VANET Network Research Articles

Analisa Kinerja Protokol Routing Destination Sequenced Distance Vector Di Jaringan VANET

Vehicular network is a network where vehicles communicate via an ad-hoc network system that runs based on certain ad-hoc routing protocols. A vehicle ad-hoc network consists of two communication parts known as vehicle-to-vehicle communication and vehicle-to-infrastructure communication. Even though the roads have become very congested with many vehicles, controlling vehicle movement has become an urgent need. Automatic traffic control, critical moment notifications, post-accident notifications, correct route selection become very important and these can be implemented using vehicle ad-hoc network systems. For good communication between vehicles, vehicle ad-hoc networks use several protocols. One of the problems faced by VANET is routing. Routing in VANETs can perform challenging tasks due to high mobility, network topology interference, and path selection processes. DSDV is one of the routing used in VANET and simulations were carried out with NS2 to obtain DSDV routing performance in the VANET network based on quality of service with scenarios of 25 and 33 nodes. DSDV routing was chosen because it has the best performance in its class. From the simulation results, the end to end delay for 25 nodes is 22.22 and 33 nodes is 24.07, the packet drop obtained for 25 nodes is 0.85, and 33 nodes is 0.80. Furthermore, the throughput value obtained in the simulation for 25 nodes is 274.19 and 33 nodes 351.12, then the packet delivery ratio for 25 nodes is 2990, and 33 nodes is 4888

Investigation Performance Analysis and Evaluation of VANET Routing Protocol on Urban Scenario Simulation: A Case Study of Melaka

Vehicles on the road create a VANET network using IEEE 802.11P for wireless data exchange. However, VANET is challenging since the characteristics of VANET such as high mobility and frequent topology making it harder to design an effective routing protocol. The needs of an efficient performance routing protocol are crucial since it is to ensure a better network Quality of Service (QoS). In this paper, the evaluation of the IEEE 802.11P performance is simulated in real-world scenario using the Melaka City roadmap. In an urban setting, the impact of vehicle density on the performance of VANET routing protocols including OLSR, AODV, and DSDV are examined. Reviewing the effectiveness of the current VANET routing protocols is the goal of this research since it is crucial to know which routing has high performance under specific circumstances. Quantitative measurements including Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), and end-to-end delay are evaluated using the Network Simulator NS-3 and SUMO. Findings from the investigation show that while proactive routing protocols like OLSR and DSDV perform better in terms of end-to-end delay, AODV performs exceptionally well in terms of PDR and PLR.

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

SummaryAn attack on VANET known as a Sybil attack involves the attacker claiming or stealing multiple identities with the intent of causing havoc on the network's operation by disseminating false information. To protect the VANET network from Sybil attack, a variety of methods have been proposed. In order to compensate for the Sybil attack on VANETs, a rumor riding with salp swarm optimization (SSO) based Routing protocol is developed in this research work. The primary goal of this investigation is to discover how VANETs are vulnerable to the Sybil attack in order to improve their security. Rumor riding will be used to identify the Sybil attack in the suggested strategy. VANETs are protected by the Blowfish Algorithm, which uses SSO to select the best key for each network. The collective intelligence and dynamic adaptation of SALP swarm optimization algorithm enable efficient identification of rumor sources in complex networks. The algorithm is scalable, flexible, and adept at reducing false positives and negatives, making it suitable for large‐scale monitoring. Its parallel processing capabilities and robustness ensure rapid and reliable detection. Using the NS2 platform, the proposed method is compared to existing benchmark in terms of delay, packet delivery ratio (PDR), packet loss, throughput, and attack detection rate. The proposed SSO has reduced the packet loss by nearly 5% and delay by nearly 7%, when compared with existing techniques. The delivery ratio of packet for proposed model is increased up to 8% of PDR, when compared with ACO, PSO, and FA. According to this evidence, it is clear that the proposed SSO outperformed existing approaches in a variety of performance metrics.

Proposed approach for securing VANET by detection and avoidance of Sybil attack

Our world has gone fully mobile, and with advanced technologies, communication in this dynamic era has been possible with MANET and VANET. Along with the facilities extended to our pockets, the security of our data has also been a big concern. The VANET network has numerous obstacles to maintaining information security. One aspect of the suggested study is finding the Sybil node and ensuring measures for avoiding the same in VANET. The proposed model uses improved RC4A data encryption to protect the network from Sybil attacks.

DYNAMIC KEY GENERATION AND DISTRIBUTION COMPARISON USING MACHINE LEARNING INTEGRATED NODE AUTHENTICATION ROUTING PROTOCOL FOR IMPROVING QOS IN VANET

VANET, or Vehicular Adhoc Network, is widely used in mobile networks that use self-driving, navigation, entertainment, and other emergency applications. It is commonly adopted in the transport system to facilitate communication between the vehicles and crucial points. It is used to control traffic, improve routing efficiency, and better functioning of the transportation systems and controls. It enables communication between the mobile nodes through several base stations. The effectiveness of the VANET is improved through Artificial intelligence techniques. With the wide adoption of VANET networks for better communication, the network is subjected to various security vulnerabilities. This paper proposes a key generation distribution and comparison-based user authentication routing protocol to improve the security of the VANET. The communication metrics of the networks are monitored, and the data obtained are processed through the RNN algorithm. The nodes are authenticated using bilinear mapping, which involves registration, authentication, and verification. The nodes are identified during registration, a private key is assigned for temporary authentication, and the nodes are verified. NS3 is used to simulate the proposed model, where it obtained throughput, PDR, energy efficiency, and packet loss of 99.8, 99.3, 99, and 0.02%, respectively.

VANETs MAC Protocol Without RSU Assistance

The aim of MAC protocol research without RSU is to solve some of the challenges and problems that exist in VANETs without RSU support. For example, Rsus cannot be deployed in certain regions or scenarios due to various reasons (such as cost, lack of infrastructure, etc.). In this case, the MAC protocol without RSU assistance provides a solution that does not rely on RSU for communication. In addition, in traditional VANETs, the RSU acts as an important relay node, helping vehicles communicate with each other. However, without a sufficiently dense RSU deployment, network coverage will be limited. Through the MAC protocol without RSU assistance, vehicles can communicate directly, expanding the coverage of the network. Through the MAC protocol without RSU assistance, vehicles can communicate directly, expanding the coverage of the network. Improve the reliability and throughput of VANETs networks: Due to the mobility of vehicles and the uncertainty of the communication environment, problems such as message collisions and data transmission delays are common in VANETs. By designing an effective MAC protocol without RSU assistance, message collision can be reduced, system throughput can be improved, and network reliability can be enhanced. Finally, in some application scenarios, such as vehicle safety and traffic management, real-time communication is very important. Without the support of RSU, traditional VANETs may suffer from large communication delays. By studying the MAC protocol without RSU assistance, the communication delay can be minimized to meet the needs of real-time communication.

Using Adaptive Zero-Knowledge Authentication Protocol in VANET Automotive Network

One of the most important components of intelligent transportation systems (ITS) is the automotive self-organizing VANET network (vehicular ad hoc network). Its nodes are vehicles with specialized onboard units (OBU) installed on them. Such a network can be subject to various attacks. To reduce the effectiveness of a number of attacks on the VANET, it is advisable to use authentication protocols. Well-known authentication protocols support a security policy with full trust in roadside unit (RSU) base stations. The disadvantage of these authentication protocols is the ability of the RSU to track the route of the vehicle. This leads to a violation of the privacy and anonymity of the vehicle’s owner. To eliminate this drawback, the article proposes an adaptive authentication protocol. An advantage of this protocol is the provision of high imitation resistance without using symmetric and asymmetric ciphers. This result has been achieved by using a zero-knowledge authentication protocol. A scheme for adapting the protocol parameters depending on the intensity of the user’s traffic has been developed for the proposed protocol. The scientific novelty of this solution is to reduce time spent on authentication without changing the protocol execution algorithm by reducing the number of modular exponentiation operations when calculating true and “distorted” digests of the prover and verifying the correctness of responses, as well as by reducing the number of responses. Authentication, as before, takes place in one round without changing the bit depth of the modulus used in the protocol. To evaluate the effectiveness of the adaptive authentication protocol, the VANET model was implemented using NS-2. The obtained research results have shown that the adaptation of the authentication protocol in conditions of increased density of vehicles on the road makes it possible to increase the volume of data exchange between OBU and RSU by reducing the level of confidentiality. In addition, a mechanism for verifying the authority of the vehicle’s owner for provided services has been developed. As a result of the implementation of this mechanism, vehicle registration sites (VRS) calculate the public key of the vehicle without using encryption and provide necessary services to the owner.

Improving AODV Performance by Software Defined Networking Using NS3

Abstract Nowadays, vehicular networks attract car manufacturers, network researchers, and governments as well. They represent one of the building blocks, for the intelligent transportation systems. Our task is to study the employment of SDN advantages to facilitate and improve the performance of vehicular ad-hoc networks. The goal of the research is to evaluate AODV routing protocol performance improved with SDN technology applied on VANET network in specified environment of a city. We have evaluated three parameters: packet delivery ratio, end-to-end delay and throughput using SUMO and NS3 simulators. The implemented evaluation protocol shows the importance of the adopted approach.

Development of pipeline feature engineering for building an AutoML service

The large–scale implementation of artificial intelligence approaches in applied fields has a number of limitations, one of which is the availability of research competencies, knowledge of data analysis methods, mathematical statistics and machine learning. Automatic machine learning is designed to simplify the methodology of ML application development. Within the framework of this study, a new approach to the construction of pipeline feature engineering for AutoML service is presented, based on the sequential expansion of the feature space and the use of autoencoders to reduce the dimension of input features and reconstruct the final output features. The results of the presented approach are shown by the example of VANET network traffic data when solving the problem of classifying attacks on nodes. The data set was obtained as a result of simulating the real traffic of a certain segment of the VANET network in the OMNET++ environment and subsequent aggregation of data on network flows by means of CICFlowmeter-V4.0. Experiments have shown that machine learning models on the source data have an accuracy of 2% lower on average, which indicates the effectiveness of using the proposed Feature Engineering approach. The highest classification accuracy was demonstrated by Pipeline using the Multi–layered Model autoencoder and the XGBoost classification model – 91.2%. Thus, the presented Feature Engineering approach can be used to build the most effective feature space and improve the quality of machine learning models.

WS-OLSR: Multipoint Relay Selection in VANET Networks using a Wingsuit Flying Search Algorithm

The routing protocol is considered the backbone of network communication. However, mobility and bandwidth availability make optimizing broadcast message flooding a problem in an Optimized Link State Routing (OLSR)-based mobile wireless network. The selection of Multi-Point Relays (MPRs) has lately been proposed as a potential approach that has the added benefit of eliminating duplicate re-transmissions in VANET networks. Wingsuit Flying Search (WFS) is one of the swarm intelligent metaheuristic algorithms, it enables one to find the minimum number of MPR. In this study, a novel methodology based on (WFS) is called WS-OLSR (Wingsuit Search-OLSR). The (WS-OLSR) is investigated to enhance the existing MPR-based solution, arguing that considering a cost function as a further decision measure will effectively compute minimum MPR nodes that give the maximum coverage area possible. The enhanced MPR selection powered by (WFS) algorithm leads to decreasing MPR count required to cover 95% of mobile nodes, increasing throughput , and decreasing topology control which mitigates broadcasting storm phenomenon in VANETs.

TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks

On account of the highly dynamic topology of vehicular networks, network congestion and energy utilization are greatly increased, which directly affects the performance of VANETs. So, managing traffic and reducing energy consumption in the network becomes a challenging task in such huge mobility-based VANET networks. Thus, in this paper a new traffic and cluster-based network method is introduced, namely, Traffic-Aware Clustering based Routing Protocol (TACRP). The main aim of the approach is to improve traffic management in the network as well as to reduce energy consumption in it. In the constructed network, a Traffic Management Unit (TMU) is introduced to control the entire network traffic with the help of RSUs. Vehicles with similar speed and direction are grouped into a cluster to increase the network stability and help to reduce the energy consumption of the network. The clustering model provides principles associated with vehicles leaving the clusters, joining the clusters, cluster updates and inter-cluster communication, which makes the network more stable and reliable. For instance, in the proposed work the CH selection is based on centralization, weight, distance, and energy calculation. Such network settings facilitate successfully clustering of vehicles on the road. Simulation experimental analysis showed that the proposed TACRP routing protocol achieved better results in terms of energy efficiency, throughput, packet delivery ratio, and end to end delay of the network when compared with earlier methods, such as ECHS and NRHCS.

Traffic flow control using multi-agent reinforcement learning

One of the technologies based on information technology used today is the VANET network used for inter-road communication. Today, many developed countries use this technology to optimize travel times, queue lengths, number of vehicle stops, and overall traffic network efficiency. In this research, we investigate the critical and necessary factors to increase the quality of VANET networks. This paper focuses on increasing the quality of service using multi-agent learning methods. The innovation of this study is using artificial intelligence to improve the network’s quality of service, which uses a mechanism and algorithm to find the optimal behavior of agents in the VANET. The result indicates that the proposed method is more optimal in the evaluation criteria of packet delivery ratio (PDR), transaction success rate, phase duration, and queue length than the previous ones. According to the evaluation criteria, TSR 6.342%, PDR 9.105%, QL 7.143%, and PD 6.783% are more efficient than previous works.

Modularized hypergraph clustering scheme model for stable VANET

SummaryVANET transforms vehicles into a social environment, allowing for maximum security and cooperative data transmission. Each vehicle is assigned a cluster, which transfers data to the cluster head (CH). The efficiency of a VANET network is determined by two factors: efficient vehicle groupings and optimal CH selection. This article proposes a novel modularized hypergraph clustering scheme in which the VANET is represented as a hypergraph network clustered using tensor trace maximization with the modularity matrix as the feed. CH is chosen from each cluster based on four vehicle parameters: vehicle speed about neighbors, consensus trust score, link lifetime, and connectivity level. For the final CH selection, all parameters are converted to eigenvalues. The fuzzy decision‐making scheme selects the best CH for all vehicles in that cluster based on a set of four parameters. The proposed algorithm for selecting the stable CH for improved network performance has also been validated and significantly improved using state‐of‐the‐art schemes.

Ideal congestion control model to enhance the quality of service of VANET network

Vehicular Adhoc Technology (VANET) is a mobile ad-hoc network where each vehicle combines with a communication link. Well fascinated and proper connectivity between the vehicle nodes are very essential to make the communication efficient. But high mobility and rapid change in locations of the vehicles often hampers its performance. Also often disconnection and overloading issues inside the network create an extra burden to vanet. So it is very important to make the network free from these factors so that smooth communication can happen between the network. In this paper, we have proposed our algorithm to control the network from congestion occurrence. Which ultimately enhanced the performance and QoS of the network

A hybrid optimization with ensemble learning to ensure VANET network stability based on performance analysis

High vehicle mobility, changing vehicle density and dynamic inter-vehicle spacing are all important issues in the VANET environment. As a result, a better routing protocol improves VANET overall performance by permitting frequent service availability. Therefore, an ensemble-based machine-learning technique is used to forecast VANET mobility. Effective routing based on a hybrid metaheuristic algorithm combined with Ensemble Learning yields significantly improved results. Based on information collected from the Road Side Unit (RSU) or the Base Station, a hybrid metaheuristic (Seagull optimization and Artificial Fish Swarm Optimization) method is used to estimate (BS). The suggested approach incorporates an ensemble machine learning and hybrid metaheuristic method to reduce the latency. The current model's execution is calculated using a variety of Machine Learning techniques, including SVM, Nave Bayes, ANN, and Decision Tree. As a result, the performance of machine learning algorithms may be studied and used to achieve the best results. Comparative analysis between the proposed method (HFSA-VANET) and (CRSM-VANET was done on different performance parameters like throughput, delay, drop, network lifetime, and energy consumption to assess system performance on two factors Speed and Nodes. The HFSA-VANET method shows an overall drop in the delay of 33% and a decrease in the energy consumption of 81% and an increase of 8% in the throughput as compared with the CRSM-VANET method at 80 node. The proposed method that is HFSA-VANET has been implemented in the MATLAB and NS2 environment.

Performance Evaluation of Vehicle Ad hoc Networks Under Wi-Fi-6 Technology

Vehicle ad hoc networks are considered mobile networks where the nodes are mobile objects and can change their positions within an environment over time. These objects can be connected at any time according to a predefined strategy. Simulating this kind of network needs high attention to many details. Moreover, the literature lacks works that describe the requirements of simulating such networks. Therefore, this work tries to describe the requirements of simulating vehicle networks (VANETs). Moreover, the goal is to determine what is needed to simulate vehicle networks in terms of the distribution of vehicles, the movement patterns, and the routing protocols used. The simulation results show interesting facts about the VANET networks and the best strategies to minimize the consumption of network resources. Finally, this work considers two communication technologies among network nodes; Wi-Fi 5 and Wi-Fi 6.

Analysis and design of secure VANET by detection and avoidance of blackhole attack

Transmission of the data and information over Ad-Hoc network and its security is a very big challenge. VANET Networks have evolved for helping mobile services, but still the users are facing lot of challenges to preserve the security of information. The proposed research includes the concepts of detecting the Blackhole node and counters it in the VANET network for preserving the network strength as well as performance using a new protocol. The proposed protocol encrypts and decrypts the information using RC4A and simultaneously protects the network from Blackhole Attack.

Phishing and Sybil Enhanced Behavior Processing and Footprint Algorithms in Vehicular Ad Hoc Network

Communication between vehicles is the core of VANETs, which are built on notions of mobile ad hoc networks (MANETs). Each car on the network is now seen as a mobile network node. All participating cars become wireless routers or nodes, depending on your viewpoint. VANET creates a huge network with a long-range by connecting all cars within range to a fixed unit. VANET assists with traffic regulation, communication between vehicles and the sharing of road data. There is a possibility that the VANET network could be compromised by identity and information concerns, resulting in data delays or theft. Attacks like Sybil and phishing are possible because of this network's weakness. Due to the two recent strikes, infrastructure and human lives may be in jeopardy. There are two novel algorithms developed by the authors to tackle Sybil and Phishing assaults on VANET networks: Phishing and Sybil Enhanced Behavior Processing and Footprints (P&SEBP&F). Originally known as the Phishing and Sybil Enhanced Behavior Processing and Footprint, P&STL&T was renamed P&STL&T. Phishing and Sybil were used as well as varied attacker-to-victim node ratios to test the effectiveness of the new tactics for assessing effectiveness. Compared to previous study and the work of the other authors cited, there were approximately 30% fewer attackers detected during the research.

Energy Utilization and Transmission Delay Optimization in Cox VANET networks

Among the challenges in VANETs networks there is power control, which permits to achieve better permformance in terms of Signal to Interference plus Noise Ratio (SINR), Energy Efficiency (EE), Energy Utilization (EU),... Another important point to address is the transmition delay, which we should adapt it to be under an acceptable threshold. For that, we model the network as a Cox Line-Point process, and the transmitter as an M/D/1 queue server. To solve this tradeoff problem, we use machine learning techniques, especialy Q learning algorithm. It is shown , via simulations, that through our algorithm, the vehicular transmitter be able to learn its transmit power in an autonomous way, and achieve better performance for the energy utilization rate, the system waiting time and the area spectral efficiency.

Studying Routing Issues in Vanets Using Ns-3 and SUMO

Vehicular Ad-hoc Networks VANETs are normally sparse, highly dense, and highly mobile with many different and ever-changing topologies. These characteristics impose a challenge on finding a routing algorithm that fits the requirements of such network. The aim of this work is to study the performance issues of VANETs under different scenarios using realistic mobility models. In this paper, a comparative study is done among Ad-hoc On- Demand Distance Vector (AODV), Optimized Link State Routing (OLSR) and position-based routing protocols, namely Greedy Perimeter stateless routing (GPSR), and Max duration Min angle GPSR (MMGPSR). The comparison is done using key quality of service QoS metrics such as average routing goodput, end-to-end delay, MacPhy overhead, and packet delivery ratio PDR. The study is conducted using Network Simulator 3 (NS3) and SUMO.