appropriate feedback control, the tracking error of the actual trajectory can be bounded into a tube, which guarantees the geometric safety of the actual vehicle. Two safety certificates, the bearing and the distance safety certificates are derived for multiple-obstacle avoidance. In order to deal with the non-convex input constraints, a safe braking maneuver is carefully considered. The feasible initial velocity set for safe braking is proposed as a part of state constraints. The feasibility and the safety of the overall system is proved. The algorithm to synthesis the CBF certificates for multiple-obstacle avoidance, and the input constraints is proposed. Simulation results from an autonomous vehicle including disturbances demonstrate the feasibility of the algorithm. The software implementation of the proposed algorithm was developed in C++ intended for real-world testing.

Steinbeck, Race, and Route 66 in The Grapes of Wrath

Abstract Despite its advocacy for tenant farmers, laborers, Okies, and other poor migrants across the United States, John Steinbeck’s novel The Grapes of Wrath seemingly ignores the Black Americans among them and effectively erases the history of African Americans even as it elevates Route 66 and the overlooked and forgotten trials and tribulations of White, rural, faming families who lived along it. This article surveys the state of scholarship on Steinbeck’s relation to racism and racial issues, and sets the novel’s depiction of African Americans in historical and well as critical contexts. In its bulk, the article examines one crucial historical fact that Steinbeck omits from his epic tale of the Joad family emigrating from Oklahoma: the many all-Black towns situated along Route 66 and its “tributary side roads” featured in the novel. Ultimately, readers of Steinbeck learn about the contexts that Steinbeck ignores but that nonetheless provide an opportunity to highlight with the fame he’s brought to the so-called “Mother Road.”

Value Determinants, Mentality and Identity of the Bosnian-Herzegovinian Village at the Beginning of the 20th Century according to Ivan Softa’s Literary Works

Ivan Softa (Sopta) (1906–1945) was a Croatian writer from Bosnia and Herzegovina. In his realistic novels Dani jada i glada (Days of Misery and Hunger) (1937) and Nemirni mir (Restless Peace) (1940), as well as in his other literary achievements, he describes the life of the Herzegovinian village at the time when the First World War was about to start. It was an ambience dominated by bare stone, hot sun, sharp north wind called bora, with somewhat scarce and sparse land that made an easy life impossible. These resources that abound in this Western Balkans region also shaped the specific (Dinaric) mentality of the people of that time: hard as the stone on which they grew up, aggressive and easily flammable like the sun that they were exposed to and unrestrained like the wind, the whipping of which they mercilessly suffered from. But it happened that even from that fiery temper some kind of an original mountaineering-like honesty and confidence, empathy and readiness for self-sacrifice had emerged. Personal martyrdom turning into self-destruction – something that seems to be completely inconceivable to modern man – is embodied in the female characters, whereas emotional rawness and hardness are more characteristic of the author’s male characters. The tragic circumstances of war and famine caused emigration to regions where there was bread. These tragic circumstances resulted in the tragic fate of the characters who left the Herzegovinian environment and, like many others, fell apart on the side roads of life.

Intelligent Caching for Mobile Video Streaming in Vehicular Networks with Deep Reinforcement Learning

Caching-enabled multi-access edge computing (MEC) has attracted wide attention to support future intelligent vehicular networks, especially for delivering high-definition videos in the internet of vehicles with limited backhaul capacity. However, factors such as the constrained storage capacity of MEC servers and the mobility of vehicles pose challenges to caching reliability, particularly for supporting multiple bitrate video streaming caching while achieving considerable quality of experience (QoE). Motivated by the above challenges, in this paper, we propose an intelligent caching strategy that takes into account vehicle mobility, time-varying content popularity, and backhaul capability to improve the QoE of vehicle users effectively. First, based on the mobile video mean opinion score (MV-MOS), we designed an average download percentage (ADP) weighted QoE evaluation model. Then, the video content caching problem is formulated as a Markov decision process (MDP) to maximize the ADP weighted MV-MOS. Owing to the prior knowledge of video content popularity and channel state information that may not be available at the road side unit in practical scenarios, we propose a deep reinforcement learning (DRL)-based caching strategy to solve the problem while achieving a maximum ADP weighted MV-MOS. To accelerate its convergence speed, we further integrate the prioritized experience replay, dueling, and double deep Q-network technologies, which improve the performance of DRL algorithm. Numerical results demonstrate that the proposed DRL-based caching strategy significantly improves QoE, and achieves better video delivery reliability compared to existing non-learning approaches.

SDN-enabled vehicular networks: Theory and practice within platooning applications

With the recent developments of communication technologies surrounding vehicles, we witness the simultaneous availability of multiple onboard communication interfaces on vehicles. While most of the current interfaces already include Bluetooth, WiFi, and LTE, they are augmented further by IEEE 802.11p and the 5G interfaces, which will serve for safety, maintenance, and infotainment applications. However, dynamic management of interfaces depending on application becomes a significant issue that can be best addressed by Software Defined Networking (SDN) capabilities. While SDN-based vehicular networks have been promoted previously, none of these works deal with practical challenges. In this paper, we propose and develop a practical framework that realizes SDN-based vehicular networks for a wide range of applications. Through this framework, we demonstrate a truck platooning application as a use case in which the two truck platoons strive to merge and establish connectivity. The route from source vehicle (i.e., Platoon Leader A) to destination (i.e., Platoon Leader B) is computed with the help of the SDN Controller to transmit the Beacon Safety Messages through Road Side Units (RSUs) at the MAC layer without relying on IP for proper platooning operations. The results show the efficiency of the SDN-based approach compared to the traditional routing approaches.

Neighborhood Fast-food availability and Fast-food consumption among Households

Background: Obesity is currently a major public health issue in all over the world. Food is important for survival. Consumption of fast foods has become almost a global phenomenon. India’s fast-food industry is expanding at the rate of 40% every year. India ranks 10th in the fast-food per capita spending with 2.1% of expenditure in annual total spending1. Aim: The aim of the study was to identify the level of knowledge, attitude and practice regarding Neighborhood fast food availability and fast-food consumption among households in selected urban area, in Puducherry”, to find out the correlation between knowledge and attitude regarding neighbourhood fast food availability and fast-food consumption among households and to find association of level of knowledge and attitude regarding neighbourhood fast food availability and fast-food consumption among households with selected demographic variables. Subjects and Methods: A descriptive cross sectional research was conducted among 50households in Lawspet area Puducherry, by convenience sampling technique, quantitative approach. Data was collected by using self-structured questionnaire developed by the investigator. Interview method was used to collect socio demographic data and to assess the level of knowledge, attitude and practice regarding Neighborhood fast food availability and fast-food consumption. Results: The study result shows that 50 households out of 34(68%) had moderate knowledge, 35(70%) had positive attitude, 21(42%) of them prefer favourite fast-food place is road side shop/restaurants, 2(40%) used to spend on fast food daily a average amount of above Rs.80 and 31(62%) were aware about fast foods through advertisement, social media and by the newspaper. Conclusion: The study concluded that households in urban area prefer fast food that there was a moderate knowledge, had positive attitude, eat fast food few times a month, favourite fast-food place prefer at road side shop/restaurants, Everyone spend Rs. 80/- on fast food daily aware about fast foods consumptions and In effect but prefer more fast food leads obesity. Community health nurse play effective role in teaching public about the effect of fast-food consumption and avoidance of fast foods. Researcher created awareness to to avoid fast foods and fast-food consumption to all households by distributing pamphlets.

An Environmentally Sustainable Software-Defined Networking Data Dissemination Method for Mixed Traffic Flows in RSU Clouds with Energy Restriction.

The connected multi road side unit (RSU) environment can be envisioned as the RSU cloud. In this paper, the Software-Defined Networking (SDN) framework is utilized to dynamically reconfigure the RSU clouds for the mixed traffic flows with energy restrictions, which are composed of five categories of vehicles with distinctive communication demands. An environmentally sustainable SDN data dissemination method for safer and greener transportation solutions is thus proposed, aiming to achieve the lowest overall SDN cloud delay with the least working hosts and minimum energy consumption, which is a mixed integer linear programming problem (MILP). To solve the problem, Joint optimization algorithms with Finite resources (JF) in three hyperparameters versions, JF (DW = 0.3, HW = 0.7), JF (DW = 0.5, HW = 0.5) and JF (DW = 0.7, HW = 0.3), were proposed, which are in contrast with single-objective optimization algorithms, the Host Optimization (H) algorithm, and the Delay optimization (D) algorithm. Results show that JF (DW = 0.3, HW = 0.7) and JF (DW = 0.5, HW = 0.5), when compared with the D algorithm, usually had slightly larger cloud delays, but fewer working hosts and energy consumptions, which has vital significance for enhancing energy efficiency and environmental protection, and shows the superiority of JFs over the D algorithm. Meanwhile, the H algorithm had the least working hosts and fewest energy consumptions under the same conditions, but completely ignored the explosive surge of delay, which is not desirable for most cases of the SDN RSU cloud. Further analysis showed that the larger the network topology of the SDN cloud, the harder it was to find a feasible network configuration. Therefore, when designing an environmentally sustainable SDN RSU cloud for the greener future mobility of intelligent transportation systems, its size should be limited or partitioned into a relatively small topology.

Tire and rubber particles in the environment—A case study from a hot arid region

Materials made of rubber are a source of polymers released into the environment, where tire abrasions are a major contributor. In many hot, arid environs, whole tire losses are more common than in moderate climates, and lead to the accumulation of additional tire material on road sides, which over time can be the source of secondary micro-tires. Other rubber containing material from cars such as mud-flaps and floor mats has been seen as an appreciable additional source of micro-rubber. Due to the lack of precipitation, it is expected that modes of micro-tire relocation are more limited in arid regions than in more moderate climates. This 2-year study examines the rubber/tire content from 34 sampling sites within the region of Al Ain, Eastern region of Abu Dhabi Emirate, United Arab Emirates. The samplings include road dust, soil along roadsides, storm water run-off samples and samples collected in Wadis (creek beds that seasonally carry water) and one artificial lake. The mean average number of micro-tires in road dust was found to be 44.4 ± 40.6 micro-tires/g. In soil samples alongside the roads, it was 22.8 ± 42.1 micro-tires/g. The mean average size of micro-tires for all 34 sampling sites was found to be 106.9 ± 98.5 µm. It could be seen that rubber/tire materials desiccate over time during the high temperatures of the Emirati summer, become hard, brittle and break into smaller pieces, resulting in secondary micro-tire particles. The study of run-offs showed that tire particles are swept into storm drains during larger, very infrequently occurring rain events. However, the predominant mode of micro-tire transport was found to be on road by physical contact with cars, off-road by a combination of physical contact with moving objects and air movement. Samples taken from an artificial lake and three Wadis within the confines of Al Ain have shown only few micro-tire particles, so that the dispersion of micro-tires is not as pervasive as had been expected prior to this study.

Artificial intelligence‐empoweredvision‐based self driver assistance system for internet of autonomous vehicles

AbstractArtificial intelligence (AI) and edge computing have truly advanced in vehicular networks encouraging assessment of real‐time traffic conditions using kinetic information of autonomous vehicles with the help of road side units (RSUs). However, regardless of numerous improvements in sensor fusion technologies the existing vision/LIDAR‐based systems have found severe difficulties during perception on roads. In addition, the inter‐vehicular communications are hampered due to inefficient RSU placement techniques causing high‐latency issues during transmission of messages. Therefore, this article presents an AI‐driven vision‐based self driver assistance system (VSDAS) comprising a joint RSU deployment mechanism that utilizes enhanced memetic architecture‐based optimal RSU placement (MARP) method and an object detection model that implements an improved Haar‐cascade object detection algorithm for speedy identification of object. We have designed two varieties of genetic algorithm (GA) to solve optimal placement problem of RSUs: genetic architecture‐based with random restart hill climbing (GARRH) and the other is MARP for efficient placement of RSUs. After our experimental results, we see that the MARP algorithm shows best possible RSU locations over different generations achieving significantly better fitness scores than the GAHRC and GA ascribing to its local search process. In addition, Haar‐cascade achieves better mean average precision than local binary pattern and histogram of oriented gradients by selecting key frames. The experimental outcomes of our model reveals that the proposed enhanced memetic algorithm reduces the transmission delay to a greater extent. Additionally, the outcomes of our investigations on two public datasets (KITTI and Panasonic) showed that our improved algorithm clearly enhances the object detection performance.

Automatic measurement of building setbacks and streetscape widths and their spatial variability along streets and in plots: integration of streetscape skeletons and plot geometry

Measuring building setbacks and heights along streets is important for evaluating the variability of streetscape skeletons, the 3D spaces of streets defined by the arrangement of surrounding buildings. Its evaluation requires computing the streetscape width, defined as the front road width of a building plus the setbacks of both sides of the front roads, the building heights and the ratio of the streetscape width to the building height, known as the streetscape width-to-height ratio. However, measuring building setbacks and streetscape widths with geographical information systems (GIS) workstations remains theoretically and technically challenging because conventional methods fail to define the ambiguous boundaries of streetscape skeletons. To address this issue, we developed a new method for defining and measuring building setbacks and streetscape widths automatically and in a consistent way. A new basic spatial unit was also developed for evaluating the variability in building setbacks, heights, streetscape widths and streetscape width-to-height ratios not only in plots focusing on classical urban morphologies but also along streets focusing on a pedestrian perspective. The method contributes practically to the measurement and evaluation of streetscape skeletons in a bottom-up way at fine intervals without the need for setting predetermined spatial units. KEY POLICY HIGHLIGHTS Measuring building setbacks and heights along roads is important for evaluating the variability of streetscape skeletons. However, measuring these in an actual complex urban space without vagueness on a GIS workstation is difficult. We have developed a new method for defining and measuring building setbacks and streetscape widths automatically. A new basic spatial unit for evaluating streetscape skeletons is proposed focusing on the plot geometry and a pedestrian perspective. The method contributes to the evaluation of streetscapes in a bottom-up way at fine intervals without setting predetermined spatial units.

Exploiting Non-Cooperative Game Against Cache Pollution Attack in Vehicular Content Centric Network

Vehicular Content Centric Network (VCCN) has been proposed to address the issues of user mobility and sporadic connectivity in Vehicular Ad hoc Network (VANET). The in-network caching of VCCN can indeed improve the performance of content distribution in terms of cache hit and delivery latency by making each node store frequently accessed data. However, data caching unfortunately suffers from Cache Pollution Attack (CPA) that sends out fabricated requests to pollute the data cache. To prevent the degradation of network performance caused by such an attack, we propose a detection and defense scheme for CPA by adopting game theory. We model the attack scenario into a non-cooperative game model. First, we prove the non-existence of pure strategy Nash Equilibrium (NE) in the attacker and defender game, and propose a normal form game model with complete information and an extensive Bayesian form game model with incomplete information. Under the guidance of NE, we propose a punishment strategy to prevent the conspiracy attack and make the attackers behave normally. Moreover, we propose a cooperative detection scheme to give the Road Side Unit (RSU) the final decision on CPA based on the direct and indirect suspicious lists which are generated by each node according to the observed traffic pattern. Simulation evaluations demonstrate that our scheme outperforms state-of-the-art schemes in terms of cache hit, detecting ratio, and cache accuracy for detecting and defending CPA.

A Novel Multifaceted Trust Management Framework for Vehicular Networks

Maintaining interconnectivity between dynamic vehicular nodes and ensuring trust in vehicular networking is still a challenging issue. It is crucial for the vehicular environments to maintain a stable interconnectivity between the vehicular nodes and further to prevent the vehicular nodes from broadcasting fake messages and simultaneously protect the vehicles against tracking attacks. An efficient Trust Management System (TMS) is proposed in this paper that will ensure trustworthiness and stable interconnectivity between vehicular entities to assure road safety and reliable communication. The proposed approach incorporates a novel timestamp mechanism and block chain concepts to ensure the steady inter-connectivity between the dynamic vehicular nodes. Further, the framework leverages block chain concepts to verify the correctness of the events stored in the Road Side Units (RSUs). It also proposes a versatile hybrid trust model that uses innovative direct and recommended trust evaluation techniques to compute trust between the vehicular entities. Moreover, the framework integrates a threading mechanism to schedule message execution in direct trust evaluation, and clustering techniques to group similar messages in indirect trust evaluation. Various experimental and comparative analyses with other related studies are carried out in a simulated environment to evaluate the performance of the proposed hybrid trust model. The findings show that the proposed trust model produces an accuracy of 92% in identifying malicious nodes.

P2SF-IoV: A Privacy-Preservation-Based Secured Framework for Internet of Vehicles

With the development of Internet of Vehicles (IoV), the integration of Internet of Things (IoT) and manual vehicles becomes inevitable in Intelligent Transportation Systems (ITS). In ITS, the IoVs communicate wirelessly with other IoVs, Road Side Unit (RSU) and Cloud Server using an open channel Internet. The openness of above participating entities and their communication technologies brings challenges such as security vulnerabilities, data privacy, transparency, verifiability, scalability, and data integrity among participating entities. To address these challenges, we present a Privacy-Preserving based Secured Framework for Internet of Vehicles (P2SF-IoV). P2SF-IoV integrates blockchain and deep learning technique to overcome aforementioned challenges, and works on two modules. First, a blockchain module is developed to securely transmit the data between IoV-RSU-Cloud. Second, a deep learning module is designed that uses the data from blockchain module to detect intrusion and its performance is assessed using two network datasets IoT-Botnet and ToN-IoT. In contrast with other peer privacy-preserving intrusion detection strategies, the P2SF-IoV approach is compared, and the experimental results reveal that in both blockchain and non-blockchain based solutions, the proposed P2SF-IoV framework outperforms.

A Novel Framework for Road Side Unit Location Optimization for Origin-Destination Demand Estimation

This study deals with the problem of road side unit (RSU) location optimization for origin-destination (OD) demand estimation. With the point-to-point measurement provided by RSUs in connected vehicle environment, the errors of OD demand estimation come from two sources: 1) the lack of enough path flow information; and 2) the vehicle-to-RSU (V2R) communication delay. However, increasing the amount of path flow information collected by RSUs results in the increase of V2R communication delay encountered by each collected data packet. Moreover, it is difficult to find a global optimal solution by formulating the problem as a single objective program. To address the investigated problem, this study proposes a novel framework consisting of solving a bi-objective RSU location optimization problem and an OD demand estimation problem. This RSU location optimization problem is formulated as a bi-objective nonlinear binary integer program to balance the maximization of the amount of path flow information and the minimization of V2R communication delay. The OD demand estimation problem is formulated as a least square estimator to identify the RSU location scheme with the smallest OD demand estimation error, among the Pareto optimal solutions to the bi-objective program. An efficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -constraint method is developed to generate the Pareto optimal solutions. The numerical example demonstrates that the proposed framework achieves 6.95 lower root-mean-square error of OD demand estimation, compared with the baseline framework.

RAVEN: Resource Allocation Using Reinforcement Learning for Vehicular Edge Computing Networks

Vehicular Edge Computing (VEC) enables vehicles to offload tasks to the road side units (RSUs) to improve the task performance and user experience. However, blindly offloading the vehicle’s tasks might not be an efficient solution. Such a scheme may overload the resources available at the RSU, increase the number of requests rejected, and decrease the system utility by engaging more servers than required. This letter proposes a Markov Decision Process based Reinforcement Learning (RL) method to allocate resources at the RSU. The RL algorithm aims to train the RSU in optimizing its resource allocation by varying the resource allocation scheme according to the total task demands generated by the traffic. The results demonstrate the effectiveness of the proposed method.

Eco-toxicological impact of atmospheric lead on road side perennial plants and ragi seedlings in petriplate culture under controlled conditions

Higher accumulation of lead in perennial old plant leaves were observed. Young plants eithershowed noresidual lead or the amount of lead if present was insignificant. No residual lead was observed in smallherbs present on both the sides of the road. Significant decrease in total chlorophyll and phaeophytincontent was recorded in plant leaves situated on the roadsides of the ghat area when compared to plantscollected from deep inside the forest area far away from freeways / highways. Insignificant decrease or nochange in carotenoid content was recorded in plant leaves collected from the plants available on the roadsidesof the ghat area. Change in leaf colour and heavy surface deposition was noted in exposed leaves of theplants. Residual lead level was untraceable in control plant leaves. Initial increment in photosynthetic ratewas observed at LC00 (MAC) of lead nitrate in petriplate culture compared to control valuefollowed bydepletion of photosynthetic rate in higher lead nitrate concentrations in petriplate culture. Tissue respirationand photosynthesis rate significantly declined in lead exposed seedlings in petriplate cultureexperimentsdepleting the productivity of the exposed seedlings when compared to control seedlings. The GPP valuedecreased in all exposed seedlings indicating the impact of the toxicant. Notable but insignificant amountof residual lead was noted in lead nitrate exposed 6day old seedlings in petriplate culture. The observedobservation in all parameters studied relates to residual lead accumulation in the seedlings which might have impacted different physiological activity in the exposed ragi seedlings.

Post-assessment of the eco-environmental impact of highway construction– A case study of Changbai Mountain Ring Road

The eco-environmental impact of highway construction is a long-term and cumulative process. Most of the existing studies pay attention to the environmental impact assessment technology of highway construction period and completion acceptance period, which can not fully explain the eco-environmental impact of highway operation period. At the same time, most of the previous studies have a single index and less consideration of the impact on species. Aiming at the highway located in the forest ecosystem in the middle temperate zone, considering the four aspects of species, land use, landscape pattern and ecological function, this paper establishes the post-evaluation index system of the eco-environmental impact of highway construction projects. This paper puts forward the quantitative assessment technology and spatio-temporal scale identification method of the long-term evolution of the impact of highway construction on the ecological environment, and uses the analytic hierarchy process to construct a comprehensive post-assessment system of ecological environmental impact of highway construction projects. Relying on the Changbai Mountain Ring Road, a 10-year comprehensive ecological impact assessment was carried out. Mammals tend to accumulate within 200 m of the road, and highway corridors attract bird activities; the animal mortality coverage rates of amphibians and reptiles are 100%, mammals 66.7%, and birds 50.9%. The utilization rate of bridges and culverts is 88% and 42%, and the total utilization rate is 49.3%. The landscape is changing to the dominant type of coniferous and broad-leaved mixed forest, with lower evenness, less diversity and higher degree of natural connection, but the ability of the ecological environment to resist external changes is reduced. The closer the landscape fragmentation is to the highway, the more serious it is, and the lower the diversity is. The time and space scale of post-assessment of eco-environmental impact of highway construction project is in the range of 4–7 years and 200-300 m on the road side, and with the increase of operation time, the change of ecosystem along the line decreases year by year and tends to be stable gradually. The research results can provide a method of reference for the follow-up study of highway ecological environment in mid-temperate forest ecological areas.

An Efficient MAC Protocol for Blockchain-Enabled Patient Monitoring in a Vehicular Network

Blockchain is an emerging computing platform that provides recording and tracking facilities to substantially increase the security issues in healthcare systems. The evolution of wireless body area networks requires the continuous monitoring of the health parameters of traveling patients while traveling on road. The health parameter data of each patient are sent to the Road Side Units (RSUs) for generating the blocks by computing the required hash functions. A major challenge in such a network is to efficiently exchange the data blocks between mining RSUs and vehicles using a medium access protocol with a reduced number of collisions. The medium access problem becomes more challenging due to the vehicle mobility, high vehicle density and the varying nature of the data generated by the vehicles. In this work, a TDMA-based MAC protocol to meet an Adaptive Patients Data traffic for Vehicular Network (TAPDVN) is proposed. TAPDVN is specifically designed for patients in a vehicular network by considering the frequent entry and exit of vehicles in a mining node’s coverage area. It allows mining nodes to adjust time slots according to the sensitive patient’s data and allows the maximum number of patient vehicular nodes by considering their sensitivity to send their data in a session to compute their hash values accordingly. Simulation results verify that the proposed scheme accommodates the maximum number of high-risk patient data and improves bandwidth utilization by 20%.

Site assessment and evaluation of the structural damages after the flood disaster in the Western Black Sea Basin on August 11, 2021

On August 11, 2021, one of the most destructive flood disasters occurred in the Western Black Sea Basin of Turkey. The flood resulted in the death of 76 individuals, 30,000 people being affected by the disaster. A maximum precipitation depth of 400 mm/day was recorded at one station, indicating a return period exceeding 500 years for the rainfall event. During a two-day site visit immediately following the flooding event, damages to infrastructures, water structures, bridges, retaining walls, roads, and private houses were observed in the Bozkurt and Ayancık regions. Based on the observations, the flood wave propagated through the initial meandering river bed and floodplain, exceeding the channelized river bed capacity. Due to the massive sediment transport and drifting of trees, several bridges have been blocked and overflown where the basements of the structures in these regions were flooded. The enormous flood flow triggered extensive scouring on bridge piers, building foundations, and retaining walls, eventually causing the walls and bridges to collapse. The collapse of structures blocked the waterway and amplified the backwater effect when combined with the sediment transport. The total collapse of the retaining walls in some sections of the stream caused accelerated scouring in the foundations of the nearby buildings. Damages were also observed on the side roads along the river beds. This paper evaluated the driving mechanism of damages caused by flood flow from hydrological, structural, and geotechnical perspectives. Based on these observations and assessments, recommendations on engineering design guidelines for structures close to the floodplain, such as bridges, retaining walls, and side roads, were elaborated. Emphasis was placed on the flood-resistant design of these structures to develop a comprehensive approach for flood risk management.