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Vehicular ad hoc networks (VANETs) facilitate communication among vehicles and possess designated infrastructure nodes to improve road safety and traffic flow. As the number of vehicles increases, the limited bandwidth of the wireless channel used for vehicle-to-vehicle (V2V) communication can become congested, leading to packets being dropped or delayed. VANET congestion control techniques attempt to address this by adjusting different transmission parameters, including the data rate, message rate, and transmission power. In this paper, we propose a decentralized congestion control algorithm where each factor adjusts the data rate (bitrate) used to transmit its wireless packet congestion based on the current load on the channel. The channel load is estimated independently by each vehicle using the measured channel busy ratio (CBR). The simulation results demonstrate that the proposed approach outperforms existing data rate-based algorithms, in terms of both packet reception and overall channel load.

In urban Vehicular Ad Hoc Network (VANET) environments, buildings play a crucial role as they can act as obstacles that attenuate the transmission signal between vehicles. Such obstacles lead to multipath effects, which could substantially impact data transmission due to fading. Therefore, quantifying the impact of buildings on transmission quality is a key parameter of the propagation model, especially in critical scenarios involving emergency vehicles where reliable communication is of utmost importance. In this research, we propose a supervised learning approach based on Artificial Neural Networks (ANNs) to develop a predictive model capable of estimating the level of signal degradation, represented by the Bit Error Rate (BER), based on the obstacles perceived by moving emergency vehicles. By establishing a relationship between the level of signal degradation and the encountered obstacles, our proposed mechanism enables efficient routing decisions being made prior to the transmission process. Consequently, data packets are routed through paths that exhibit the lowest BER. To collect the training data, we employed Network Simulator 3 (NS-3) in conjunction with the Simulation of Urban MObility (SUMO) simulator, leveraging real-world data sourced from the OpenStreetMap (OSM) geographic database. OSM enabled us to gather geospatial data related to the Two-Dimensional (2D) geometric structure of buildings, which served as input for our Artificial Neural Network (ANN). To determine the most suitable algorithm for our ANN, we assessed the accuracy of ten learning algorithms in MATLAB, utilizing five key metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Correlation Coefficient (R), and Maximum Prediction Error (MaxPE). For each algorithm, we conducted fifteen iterations based on ten hidden neurons and gauged its accuracy against the aforementioned metrics. Our analysis highlighted that the ANN underpinned by the Conjugate Gradient With Powell/Beale Restarts (CGB) learning algorithm exhibited superior performance in terms of MSE, RMSE, MAE, R, and MaxPE compared to other algorithms such as Levenberg–Marquardt (LM), Bayesian Regularization (BR), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Fletcher–Powell Conjugate Gradient (CGF), Polak–Ribiére Conjugate Gradient (CGP), One-Step Secant (OSS), and Variable Learning Rate Backpropagation (GDX). The BER prediction by our ANN incorporates the TWO-RAY Ground (TRG) propagation model, an adjustable parameter within NS-3. When subjected to 300 new samples, the trained ANN’s simulation outcomes illustrated its capability to learn, generalize, and successfully predict the BER for a new data instance. Overall, our research contributes to enhancing the performance and reliability of communication in urban VANET environments, especially in critical scenarios involving emergency vehicles, by leveraging supervised learning and artificial neural networks to predict signal degradation levels and optimize routing decisions accordingly.

Currently, the outbreak of COVID-19 pandemic has caused catastrophic effect on every aspect of our lives, globally. The entire human race of all countries and regions has suffered devastating losses. With its high infectiousness and mortality rate, it is of great significance to carry out effective precautions and prevention of COVID-19. Specifically, the transportation system has been confirmed as one of the crucial spreading routes. Hence, enhancing healthcare monitoring and infection tracking for high-mobility transportation system is infeasible for pandemic control. Meanwhile, due to the promising advantages in the emerging intelligent transportation system (ITS), vehicular ad hoc networks (VANETs) is able to collect and process relevant vehicular data for improving the driving experience and road safety, which provide a way for non-contact automatic healthcare monitoring. Furthermore, the proliferating cloud computing and blockchain techniques enable sufficient processing and storing capabilities, along with decentralized remote auditing towards heterogenous vehicular data. In this case, the automated infection tracking for pandemic control could be achieved accordingly. For the above consideration, in this paper we develop a practical homomorphic authentication scheme for cloud-assisted VANETs, where the healthcare monitoring for all involving passengers is provided. Notably, the integrated cloud-assisted VANET infrastructure is utilized, where the hybrid medical data acquisition module is attached. In this way, timely, non-contact measurement on all passengers’ physical status can be remotely done by vehicular cloud (VC), which could also drastically improve the efficiency and guarantee safety. Vulnerabilities of the employed dedicated-short-range-communication (DSRC) technique could be properly addressed with the applied homomorphic encryption design. Additionally, the decentralized blockchain-based vehicle recording mechanism is cooperatively performed by VC and edge units. Infection tracking on specific vehicle and individual can be offered in this way. Each signature sequence is collaboratively maintained and verified by the current roadside unit (RSU) and its neighbor RSUs. The security analysis demonstrates that the proposed scheme is secure against major attacks, while the performance comparison with the state-of-the-arts relevant methods are presented for efficiency discussion.

Intelligent Transportation Systems have gained significant attention among Internet of Things applications due to its specific features and its high capability to promote the innovation of the automotive industries. As part of smart cities, smart mobility initiatives offer new opportunities for intelligent transportation systems to maximize the utilization of the time-sensitive data that are streaming out of different sensory transport resources to support “newcasting” instead of “forecasting” technique. As a result, efficient information dissemination has become the new production factor, notably in terms of mitigating traffic congestion, maximizing bandwidth utilization, and reducing transmission power consumption over the network. Cloud Computing and its counterparts have been established as relatively stable environments for proving a wide number of tackled solutions. However, the limitations of network bandwidth as well as the rapid expansion into the data transfer rate are still the bottlenecks of vehicular networks. The main contribution of this study is to improve Vehicular Ad hoc Network performance in real-time using a geographically distributed computing architecture based on fog technology. This architecture presents a set of novel techniques from different points of view. These novelties include, (i) a flexible registration methodology for improving the navigation process among mobile vehicles; (ii) a generic distributed mechanism to adjust the communication range and the network connectivity; and (iii) a new mathematical model to ensure transmission reliability through establishing powerful communication channels between the vehicular entities and fog layer. The effectiveness of the proposed architecture is evaluated using several performance metrics such as throughput, delay time, and jitter. The experimental results reveal that the worthiness of the proposed architecture to meet the quality of service requirements is more than other state-of-the-art techniques in the literature review.

VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology.

Network coding is a data processing technique in which the flow of digital data is optimized in a network by transmitting a composite of two or more messages to make the network more robust. Network coding has been used in traditional and emerging wireless networks to overcome the communications issues of these networks. It also plays an important role in the area of vehicular ad-hoc networks (VANETs) to meet the challenges like high mobility, rapidly changing topology, and intermittent connectivity. VANETs consist of network of vehicles in which they communicate with each other to ensure road safety, free flow of traffic, and ease of journey for the passengers. It is now considered to be the most valuable concept for improving efficiency and safety of future transportation. However, this field has a lot of challenges to deal with. This paper presents a comprehensive survey of network coding schemes in VANETs. We have classified different applications like content distribution, multimedia streaming, cooperative downloading, data dissemination, and summarized other key areas of VANETs in which network coding schemes are implemented. This research work will provide a clear understanding to the readers about how network coding is implemented in these schemes in VANETs to improve performance, reduce delay, and make the network more efficient.

With the rapid increase in the internet technologies, Vehicular Ad hoc Networks (VANETs) are identified as a crucial primitive for the vehicular communication in which the moving vehicles are treated as nodes to form a mobile network. To ameliorate the efficiency and traffic security of the communication, a VANET can wirelessly circulate the traffic information and status to the participating vehicles (nodes). Before deploying a VANET, a security and privacy mechanism must be implemented to assure the secure communication. Due to this issue, a number of conditional privacy-preserving authentication schemes are proposed in the literature to guarantee the mutual authentication and privacy protection. However, most of these schemes use the Diffie–Hellman (DH) problems to secure the communication. Note that, these DH-type problems can be solved in polynomial-time in the presence of new modern technologies like quantum computers. Therefore, to remove these difficulties, we motivated to attempt a non-DH type conditional privacy-preserving authentication scheme which can resist the quantum computers. In this paper, we developed the first lattice-based conditional privacy-preserving authentication (LB-CPPA) protocol for VANETs. A random oracle model is used to analyze the security of proposed protocol. The security of our LB-CPPA scheme is based on the complexity of lattice problems. By security analysis, we show that our proposal endorses the message integrity and authentication as well as the privacy preservation at the same time. A security comparison of our claim is also done. Further, we analyze the performance of the proposed scheme and compare it with the DH-type schemes.

Vehicular ad hoc network usually operates in various challenging situations like frequent topology changes, high vehicular mobility and the wide range of communication networks. Due to this it is very hard to maintain a higher data rate and also to achieve low latency during data communication. To overcome these problems, given the dynamic natures of all the vehicles in a given network in the proposed routing method, we have defined two fundamental parameters to determine the forwarding vehicle. The first parameter, which we developed, we call it “Channel quality factor (CQF)” or ‘Z’. The other parameter known as “Communication expiration time” or ‘T’ together with CQF is used in the present method to determine the forwarding vehicle. Fuzzy logic is also used to optimize various Quality of Service matrices. This proposed routing method involves two main parts; one is for forwarding Vehicle selection in the road based on the fuzzy logic. The second one is Road selection at the Road Junction to select the right path to reach the signal to the destination vehicle. The simulation results show that our proposed method performs well compare to other well-known protocols (MoZo, BRAVE, OFAODV) in terms of the average end to end delay, packet delivery ratio and control packet overhead, given any number of vehicles in a set of streets. While we are comparing with VEFR protocol, our proposed method shows higher performance in terms of average E2E delay and control packet overhead. However, it is interesting to see that VEFR gives ∼ 5% better result than our proposed method when the number of vehicles in the streets are lower. But in the limit, when the number of vehicles reaches close to ∼ 1900 the difference between the proposed method and method in VEFR goes to zero. At last we compare our proposed method with junction based two V2I protocols. In every cases, it shows better result even though we change the speed of the vehicles, beacon interval, channel data rate and transmission region.

The seamless data delivery is essential in VANET for application such as autonomous vehicle, intelligent traffic management and for the road safety and emergency applications. The incorporation of named data networking (NDN) with VANET, intended to frame intelligent traffic flow and seamless data delivery. Such integration of vehicular ad hoc networks (VANET) with NDN is termed as vehicular named data networks (VNDN). Because of the continuous node/vehicle mobility, it is a tedious process to build constant and consistent communication between vehicles. With that concern, for enhancing the performance of VNDN and solving the issues such as frequent cluster formation on heavy loaded data transmissions, position-based adaptive clustering model (PACM) is developed. The major intention of PACM is to form clusters based on trajectory. Besides, PACM performs efficient data caching by collecting significant data from vehicles to establish consistent data communication with all nodes in the network. Efficient data caching is done with the elected cluster heads among the framed clusters based on its positions and mobility models. For handling the vehicles at higher mobility speed, mutual data caching process is also designed that makes vehicles to perform on-demand data gathering from cluster heads. Further, the model is simulated and the obtained results are compared with the existing models based on the metrics such as packet delivery ratio, mean delay, cache hit rate and mean hop distance. The comparative analysis shows that the proposed model outperforms the available techniques.