Vehicular Ad Hoc Networks Network Research Articles

Knowledge-based system and expectation-maximization to discovering causes of imperfect labels in vehicular networks clustering

This paper focuses on the development of a knowledge-based system for automatically diagnosing issues in Vehicular ad hoc networks (VANETs). VANETs enable communication between vehicles and infrastructure, enhancing road safety and efficiency through timely information exchange. The proposed system aims to efficiently maintain and ensure the continuity of network service by leveraging innovative pattern recognition methods tailored to VANETs. The automatic diagnosis problem in VANETs involves estimating the operating class of network components based on sensor observations. This entails associating sensor measurements with specific operating modes. By implementing condition-based preventive maintenance procedures, potential component failures can be detected early, mitigating network disruptions. Various approaches, such as expert systems, fault trees, network state models, and statistical learning through pattern recognition, can be employed to address this problem. This paper primarily focuses on the statistical learning approach, where a classification or regression function is learned from a set of examples to assign operation modes to new measurements. It discusses relevant metrics and preprocessing techniques to simplify the decision-making process. The diagnostic system's results are determined based on the formulation of the classification or regression problem. The learning base is constructed, and an appropriate classification method is selected to develop and validate the automatic diagnosis system. While non-parametric models like support vector machines are commonly used, this article emphasizes the significance of considering assumptions and leveraging additional information to enhance performance. It proposes a more specific formalization of the problem, integrating the unique characteristics of VANETs. The contributions of this article revolve around the theory of belief functions, a generative approach, and the utilization of parametric models defined using graphical models. Experimental studies conducted on artificial datasets have demonstrated the benefits of the semi-supervised approach within the context of VANET networks.

Evaluation of the performance of the vehicular ad hoc network protocols in the case of V2I and EV2I communications

Vehicular ad hoc network (VANET) is an intelligent technology that enables efficient communication, secure data transmission, and traffic management. The purpose of routing protocols in the VANET network is to route data between vehicles (V2V) and vehicles-to-infrastructure (V2I). Recently, researchers have shown interest in designing effective routing protocols for the VANET network, as not all existing protocols are suitable for all traffic scenarios. Electric vehicles (EVs) are increasingly being adopted and integrated into intelligent transportation systems (ITS). Developed countries are actively promoting sustainable transportation solutions to increase energy efficiency and reduce carbon emissions. Therefore, this research presents an EV charging station (CS) management scheme based on communication between EVs and RSUs, with performance evaluation simulated using VANET network protocols. In this study, the G-MDORA, MDORA, and geographical routing protocol (GRP) protocols were modified to accommodate V2I communication, and RSUs were distributed along the roadmap. Additionally, a scheme for managing electric vehicle CS was presented, focusing on the communication between electric vehicles and RSUs in the EV2I context. Performance was evaluated using the G-MDORA, GRP, and MDORA protocols, considering factors such as throughput, communication overhead, packet delivery ratio, and end to end delay.

Analysing and Evaluation of Routing Protocols in VANET

Among the application of Ad Hoc network we have Vehicular Ad Hoc Network (VANET), which is an important data research domain today. VANET can improve the traffic road efficiency in order to overcome many problems and offer more services to customer. For instance, VANET can lead to reduce traffic jam or improve road safety by minimizing vehicles’ accident. Like any wireless network without infrastructure, the routing task is the most difficult task especially when nodes are mobile. The mobility of nodes, which are vehicles in VANET, causes a rapid change of network topology and leads to bread connection between source and destination nodes. In this paper, we will analyze some routing protocols in order to evaluate their performances. In this study we have proposed some scenarios of VANET routing protocols. The simulation has been done on NS3 simulator and SUMO. Simulation proves that routing protocols have a huge impact on VANET network performances.

Investigating Routing in the VANET Network: Review and Classification of Approaches

Vehicular Ad Hoc Network (VANETs) need methods to control traffic caused by a high volume of traffic during day and night, the interaction of vehicles, and pedestrians, vehicle collisions, increasing travel delays, and energy issues. Routing is one of the most critical problems in VANET. One of the machine learning categories is reinforcement learning (RL), which uses RL algorithms to find a more optimal path. According to the feedback they get from the environment, these methods can affect the system through learning from previous actions and reactions. This paper provides a comprehensive review of various methods such as reinforcement learning, deep reinforcement learning, and fuzzy learning in the traffic network, to obtain the best method for finding optimal routing in the VANET network. In fact, this paper deals with the advantages, disadvantages and performance of the methods introduced. Finally, we categorize the investigated methods and suggest the proper performance of each of them.

LSKA-ID: A lightweight security and key agreement protocol based on an identity for vehicular communication

Recently, a huge effort has been pushed to the wireless broadcasting nature in the open area. However, the vehicular ad hoc network (VANET) is disposed to various kinds of attacks. Hence, keeping the security in VANET is the most critical issue because of the VANET network related to human life. Thus, we propose a robust and lightweight security and key agreement-based identity protocol LSKA-ID for vehicular communication. Our protocol utilizes the elliptic curve cryptography, Chinese reminder theorem, and identity (ID)-based cryptosystem to resolve the issues found in the previously proposed schemes, in which our protocol can resolve the key escrow issues accompanied in most ID-based schemes. Also, it does not need batch verification operations, which cause some problems to the verifier in case the batch beacons have one or more illegal beacons. Moreover, the LSKA-ID protocol addresses the dependency on the trusted authority (TA) during the high frequent handover between the groups that may cause a bottleneck problem on the TA. The security analysis proves the correctness of the LSKA-ID protocol by using the random oracle model and has shown to be effective in a performance evaluation.

Analysis of Greedy Strategies in Geographic Routing of VANETs

The study and creation of networks that are independent of any pre-existing infrastructure has become highly popular in recent years. These networks fall under the category of Vehicular Ad-hoc Networks (VANETs), in which each car takes part in routing by sending data to other vehicles. However, geographic routing attracts VANETs networks because they lack route discovery and routing databases and the accessibility of low-cost GPS systems as well as the advent of the 128-bit address structure of IPv6 have enhanced the advantages of geographical routing over classical routing techniques. Additionally, the proposed algorithms based on greedy routing are the main method for the next hop selection in multi-hop communication for optimal and optimized solution in geographic routing. In this article, the study takes place with a number of hops per route and greedy algorithm selection success ratio in VANETs with source-to-destination pairs. A comparative study is taken out in which LLP affects node density, hop count, and transmission range. Monte Carlo simulations have been done with two important network parameters which are transmission range and node density. This study will be a guideline for the choice of greedy decisions to meet the objectives of network applications.

A comparison of the performance of the ad hoc on-demand distance vector protocol in the urban and highway environment

In recent years, the vehicular ad hoc network (VANET) has received great attention, as it is involved in the design of the intelligent transportation system (ITS). The VANET network includes message flows from vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) where the network is propped by wireless communication technology, such as IEEE 1609 WAVE and IEEE 802.11P. The VANET network implementation faces challenges, and one of these challenges is the design of routing protocols that transfer reliable and efficient packets from vehicle to vehicle. In VANET, steering is a challenging task in the highway and urban environment. Therefore, this paper presents an assessment of ad hoc on-demand distance vector protocol (AODV) performance in the highway and urban environment and to study the effect of vehicle density on protocol performance. The AODV protocol was simulated by MATLAB. In this study, the performance of AODV protocol was evaluated through four measures, namely, packet delivery ratio (PDR), overhead, end-to-end (E2E) delay, and dropped packets. The study in our paper showed that the best performance of the AODV protocol is in an environment where vehicle speed and vehicle density are low.

Adaptive Sliding Contention Window Design to Minimize Safe Message Collision Rates with Different Priority Levels in VANET

In Vehicular Ad Hoc Networks (VANETs), the broadcast is an important form of communication and occupies the main traffic in the network. However, since there is no recovery for broadcast frames in the VANET network, the collision rate between safe message traffic can become very high, especially in vehicle-dense network conditions. This paper proposes a new coordination mechanism that adaptively controls the Contention Window (CW) size for broadcast to reduce the safe message collision rate with different priority levels. In our mechanism, each vehicle in the VANET can automatically adjust the CW based on the perception of the current network condition by analyzing the percentage of successfully received frames. The algorithm controls the CW size by sliding the window with a dynamic persistence factor according to each type of safe message traffic. Each data traffic chooses a backoff timer dynamically varying in the range [0, CW[AC[i]]], which can overlap the CW range with other data traffic to improve efficient bandwidth depending on the network conditions. Simulation results prove that the proposed mechanism significantly reduces the collision rate for both safe message traffic in high-priority and low-priority in vehicle-dense network conditions.

A collaborated genetic with lion optimization algorithms for improving the quality of forwarding in a vehicular ad-hoc network

Vehicular ad-hoc network (VANET) is dynamic and it works on various noteworthy applications in intelligent transportation systems (ITS). In general, routing overhead is more in the VANETs due to their properties. Hence, need to handle this issue to improve the performance of the VANETs. Also due to its dynamic nature collision occurs. Up till now, we have had immense complexity in developing the multi-constrained network with high quality of forwarding (QoF). To solve the difficulties especially to control the congestion this paper introduces an enhanced genetic algorithmbased lion optimization for QoF-based routing protocol (EGA-LOQRP) in the VANET network. Lion optimization routing protocol (LORP) is an optimization-based routing protocol that can able to control the network with a huge number of vehicles. An enhanced genetic algorithm (EGA) is employed here to find the best possible path for data transmission which leads to meeting the QoF. This will result in low packet loss, delay, and energy consumption of the network. The exhaustive simulation tests demonstrate that the EGA-LOQRP routing protocol improves performance effectively in the face of congestion and QoS assaults compared to the previous routing protocols like Ad hoc on-demand distance vector (AODV), ant colony optimization-AODV (ACO-AODV) and traffic aware segmentAODV (TAS-AODV).

Millimeter-wave channel modeling in a VANETs using coding techniques.

The Vehicular ad-Hoc Network (VANET) is envisioned to ensure wireless transmission with ultra-high reliability. In the presence of fading and mobility of vehicles, error-free information between Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) requires extensive investigation. The current literature lacks in designing an ultra-reliable comprehensive tractable model for VANET using millimeter wave. Ultra-reliable communication is needed to support autonomous vehicular communication. This article aims to provide a comprehensive tractable model for VANET over millimeter waves using Space-Time-Block-Coding (STBC) concatenated with Reed Solomon (RS) coding. The designed model provides the fastest way of designing and analyzing VANET networks on 60 GHz. By using the derived BER expressions and Reed Solomon coded doppler expression ultra-reliable vehicular networks can be build meeting the demands of massive growing volume of traffic. The performance of the model is compared with previous BER computational techniques and existing VANET communication systems, i.e., IEEE 802.11bd and 3rd generation partnership project vehicle to everything (3GPP V2X). The findings show that our proposed approach outperforms IEEE 802.11bd and the results are comparable with V2X NR. Packet Error Rate (PER), Packet Reception Ratio (PRR) and throughput are used as performance metrics. We have also evaluated the model on higher velocities of vehicles. Further, the simulation and numerical findings show that the proposed system surpass the existing BER results comprising of various modulation and coding techniques. The simulation results are verified by the numerical results there-by, showing the accuracy of our derived expressions.

Flexible handover solution for vehicular ad-hoc networks based on software defined networking and fog computing

<p>Vehicular ad-hoc networks (VANET) suffer from dynamic network environment and topological instability that caused by high mobility feature and varying vehicles density. Emerging 5G mobile technologies offer new opportunities to design improved VANET architecture for future intelligent transportation system. However, current software defined networking (SDN) based handover schemes face poor handover performance in VANET environment with notable issues in connection establishment and ongoing communication sessions. These poor connectivity and inflexibility challenges appear at high vehicles speed and high data rate services. Therefore, this paper proposes a flexible handover solution for VANET networks by integrating SDN and fog computing (FC) technologies. The SDN provides global knowledge, programmability and intelligence functions for simplified and efficient network operation and management. FC, on the other hand, alleviates the core network pressure by providing real time computation and transmission functionalities at edge network to maintain the demands of delay sensitive applications. The proposed solution overcomes frequent handover challenges and reduces the processing overhead at core network. Moreover, the simulation evaluation shows significant handover performance improvement of the proposed solution compared to current SDN based schemes, especially in terms of handover latency and packet loss ratio under various simulation environments.</p>

Data Dissemination in VANETs Using Particle Swarm Optimization.

A vehicular Ad-Hoc Network (VANET) is a type of Mobile Ad-Hoc Networks (MANETs) that uses wireless routers inside each vehicle to act as a node. The need for effective solutions to urban traffic congestion issues has increased recently due to the growing number of automobile connections in the car communications system. To ensure a high level of service and avoid unsafe situations brought on by congestion or a broadcast storm, data dissemination in a VANET network requires an effective approach. Effective multi-objective optimization methods are required to tackle this because of the implied competing nature of multi-metric approaches. A meta-heuristic technique with a high level of solution interactions can handle efficient optimization. To accomplish this, a meta-heuristic search algorithm particle optimization was chosen. In this paper, we have created a network consisting of vehicles as nodes. The aim is to send emergency messages immediately to the stationary nodes. The normal messages will be sent to the FIFO queue. To send these messages to a destination node, multiple routes were found using Time delay-based Multipath Routing (TMR) method, and to find the optimal and secure path Particle Swarm Optimization (PSO) is used. Our method is compared with different optimization methods such as Ant Colony Optimization (ACO), Firefly Optimization (FFO), and Enhanced Flying Ant Colony Optimization (EFACO). Significant improvements in terms of throughput and packet loss ratio, reduced end-to-end delay, rounding overhead ratio, and the energy consumption are revealed by the experimental results.

Metaheuristics-based Clustering with Routing Technique for Lifetime Maximization in Vehicular Networks

Recently, vehicular ad hoc networks (VANETs) finds applicability in different domains such as security, rescue operations, intelligent transportation systems (ITS), etc. VANET has unique features like high mobility, limited mobility patterns, adequate topology modifications, and wireless communication. Despite the benefits of VANET, scalability is a challenging issue which could be addressed by the use of cluster-based routing techniques. It enables the vehicles to perform intercluster communication via chosen CHs and optimal routes. The main drawback of VANET network is the network unsteadiness that results in minimum lifetime. In order to avoid reduced network lifetime in VANET, this paper presents an enhanced metaheuristics based clustering with multihop routing technique for lifetime maximization (EMCMHR-LM) in VANET. The presented EMCMHR-LM model involves the procedure of arranging clusters, cluster head (CH) selection, and route selection appropriate for VANETs. The presented EMCMHR-LM model uses slime mold optimization based clustering (SMO-C) technique to group the vehicles into clusters. Besides, an enhanced wild horse optimization based multihop routing (EWHO-MHR) protocol by the optimization of network parameters. The presented EMCMHR-LM model is simulated using Network Simulator (NS3) tool and the simulation outcomes reported the enhanced performance of the proposed EMCMHR-LM technique over the other models.

A Review on Machine Learning Techniques Used in VANET and FANET Networks

The widespread use of the Internet and the increase in the number and variety of devices connected to the internet have led to the emergence of new methods in wireless communication. Dynamic and temporary Ad-Hoc networks, which do not require a fixed infrastructure as in traditional wireless network communication, are one of these new methods. The fact that Ad-Hoc networks do not need a fixed infrastructure has revealed a network structure with a lower cost and less configuration. Mobile Ad-Hoc networks play an important role, especially in the communication of nodes on the move. FANET (Flying Ad-Hoc Networks) networks, which are called flying ad hoc networks, are mobile Ad-Hoc networks used for communication of unmanned aerial vehicles (UAV), and VANET (Vehicular Ad-Hoc Networks) networks, which are called vehicular ad hoc networks, are mobile Ad-Hoc networks used for communication of road vehicles. The development and dissemination of these networks make a significant contribution to the development of autonomous vehicles and UAVs. The increase in the use of FANET and VANET networks, which are specialized subnets of mobile Ad-Hoc networks, and the increase in the number of nodes in these networks have caused problems related to security, efficiency, and sustainability in these networks. Machine learning methods, one of today' s effective and common approaches, are one of the ways that are frequently used in solving the problems specified in FANET and VANET networks. The rapid topology change, which is one of the most important features of these networks, makes it difficult to provide traffic management, trust management, routing, and data transmission. In this direction, machine learning approaches play an active role. In this study, it is presented by examining which machine learning techniques are used in the literature to perform important tasks such as traffic management, trust management, routing, and data transfer. Thus, it is aimed for those who will work in these fields to acquire information about machine learning approaches that can be used. Since the FANET network type is a new approach, it has been observed that there are few studies using machine learning. In VANET systems, studies using machine learning methods are especially intense in 2021. This study was carried out to give the reader an idea about which machine learning methods can be used in which problems in FANET and VANET networks.

Analisis Jaringan VANET Antar Kendaraan Pada Kondisi LOS dan NLOS Menggunakan Metode Single Relay

The development of transportation technology continues to progress with various models and forms of vehicles that have been produced both on a small scale and on a large scale. The large use of transportation equipment can cause congestion in operating it, one way to reduce this congestion is to use a vehicle-to-vehicle communication system in conditions where there are no obstacles and there are obstacles based on the parameters used through Bit Error Rate and Throuhgput. Vehicular ad hoc network (VANET) is a discipline branch of mobile ad hoc network (MANET) which has the advantage of being able to adapt points to vehicles, markings, signs and buildings. Information exchange on VANET technology communication consists of vehicle to vehicle (V2V) and vehicle to infrastructure (V2I). This study discusses the comparison of VANET networks when there are no obstacles (LOS) and obstacles (NLOS) conditions using Quarature Amplitude Modulation 16 bits per symbol (16-QAM). The use of the single relay method in this study consists of a simulation stage with the MATLAB application and an analysis of the simulation results obtained after carrying out the simulation. The results of this study are a comparison method between vehicles in the single relay network model where the value of the Bit Error Rate without obstacles is BER to distance ratio 〖4.54 x 10〗^(-5), BER to SNR 〖3.00 x 10〗^(-3), BER to speed 〖5.39 x 10〗^(-5). Whereas for Throughput when there are no obstacles, the value of Throughput to distance ratio is 23.750 Mbps, Throughput to SNR is 23.927 Mbps. Throughput to speed of 23.849 Mbps.. Then for comparison when there are obstacles, the value for Bit Error Rate is BER to distance ratio 〖7.17 x 10〗^(-5), BER to SNR 〖3.28 x 10〗^(-3), BER to speed 〖9.56 x 10〗^(-5) and the Throughput value when there are obstacles is the Throughput to distance ratio of 23.486 Mbps. Throughput of SNR 23.920 Mbps, Throughput of speed 23.633 Mbps

Controlling Adaptive Contention Window to Improve Safe Message Received Rate in VANET

The primary goal of Vehicular Ad Hoc Networks (VANETs) is to support the secure transmission of applications via vehicles. Broadcasting is an important form of communication in the VANET network. Since there is no recovery for broadcast frames in the VANET network, the rate of receiving safe messages can become very low, especially in dense network conditions. In this paper, we present a new broadcast coordination mechanism with the aim of adaptive control of CW size to improve the received rate of safety messages. Each vehicle in the VANET can identify current local conditions of the network such as collisions or congestion by analyzing recently successfully sent and received frames. Based on the analysis of the received frame rate at each vehicle, the proposed mechanism controls the CW size and uses the EDCA mechanism to prioritize the important data flows. Using a combination of simulation tools in VANET, we build simulation scenarios in different network conditions to evaluate the received rate of safety messages compared to the default mechanism in the 802.11p standard. Simulation results have demonstrated that the proposed mechanism improves the received rate of safety messages better than the default mechanism in the 802.11p standard in all cases.

Throughput and Outage Probability-Aware Intelligent Swarm-Based Multi-Hop Selection in Vehicular Network

Cooperation is an emerging paradigm for improving spatial differentiation in Vehicular Ad-Hoc Networks (VANETs). Hence, many network solutions are motivated through cooperative communications for boosting VANET efficiency regarding specific network constraints like energy efficiency, network capacity, and outage probability. The main intention of this paper is to design and develop an optimization model for selecting the minimum number of multi-hops between the source and destination for the cooperative VANET networks. The first phase has a first-time slot, where a signal is transmitted by the source to the cooperative nodes (its relays) and their equivalent destination. The main problem considered here is to optimally select the number of hops or relays that are adaptable for communication, to solve the multi-objective functions concerning the target throughput and outage probability. The adoption of new Dimension-based Cat Swarm Optimization (D-CSO) is the main contribution here to optimally select the multi-hops among source and destination. Through the performance evaluation, it is observed that the designed model has achieved networks-wide fairness performance and a good convergence rate.

A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network

Nowadays, technology is developed rapidly in communication technology. Several new technologies have been introduced due to the evolution of wireless communication and this provided the way to communicate among vehicles, using a Vehicular Ad-Hoc Network (VANETs). Routing in VANETs becomes most challenging because of the huge mobility and dynamical topology changes, which lead to reduced efficiency in the network. The core idea of this network is to increase the efficiency during the process of the communication. The most suited routing protocol for VANETs is Geographic routing, for the reason that it provides higher scalability and low overheads. The major challenges in VANETs are the selection of best neighbor in dynamically changing VANET topology. Furthermore, to provide better QoS needful actions are essential. In this paper, we introduced a new MAC/NET with Updated Genetic Algorithm—A Cross Layer Approach, (MNUG-CLA) based on a MAC layer and network layer to overcome the drawbacks of the network. In the network layer, a new neighbor discovery protocol is developed to select the best next hop for the dynamically varying network. In the MAC layer, in order to improve the quality, multi-channel MAC model is introduced for instantaneous transmission from various service channels. For overall optimal path selection, we used an updated GA algorithm. The performance was demonstrated through the use of an extensive simulation environment, NS-2. The simulation results prove that this protocol provides better results, in terms of energy efficiency, energy consumption and successive packet transmission, when compared with the earlier approaches.

Secure Authenticated Communication Via Digital Signature and Clear List in VANETs

Vehicular ad hoc network (VANET) plays a vital role in the intelligent transportation system (ITS). When a vehicle receives a message through network, the CRL (certificate revocation list) checking process will operate before certificate and signature verification. After successful authentication, a CRL list is created based on authentication. This CRL is used to verify whether a vehicle node can be permitted for communication in the VANET network. But when using CRL, a huge amount of storage space and checking time is needed. So we proposed a method without the CRL list. It mentions a key management list to overcome large storage space and checking time. Even it reduced the access delay. For access permission we can do an authentication system based digital novel signature authentication (DNSA) for each vehicle in the VANET with the RSU unit or with other participant node vehicles in the communication as per the topology. We can perform an efficient and secured communication in VANET.

Improved maximum distance on-demand routing algorithm routing protocol for vehicular ad hoc network network in an urban environment

Vehicular ad hoc network (VANET) is a modern technology that has received great attention in the modern era due to daily road accidents. In VANET network it is difficult to design effective routing protocols due to the speed of movement of nodes and the rapid change in network architecture, and the purpose of routing protocols in the VANET network is to route data between vehicles (V2V), and between vehicles to infrastructure (V2I). Recently researchers have been interested in designing effective routing protocols for the VANET network because not all existing protocols are suitable for all traffic scenarios. Therefore, the focus of this paper will be on the maximum distance on-demand routing algorithm (MDORA) protocol and work on improving the protocol algorithm so that it is compatible with the urban environment. After that, the improved performance of the MDORA-without direction (MDORA-WD) protocol will be compared with the ad-hoc on-demand distance vector (AODV) protocol in terms of communication overhead, packet delivery ratio (PDR) and end to end (E2E) delay. The protocols will be simulated by MATLAB.