Vehicular Ad Hoc Networks Routing Protocols Research Articles

Comparative analysis of reactive routing protocols for vehicular adhoc network communications

In the recent past, vehicular adhoc networks (VANETs) have gained a lot of importance. The routing protocols play a vital role to deliver payloads from one vehicle to another one while they are moving at relative speeds. It is a challenging task to select a routing protocol for VANETs because of the uneven distribution and high mobility of vehicles. In this paper, we have analysed the two standard reactive protocols, adhoc on-demand distance vector (AODV) and ant colony optimization (ACO). The performance comparison of AODV and ACO routing protocols has been presented in this paper. The results show AODV performed better in terms of energy consumption and routing overhead. While considering the throughput, energy loss ratio, and delay ACO has performed. ACO resulted as upperhand.

A comprehensive review on vehicular ad-hoc networks routing protocols for urban and highway scenarios, research gaps and future enhancements

A comprehensive review on vehicular ad-hoc networks routing protocols for urban and highway scenarios, research gaps and future enhancements

Secure Vehicle-to-Vehicle Communication Using Routing Protocol Based On Trust Authentication Secure Sugeno Fuzzy Inference System Scheme

Introduction: Vehicular Ad-hoc Network (VANET) is wireless communication between Roadside vehicles and vehicle infrastructure. Vehicle Ad Hoc Network (VANET) is a promising technology that effectively manages traffic and ensures road safety. However, communication in an open-access environment presents real challenges to security and privacy issues, which may affect large-scale deployments of VANETs. Vehicle identification, classification, distribution rates, and communication are the most challenging areas in previous methods. Vehicular communications face challenges due to vehicle interference and severe delays. Method: To overcome the drawbacks, this work proposed a new method based on the Artificial Neural Network Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators. Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the security performance of VANET. Use ANFIS-based Secure Sugeno Fuzzy System for calculating the node weights for data transferring; reduced the attacks accuracy of network malicious attacks. objective: To overcome the drawbacks, this work proposed a new method based on Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the security performance of VANET. Result: In the improved cluster-based VANET routing protocol, each node obtains an address using a new addressing scheme between the wireless vehicle-2-vehicle (V2V) exchanges and the Roadside Units (RSUs). It will explore the effectiveness of the Secure Sugeno Fuzzy System-based adaptation term Enhanced Cluster-based routing protocol in finding the vehicle's shortest-path for transmission. Conclusion: Simulation results show that in the proposed ANN-based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) analysis, the packet delivery ratio is 93%, delay performance is 0.55sec, throughput performance is 94%, bandwidth is 55bits/sec, Network security is 92%, and the transmission ratio is 89%, attack detection is 90%.

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.

RTRV: An RSU-assisted trust-based routing protocol for VANETs

Routing in vehicular ad hoc networks (VANETs) enables efficient communication and data exchange between vehicles and infrastructure components, which is a critical aspect of providing intelligent transportation systems (ITS). Due to the dynamic nature of VANETs, where vehicles have high mobility and frequent link disconnections, secure routing protocols are essential for establishing reliable and efficient communication paths. Employing trust criteria in routing is an effective solution that routing protocols use to become secure. In this paper, we propose an RSU-assisted trust-based routing protocol for VANETs (RTRV), which considers trust criteria to provide secure routing. To identify and limit malicious nodes from disrupting the routing, RTRV provides a more reliable monitoring process for trust management, which increases resistance to trust-based attacks. In the monitoring process, as each data packet is sent to a next-hop, two vehicles observe the behavior of the next-hop in forwarding the packet, and the observations are then used to update the direct trust of the next-hop. The observations are additionally reported to RSUs, which gather the reports to update the indirect trust of the next-hop and provide recommendations to vehicles within their transmission range. In addition to indirect trust management, in RTRV, RSUs play an active role in the routing of data packets, which improves the routing performance. We simulated the proposed protocol in different scenarios with SUMO and OMNeT++ tools and then compared it with the TGRV protocol. The results of our analysis conclusively show that RTRV outperforms TGRV in terms of packet delivery ratio, end-to-end delay, and average hop count in both scenarios, with and without RSU. Specifically, in the RTRV without RSU scenario, when the percentage of malicious vehicles reaches 25%, RTRV achieves a 3.8% higher packet delivery ratio compared to TGRV. Furthermore, RTRV demonstrates significantly reduced end-to-end delay and average hop count, with values 0.14 and 0.11 times lower than TGRV, respectively. In addition, the cost and security analysis demonstrate that the proposed protocol has acceptable overheads in memory, communication, and computation and is also resistant to critical trust-based attacks.

A Hybrid Model for Performance Evaluation of Fixed VANETs using Novel 1C3N and Topology-Based Ad-Hoc Routing Protocols with Packet Loss Control Methods

Vehicular ad-hoc Networks (VANETs) play a significant role in Intelligent Transportation Systems (ITS) Design. Intelligent Transportation Systems are the first mandatory requirements for any smart city. Researchers are vigorously working on ITSs for smart cities and so VANETs have received a lot of attention. In VANETs, Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) message transmissions are carried out using wireless access technologies like IEEE 802.11p and IEEE 1609 WAVE family of standards. The crucial challenge in the implementation of VANETs involves the task of deciding the routing protocol because unlike MANETs, handover in VANETs is extremely high. In this paper, a novel routing technique, One Caption for 3 Nodes (1C3N) algorithm is proposed. This algorithm is implemented along with other topology-based existing routing protocols for the implementation of VANETs in the Coimbatore-Urban Area (Indian Smart City). The performance evaluation is carried out by comparing metrics like goodput, Overhead, Packet delivery ratio (PDR), Packet loss ratio (PLR) and end-to-end delay for four existing VANET routing protocols. The results show that a proper combination of packet loss model with routing protocol enhances the goodput and reduces the overhead for a fixed VANET. It is observed that the proposed 1C3N routing technique provides 60-65% better goodput than the other four algorithms.

OPBRP - obstacle prediction based routing protocol in VANETs

Vehicular Ad-hoc Networks (VANETs) are recently getting high attention from different researchers due to increasing traffic problems, especially in densely populated countries. Increasing rates of accidents call for an Intelligent Transportation System (ITS) with efficient performance to reduce and mitigate this trend. The required enhancement of the ITS can be more focused on traffic performance, integrity, and reduction of the vehicles’ CO 2 emissions. Existing routing protocols for VANETs consider different situations and methods to establish reliable communication between the vehicles and infrastructures. However other situations have not been addressed carefully like the link stability between vehicles during packet exchange. This paper develops an Obstacle Prediction Based Routing Protocol (OPBRP) for vehicle detection, packets transmission to Roadside Units (RSU) and choosing a better route in terms of reliability via using vehicle's Kinematics and Mobility prediction in VANET. Two fundamental contributions are included in this paper: (1) Upgrading prediction routing protocol to transfer packets using a reliable path, and (2) Adding new logic in choosing the intermediate nodes to the destination to achieve a higher Packet delivery ratio (PDR). The OPBRP uses the predictive greedy as forwarding algorithm and the predictive perimeter forwarding as recovery algorithm after introducing enhancements to both algorithms to meet the requirements of VANETs environment. To materialize the value achieved from the mentioned contributions we tested the newly developed OPBRP against the existing routing protocols using Vehicle in Network Simulation (Veins) which shows that our proposed protocol outperformed current existing routing protocols in terms of PDR by achieving 18.46% improvement, end-to-end delay (E2E-Delay) by achieving 10.51% improvement, and total power consumption used in transmission by achieving 23.80% improvement.

A novel k-means powered algorithm for an efficient clustering in vehicular ad-hoc networks

<p>Considerable attention has recently been given to the routing issue in vehicular ad-hoc networks (VANET). Indeed, the repetitive communication failures and high velocity of vehicles reduce the efficacy of routing protocols in VANET. The clustering technique is considered an important solution to overcome these difficulties. In this paper, an efficient clustering approach using an adapted k-means algorithm for VANET has been introduced to enhance network stability in a highway environment. Our approach relies on a clustering scheme that accounts for the network characteristics and the number of connected vehicles. The simulation indicates that the proposed approach is more efficient than similar schemes. The results obtained appear an overall increase in constancy, proven by an increase in cluster head lifetime by 66%, and an improvement in robustness clear in the overall reduction of the end-to-end delay by 46% as well as an increase in throughput by 74%.</p>

Intersection-Based Unicast Routing Using Ant Colony Optimization in Software-Defined Vehicular Networks

A vehicular ad hoc network (VANET) is a mobile ad hoc network composed of communication between vehicles, between vehicles and roadside units, and between vehicles and pedestrians, in order to achieve traffic safety and entertainment services. The design of the routing protocol is very important for the realization of the service function of VANET. Local optimum and network congestion problems are restraints of traditional geographic routing protocols for VANET. In this paper, a software-defined network (SDN) based unicast routing scheme in an urban traffic environment is proposed, which uses Dijkstra’s algorithm to find a global optimal anchor path. The RSU neighbor discovery protocol is proposed, through which each RSU can discover its neighbor RSUs, and then each RSU periodically sends ant packets to its neighbor RSUs, evaluates the communication connection quality of each street segment according to the statistical data of the ant packets received, and sends the evaluation value to the SDN server in time. The SDN server has the connection quality evaluation values of all street segments in the global scope, from which an optimal anchor path can be calculated. The simulation results show that the proposed scheme has better packet delivery ratio than other related schemes.

Weight-Based Clustering Algorithm for Military Vehicles Communication in VANET

In vehicular ad-hoc network (VANET), every vehicle node indicates a mobile node and it acts as a transmitter, receiver and router for the delivery of the information. VANET is a subgroup of mobile ad-hoc network (MANET) and is related to the dynamic topology. Dynamic network scenarios are more challenging issues as compared to MANET topologies, so finding a suitable algorithm for all VANET applications is the major challenge for the researchers. Routing protocols in VANET are divided into six parts i.e., cluster-based, geocast-based, topology-based, position-based, and broadcast-based. Autonomous robots and unmanned military vehicles (UMVs) become part of the advanced warfare strategy to execute dangerous war field operations and military combat missions. The military vehicles (MVs) transfer information to each other in order to achieve required military tasks collectively. In the proposed work, rhombus shaped area is divided into multiple clusters using a weight-based clustering algorithm for transmitting the event information to the vehicles. Intersection clustering with rhombus shaped area which are very effective for clustering. To choose cluster head (CH), the proposed method has used two weighted metrics, one is real time average speed and the other parameter is degree. This work is useful for choosing right CH in the network. Each vehicle in the same cluster transmits the data to the CH instead of broadcasting it. The simulation has been done in the SUMO and NETSIM simulator, which shows the network performance for the different protocols like Ad-hoc on-demand distance vector (AODV), dynamic source routing (DSR) in terms of packet delivery ratio, throughput, delay, overhead transmission, mean and standard deviation.

DARVAN: A fully decentralized anonymous and reliable routing for VANets

With the recent advances in the automobile industry and the emergence of smart self-driving vehicles, the increasing need for proper and optimal Vehicular Ad-hoc Network (VANet) protocols has drawn the attention of many researchers. Nevertheless, the VANet community has yet to agree on a unified and suitable set of protocols for such networks. One of the major concerns in these networks is security considerations, which are significant because of the specific characteristics of VANet. In this article, we aim to address the location privacy and reliability issues in VANet routing protocols. We propose DARVAN, a fully decentralized infrastructure that provides anonymous and reliable routing in VANets. By employing a distributed database and collective consensus, DARVAN minimizes the exposure of critical data, which is conventionally stored and processed in centralized units. DARVAN is deployed by modifying the I2P protocol, which aims to improve routing reliability and resilience to many adversary activities in VANets. Specifically, with DARVAN, we present an effective and efficient network-level mitigation for Sybil attacks in Vanets. Our extensive simulations on NS3 show that DARVAN performs well in terms of packet delivery ratio, overhead, delay, and reliability compared to the previous anonymous scheme proposed for VANet routing.

Comparative Analysis of Low and High Scalable VANET in terms of Receive Rate, Packets Received, MAC/PHY Overhead and Average Goodput

The modern advancements in mobile and wireless communications have made an tremendous change in human lifestyles. Mobile Ad-hoc Networks (MANETs) are self-organized and self-configured networks consisting of moving nodes that communicate through wireless communication systems. Vehicular Ad-hoc Network (VANET) is a special category of MANETs with some distinctive set of characteristics. Some common features of VANET are its use of multi-hop communications between mobile vehicles with limited bandwidth and self-organization of the moving vehicles. Also, VANET is differed from MANET by the feature of high mobility of nodes. One significant advantage of VANET over MANET is that the computing devices get continuous power from its vehicles. Scalability refers to a VANET's ability to accept a rising number of communicating vehicles without experiencing disruption or loss in data transfer or traffic loading, which increases administrative complexity and reduces network performance. Because of its difficult properties, designing a scalable and robust network is an open topic of research in VANET. When VANETs go from sparse to dense mode, or from high mobility to slow traffic conditions, several design methods fall short. The performance metrics Average Goodput, MAC/PHY overhead, packet received, and receive rate over four VANET routing protocols- Ad-hoc On Demand Vector (AODV), Destination Sequenced Distance Vector (DSDV), Dynamic Source Routing (DSR), and Optimized Link State Routing (OLSR) in a defined area. The results showed that DSR performed well in average goodput,MAC/PHY overhead, receive rate, and packets received in low and highly scalable scenarios.

An optimized framework for VANET routing: A multi-objective hybrid model for data synchronization with digital twin

The utilization of Digital Twin technology allows for the simulation of network behavior, anticipating traffic surges, and implementing efficient traffic routing strategies to prevent congestion. This enhances network efficiency and improves overall speed. However, VANETs (Vehicular Ad-Hoc Networks) pose unique challenges due to their dynamic nature and frequent network disconnects. Developing and implementing effective VANET routing protocols becomes complex considering these factors. To address these challenges, a novel hybrid optimization model is proposed in this research. The model comprises optimal Cluster Head (CH) selection for data transmission. The clustering of mobile nodes is initially performed, but ensuring consistency in fast-paced environments remains a significant challenge. Therefore, the selection of the most suitable node as the CH is crucial. This research introduces a novel route selection mechanism that focuses on optimal CH selection. Multiple objectives such as mean routing load, packet delivery ratio, throughput, End-to-End Delay, and Control packet overhead are considered in the CH selection process. To determine the ideal CH from a pool of potential candidates, a new hybrid optimization model called Hunger's Foraging Behavior Customized Honey Badger Optimization (HFCHBO) is introduced. The HFCHBO combines the standard Honey Badger Algorithm (HBA) with Hunger Games Search (HGS). This hybrid model effectively formulates successful routing paths for data transmission between vehicles and the CH to the Base Station (BS). By combining these two approaches, the HFCHBO model aims to overcome the limitations of traditional clustering algorithms in ensuring consistent performance in dynamic environments. The proposed route selection mechanism incorporates multiple objectives to evaluate the performance of potential CHs, including mean routing load, packet delivery ratio, throughput, End-to-End Delay, and Control packet overhead. To facilitate data transmission between vehicles and the CH to the Base Station (BS), the HFCHBO model formulates successful routing paths. By utilizing the simulation capabilities of the Digital Twin technology, the model analyzes the network behavior, predicts traffic patterns, and makes informed decisions on routing strategies.

AN EXPERIMENTAL TESTBED PERFORMANCE EVALUATION FOR VEHICULAR AD HOC NETWORK (VANET)

Vehicular ad hoc networks (VANETs) are essential components of a smart city environment owing to some safety-enhancing applications. The use of VANETs extends from safety applications such as accident warning and collision avoidance to entertainment applications such as streaming video or comfort applications such as parking spot identification. Besides, these applications require interconnecting, for example, by enabling vehicles to communicate with other vehicles. Thus, the flow of information between different vehicles requires implementing a suitable transmission route. This paper aims to use VANETs to empower vehicle-to-vehicle (V2V) and V2I communication through a network that does not need infrastructure. In this paper, we propose to utilize proactive routing protocols such as OLSR, BATMAN, and BABEL and present their performance via a developed testbed. The performance is compared based on two different positionings, multi-hop, and full mesh, using metrics such as throughput and packet loss. The proposed approach demonstrates that BABEL outperforms both the OLSR and BATMAN in both positionings. This research highlights the need to select the appropriate routing protocol for VANETs and evaluate their performance, which will be significant for widespread deployment.

Reinforcement Learning-Based Routing Protocols in Vehicular Ad Hoc Networks for Intelligent Transport System (ITS): A Survey

Today, the use of safety solutions in Intelligent Transportation Systems (ITS) is a serious challenge because of novel progress in wireless technologies and the high number of road accidents. Vehicular ad hoc network (VANET) is a momentous element in this system because they can improve safety and efficiency in ITS. In this network, vehicles act as moving nodes and work with other nodes within their communication range. Due to high-dynamic vehicles and their different speeds in this network, links between vehicles are valid for a short time interval. Therefore, routing is a challenging work in these networks. Recently, reinforcement learning (RL) plays a significant role in developing routing algorithms for VANET. In this paper, we review reinforcement learning and its characteristics and study how to use this technique for creating routing protocols in VANETs. We propose a categorization of RL-based routing schemes in these networks. This paper helps researchers to understand how to design RL-based routing algorithms in VANET and improve the existing methods by understanding the challenges and opportunities in this area.

TGRV: A trust-based geographic routing protocol for VANETs

Vehicular ad-hoc networks (VANETs) consist of highly mobile and self-organized nodes that wirelessly communicate with one another and transmit a variety of information, some of which may be critical information. Due to the rapidly changing topology of VANETs and the high-speed mobility of the participating vehicles, the routing of packets in these networks is a challenging task. Moreover, the presence of malicious vehicles in VANETs makes the correct routing of packets more challenging. In this paper, we propose a trust-based geographic routing protocol for VANETs (TGRV) that limits the participation of malicious vehicles in routing. In TGRV, to select the next-hop, a sender not only considers the distance, speed, and direction of its neighbors but also evaluates their direct trust and recommendation trust. For this purpose, TGRV uses a monitoring system that allows each vehicle to monitor the correct packet forwarding rate of its next-hop. In this way, the vehicle updates its direct trust to the next-hop and retransmits the packets if the packets are lost. The vehicle also sends its observations of the next-hop to its neighbors with a push-based notification, and based on that, the neighbors can update their recommendation trust about the next-hop. The monitoring system applies distance prediction in a modified promiscuous mode to better estimate the correct packet forwarding rate of the next-hop. To enhance the accuracy of trust management, the trust values of interactions are reduced over time by using a decay factor. TGRV uses the number and trust of two-hop neighbors, which helps in selecting the next-hop that is in a more trusted zone. Our extensive simulations on OMNeT++ show that when the percentage of malicious vehicles in an urban scenario increases from 0 to 25%, the packet delivery ratio of TGRV decreases from 95.1 to 88.7%, which performs very well compared to the GPSR and PGRP protocols. Also, the end-to-end delay of TGRV increases from 3.79 to 7.07 s and the average hop count of TGRV increases from 6.7 to 9.9, which are significant increases compared to the other two protocols. These increases are acceptable because, in the other two protocols, there is no monitoring and retransmission, and the probability of packet delivery failure on longer routes is higher. The cost and security analysis also show that the memory, communication, and computational overheads of TGRV are acceptable, and TGRV is resistant and resilient to some important trust-based attacks.

A Review Paper of M-AODV Routing Protocol in VANET to Detect and Prevent Black Hole Attack

Abstract: In the field of contemporary intelligent transportation, vehicular ad hoc networks (VANETs) are a popular and promising technology. They are employed to provide effective life safety, intelligent transportation systems, and traffic information systems (TIS). Due to the nodes' mobility and the connections' brittleness, the VANET is open to a variety of security risks. One type of security risk known as a "black hole attack" involves a node presenting itself to other nodes in such a way that it has the quickest and most direct access to the target. As a result, an effective method for the detection and eradication of the Black hole attack in the Vehicular Ad Hoc Networks (VANET) is given in this research study. One of the most widely used routing protocols for VANET, AODV (Ad hoc On Demand Distance Vector) is used to achieve the suggested method. In the first stages of route discovery, the technique can identify both a single black hole attack and a cooperative black hole attack. The simulation is carried on NS2 and the results of the proposed scheme are compared to [14] and the fundamental AODV routing protocol, this results are examined on various network performance metrics such as packet delivery ratio, throughput and endto-end delay. The found results show the efficacy of the proposed method as throughput and the delivery ratio of the network does not deteriorate in presence of the back holes

Cross-layer greedy forwarding algorithm for VANET

In recent years, vehicular communication systems have taken an important place in Intelligent Transport Systems (ITS). These systems are designed on self-organizing networks, known as Vehicular Ad hoc NETworks (VANETs). Their objective is to struggle against road safety problems and road traffic congestion by integrating information and communication technologies in road infrastructures and vehicles. VANET differs from other types of ad hoc networks by high mobility and very dynamic topology. Consequently, the design of an efficient VANET routing protocol becomes a challenging task. Many routing schemes with QoS (Quality of Service) have been proposed in the literature for VANET, to satisfy the performances required by the different applications running upon such networks. Cross-layer designs, which make it possible to exchange and share information between the different layers of the network, have shown to be efficient in VANETs. This paper proposes an improved version of the geographic Greedy algorithm as a cross-layer routing protocol for VANETs. The cooperation between the network layer and the lower layers involves three cyber-physical parameters: Speed, position and Signal-to-Noise Ratio (SNR). The simulation results over the Network Simulator-3 (NS3) demonstrated the effectiveness of the proposed solution in terms of end-to-end delay, throughput and packet delivery ratio.

G-3MRP: A game-theoretical multimedia multimetric map-aware routing protocol for vehicular ad hoc networks

The particular requirements and special features of vehicular ad hoc networks (VANETs) (e.g., special mobility patterns, short link lifetimes, rapid topology changes) involve challenges for the research community. One of these challenges is the development of new routing protocols specially designed for VANETs. In this paper, we present a novel game-theoretical approach of a multimetric geographical routing protocol for VANETs to forward video-reporting messages in smart cities. Game theory is considered a very interesting theoretical framework to analyze and optimize resource allocation problems in digital communication scenarios. Our contribution has shown to enhance the overall performance of VANETs in urban scenarios, in terms of percentage of packet losses, average end-to-end packet delay and peak signal to noise ratio (PSNR).

A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs

Routing security attacks in Vehicular Ad hoc Networks (VANETs) represent a challenging issue that may dramatically decrease the network performances and even cause hazardous damage in both lives and equipment. This study proposes a new approach named Multivariate Statistical Detection Scheme (MVSDS), capable of detecting routing security attacks in VANETs based on statistical techniques, namely the multivariate normality tests (MVN). Our detection approach consists of four main stages: first, we construct the input data by monitoring the network traffic in real time based on multiple metrics such as throughput, dropped packets ratio, and overhead traffic ratio. Secondly, we normalize the collected data by applying three different rescaling techniques, namely the Z-Score Normalization (ZSN), the Min-Max Normalization (MMN), and the Normalization by Decimal Scaling (NDS). The resulting data are modeled by a multivariate dataset sampled at different times used as an input by the detection step. The next step allows separating legitimate behavior from malicious one by continuously verifying the conformity of the dataset to the multivariate normality assumption by applying the Rao–Ali test combined with the Ryan–Joiner test. At the end of this step, the Ryan–Joiner correlation coefficient (R–J) is computed at various time windows. The measurement of this coefficient will allow identifying an attacker’s presence whenever this coefficient falls below a threshold corresponding to the normal critical values. Realistic VANET scenarios are simulated using SUMO (Simulation of Urban Mobility) and NS-3 (network simulator). Our approach implemented in the Matlab environment offers a real time detection scheme that can identify anomalous behavior relying on multivariate data. The proposed scheme is validated in different scenarios under routing attacks, mainly the black hole attack. As far as we know, our proposed approach unprecedentedly employed multivariate normality tests to attack detection in VANETs. It can further be applied to any VANET routing protocol without making any additional changes in the routing algorithm.