Dynamic Nature Of Vehicular Ad Hoc Networks Research Articles

Cluster-based multicast optimized routing in VANETs using elite knowledge-based genetic algorithm

The advancement of autonomous and driver-assistance technology has greatly improved the experience of driving. With the growing use of vehicles, there is a greater need for effective communication among them to ensure both applications in Vehicular Ad-hoc Networks (VANETs) for safety and non-safety. VANETs commonly prioritize multicast communication to efficiently use computational resources and achieve a desirable Quality of Service (QoS). However, the dynamic nature of VANETs, marked by high mobility and frequent topology changes, presents significant challenges in establishing and sustaining multicast communication. To ensure that sensing data from all target points is reliably sent to the base station, it is crucial to address the important factors of energy balancing, load balancing, connectivity, and coverage. These factors play a vital role in achieving a balanced and efficient transmission system. In this research paper, clustering-based multicast routing is proposed that utilizes an enhanced Genetic Algorithm Elite Knowledge Sharing Genetic Algorithm (EKSGA) to choose cluster heads. The EKSGA identifies the top 5 % performers with the highest fitness score and generates a new population by exchanging genetic information, aiming to establish a population with the greatest potential for optimal fitness. The performance assessment of the suggested protocol demonstrates significant enhancements in network lifetime and throughput compared to various existing protocols, including LEACH, DEEC, p-WOA, DDEEE, ECBLTR, and GA with improvements ranging from 2.4 % to 22 % in network lifetime and from 3.2 % to 23.2 % in throughput for a system consisting of 500 nodes with energy level set at 0.1 J for rounds 300. Additionally, the proposed approach utilizes the existing population to generate a more fit population, thereby reducing the need for excessive iterations and computational overhead.

FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs

Vehicular ad hoc networks (VANETs) are used for vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. They are a special type of mobile ad hoc networks (MANETs) that can share useful information to improve road traffic and safety. In VANETs, vehicles are interconnected through a wireless medium, making the network susceptible to various attacks, such as Denial of Service (DoS), Distributed Denial of Service (DDoS), or even black hole attacks that exploit the wireless medium to disrupt the network. These attacks degrade the network performance of VANETs and prevent legitimate users from accessing resources. VANETs face unique challenges due to the fast mobility of vehicles and dynamic changes in network topology. The high-speed movement of vehicles results in frequent alterations in the network structure, posing difficulties in establishing and maintaining stable communication. Moreover, the dynamic nature of VANETs, with vehicles joining and leaving the network regularly, adds complexity to implementing effective security measures. These inherent constraints necessitate the development of robust and efficient solutions tailored to VANETs, ensuring secure and reliable communication in dynamic and rapidly evolving environments. Therefore, securing communication in VANETs is a crucial requirement. Traditional security countermeasures are not pertinent to autonomous vehicles. However, many machine learning (ML) technologies are being utilized to classify malicious packet information and a variety of solutions have been suggested to improve security in VANETs. In this paper, we propose an enhanced intrusion detection framework for VANETs that leverages mutual information to select the most relevant features for building an effective model and synthetic minority oversampling (SMOTE) to deal with the class imbalance problem. Random Forest (RF) is applied as our classifier, and the proposed method is compared with different ML techniques such as logistic regression (LR), K-Nearest Neighbor (KNN), decision tree (DT), and Support Vector Machine (SVM). The model is tested on three datasets, namely ToN-IoT, NSL-KDD, and CICIDS2017, addressing challenges such as missing values, unbalanced data, and categorical values. Our model demonstrated great performance in comparison to other models. It achieved high accuracy, precision, recall, and f1 score, with a 100% accuracy rate on the ToN-IoT dataset and 99.9% on both NSL-KDD and CICIDS2017 datasets. Furthermore, the ROC curve analysis demonstrated our model’s exceptional performance, achieving a 100% AUC score.

Filtering Malicious Messages by Trust-Aware Cognitive Routing in Vehicular Ad Hoc Networks

Vehicular Ad hoc Networks (VANET), as an inseparable part of Intelligent Transportation Systems (ITS), enable data communication between vehicles to promote road safety and traffic efficiency. But adversaries can also spread false information across these networks. Therefore, vehicles’ cognitive capabilities with respect to received data must be improved. This requires spatial intelligence and a mechanism to evaluate the trustworthiness of received data. But the dynamic nature of VANETs with intermittent connections, lack of infrastructure and real-time constraints make fulfilling this task very challenging. In this paper, we propose a trust-aware cognitive framework that exploits the redundancies in the exchanged DENM and CAM packets as well as spatial intelligence to filter malicious messages and prevent attackers from disrupting network operation. A novel supplementary mechanism is also presented that applies subjective logic on the pieces of information collected from all network vehicles to detect and isolate malicious entities. To assess the reliability of the proposed scheme, we made extensive comparisons between our model and two others. In the obtained results, our approach outperformed both of them and yielded an accuracy of over 90%, even when 50% of network participants were attackers. The supplementary malicious node detection mechanism of ours similarly yielded high accuracy and F1 scores in the simulations.

State of the Art VANETs Routing Protocols: A Literature Review

Wireless technology, especially Vehicular Ad-hoc Network (VANET), is developing rapidly. VANET is an emerging technology that assists intelligent transportation systems by improving traffic services and helping in minimizing road accidents. Data sharing in VANETs is time-critical, necessitating the formation of fast and robust network connections. Due to the highly dynamic nature of VANET, providing reliable, consistent, and seamless communication is a significant challenge. In the last decade, various routing approaches have been proposed to efficiently handle quick handover of safety and infotainment-related VANET applications. This paper reviews and investigates the existing routing protocols and classifies them into a taxonomy based on essential attributes such as forwarding strategies, routing strategies, network dimensions. Routing challenges and future research directions in the VANET area are discussed in this paper.

A Provably Secure Identity Based Authenticated Key Agreement Protocol with Multiple PKG Compatibility for Inter-Vehicular Ad hoc Networks

Vehicular Ad hoc Networks (VANETs) facilitate the timely dissemination of road traffic information, including early warnings related to road conditions, weather updates, traffic congestion and collisions. The information exchanged in VANETs is crucial for maintaining uninterrupted traffic flow, minimizing road mishaps, expediting emergency services and analyzing potential detours in case of congestion. In order to ensure the legitimacy of the interchanged messages and to preserve confidentiality, it is essential to establish secure authenticated channels among the communicating automotive nodes. Considering the highly dynamic nature of VANETs and the diversity of the participating vehicular nodes, Identity based Authenticated Key Agreement (ID-AKA) protocol in the multiple Private Key Generator (PKG) setting, is an ideal cryptographic technique for securing inter-vehicular communications. We cryptanalyze the existing pairing-free multiple PKG compatible ID-AKA (mPKG-ID-AKA) protocol designed for securing VANETs and prove that the protocol lacks weak Perfect Forward Secrecy (wPFS). Our investigations also reveal that previous attempts to model mPKG-ID-AKA protocols are computation-intensive and provide weaker security guarantees. To this end, we propose an efficient eCK secure mPKG-ID-AKA protocol based on the gap Diffie-Hellman assumption. Experimental analysis suggests that the proposed protocol attains the highest computation efficiency, when compared to the existing mPKG-ID-AKA schemes and is therefore highly suitable for securing inter-vehicular communications.

Smart Fuzzy Logic-Based Density and Distribution Adaptive Scheme for Efficient Data Dissemination in Vehicular Ad Hoc Networks

In vehicular Ad Hoc Networks (VANETs), smart data dissemination is crucial for efficient exchange of traffic and road information. Given the dynamic nature of VANET, the challenge is to design an adaptive multi-hop broadcast scheme that achieves high reachability while efficiently utilizing the bandwidth by reducing the number of redundant transmissions. In this paper, we propose a novel intelligent fuzzy logic based density and distribution adaptive broadcast protocol for VANETs. The proposed protocol estimates the spatial distribution of vehicles in the network employing the Nearest Neighbor Distance method, and uses it to adapt the transmission range to enhance reachability. To reduce packet collisions, the protocol intelligently adapts the contention window size to the network density and spatial distribution. Bloom filter technique is used to reduce the overhead resulting from the inclusion of the neighbor IDs in the header of the broadcast message, which is needed in identifying the set of potential rebroadcasting vehicles. Our simulation results confirmed the effectiveness of the proposed scheme in enhancing reachability while efficiently utilizing bandwidth.

Collaborative Content Downloading in VANETs with Fuzzy Comprehensive Evaluation

Vehicle collaborative content downloading has become a hotspot in current vehicular ad-hoc network (VANET) research. However, in reality, the highly dynamic nature of VANET makes users lose resources easily, and the transmission of invalid segment data also wastes valuable bandwidth and storage of the users’ vehicles. In addition, the individual need of each customer vehicle should also be taken into consideration when selecting an agent vehicle for downloading. In this paper, a novel scheme is proposed for vehicle selection in the download of cooperative content from the Internet, by considering the basic evaluation information of the vehicle. To maximize the overall throughput of the system, a collaborative content downloading algorithm is proposed, which is based on fuzzy evaluation and a customer’s own expectations, in order to solve the problems of agent vehicle selection. With the premise of ensuring successful downloading and the selection preferences of customer vehicles, linear programming is used to optimize the distribution of agent vehicles and maximize customer’s satisfaction. Simulation results show that the proposed scheme works well in terms of average quality of service, average bandwidth efficiency, failure frequency, and average consumption.

A Hierarchical Failure Detector Based on Architecture in VANETs

The failure detector is one of the fundamental components for maintaining high availability of Vehicular Ad-hoc Networks (VANETs). However, the dynamic nature of VANETs caused by the high mobility of vehicles and communication link failures has a serious impact on the performance of failure detection. Therefore, it is very meaningful to design a suitable failure detector that can deal with the dynamic nature of VANETs well. In this paper, we propose a hierarchical failure detector based on the architecture of VANETs. This failure detector can adapt to the dynamic network conditions and meet the different Quality of Service (QoS) requirements of multiple applications in VANETs. Different from existing failure detectors, we propose a failure detector that employs a detection-result sharing mechanism and groups the nodes according to the architecture of VANETs. We evaluate our proposed failure detector by using NS2 and GT-ITM to simulate the work environment of VANETs. The experimental result shows that our proposed failure detector can improve the detection time by at most 45% and the detection accuracy by at most 25% under similar detection overhead.

A Self-Adaptive and Link-Aware Beaconless Forwarding Protocol for VANETs

With the development of networks, Vehicular Ad-hoc Networks (VANETs) which act as the emerging application enhance the potential power of networks on the traffic safety and the entertainment. However, the high mobility and the dynamic nature of VANETs lead to the unreliable link which causes unreachable transmission and degrades the performance of the routing protocol in terms of the quality of experience (QoE). To provide a reliable routing for the data transmission, a self-adaptive and link-aware beaconless forwarding (SLBF) protocol is proposed. Based on the receiver based forwarding (RBF) scheme, SLBF designs a self-adaptive forwarding zone which is used to make the candidate nodes accurate. Furthermore, it proposes a comprehensive algorithm to calculate the waiting time by taking the greedy strategy, link quality, and the traffic load into account. With the NS-2 simulator, the performance of SLBF is demonstrated. The results show that the SLBF makes a great improvement in the delivery ratio, the end-to-end delay, and the average hops.

An Efficient Prediction-Based Data Forwarding Strategy in Vehicular Ad Hoc Network

As an important infrastructure of intelligent transportation system (ITS), vehicular ad hoc networks (VANETs) provide navigation and positioning, information services, driver assistance, in-vehicle entertainment, and other functions for the owners improving the development of the future smart city. Due to the high dynamic nature of VANETs, choosing a relatively stable and reliable transmission path to reduce the link breakage is obviously very important. In this paper, we propose an efficient prediction-based data forwarding strategy to ensure the reliability and efficiency of data transmission in VANETs. Besides the link lifetime prediction, we introduce a new metric named link utility used in the process of forwarder selection, in order to improve data forwarding efficiency. Link utility reflects the impact of relative velocity and distance between the vehicles on the efficiency of intervehicle data transmissions. It can be used to optimize packet routing and minimize number of hops especially in the high-density VANETs. Finally, we evaluate the performance of the proposed forwarding strategy through simulation confirming its feasibility and effectiveness.

Comparative Analysis of Different Data Dissemination Techniques based Genetic Algorithm and Fuzzy in Vehicular Adhoc Networks (VANETs)

In today‟s world Vehicular adhoc network done promising job towards public safety and provide important element to the transport facility. A Vehicular adhoc network is a new technology which has garnered enormous attention in recent years.VANETs is a special class of MANETs which uses vehicles as a mobile node. It uses the Intelligent transportation system in which vehicles can communicate with each other to avoid large number of problems such as real-time traffic problem, parking availability problem etc. The communication among the vehicles is at greater risk because the messages are broadcasted by wireless channel and vehicles move with high mobility. VANET does not have any fixed infrastructure Data dissemination is the tough job among these vehicles. While driving, a large amount of data and information are accessible to everyone. Many attractive applications over vehicular ad hoc network (VANETs) need data to be transmitted to the remote destinations through multiple paths, but some unique characteristics of VANETs incur unstable data delivery performances. Data dissemination in VANETs is more challenging because vehicles are highly mobile. Efficient data dissemination to a desired number of receivers in a vehicular ad hoc network (VANET) is a new issue and a challenging one considering the dynamic nature of VANETs. To overcome such situation and achieve efficient data dissemination among these vehicles different techniques are used. This paper represents a simple and robust dissemination technique that efficiently deals with data dissemination where the density of roadside base stations and vehicles distribution are both high. This technique divides the users in two categories premium user as well as free users. This paper illustrates three schemes such as fuzzy inference system, genetic algorithm scheme and hybrid of fuzzy inference and genetic algorithm. Two types of users have been taken in this paper.

Efficient Data Dissemination Techniques in VANETs: A Review

Vehicular ad hoc network (VANETs) being a sub class of MANET is a challenging field of wireless communication. It is a sensible way of using infrastructure fewer networks augmented with Intelligent Transportation System (ITS). It is an emerging trend now days for vehicles to be equipped with an embedded on-board computing unit with communication capabilities to enhance the overall driving experience. Such a system enables vehicle to vehicle and vehicle-toinfrastructure communication and provides vehicles with upto-date route and traffic information. Due to its highly dynamic topology as well as occurring often disconnection among vehicles, various categories of protocols have designed for its efficient and reliable working. While driving, a large amount of data and information are accessible to everyone. Many attractive applications over vehicular ad hoc network (VANETs) need data to be transmitted to the remote destinations through multiple paths, but some unique characteristics of VANETs incur unstable data delivery performances. Efficient data dissemination to a desired number of receivers in a vehicular ad hoc network (VANET) is a new issue and a challenging one considering the dynamic nature of VANETs. This paper presents an overview on simple and robust dissemination technique that efficiently deals with data dissemination in both dense and sparse vehicular networks. This technique divides the users in two categories and it takes three cases.

DOVE: Data Dissemination to a Desired Number of Receivers in VANET

Efficient data dissemination to a desired number of receivers in a vehicular ad hoc network (VANET) is a new issue and a challenging one considering the dynamic nature of VANETs. We aim to accurately control the number of receivers in a particular area of interest, achieve low dissemination delay, and incur only small communication overhead. To achieve these goals, we designed the data dissemination to a desired number of receivers in VANET (DOVE) scheme. DOVE is inspired by processor scheduling treating roads as processors to optimize the workload assignment and improve the efficiency of on-road dissemination. DOVE reaches the desired number of receivers with little inaccuracy and minimizes the dissemination delay with low communication overhead. We enhance our protocol with workload backup to deal with vehicles that are quitting the network. We utilize the unique characteristics of VANET and propose heuristics accordingly to significantly reduce the dissemination delay and overhead. Simulation results show that our scheme can disseminate data to almost all the pregiven number of receivers with a light overhead and low delay.

Analysis of Acceleration Effect in Data Dissemination in Vehicular Networks Using Rateless Codes

Mobility models of nodes have an important role in the evaluation of data dissemination protocols in vehicular ad hoc networks (VANETs). Many recent researches have used the constant velocity mobility models while vehicles have acceleration and their speeds change as they move. Because of the dynamic nature of VANETs, the network changes from a densely connected to the sparsely connected environment in a short time. In sparsely connected networks, it is said that vehicles move in clusters. In this paper, the constant acceleration mobility calculations for the sparsely connected network and its characteristics have been presented. These characteristics are usable for the evaluation of the data dissemination protocols in VANETs. The results show that acceleration affects the number of viewed clusters during the trip and the number of vehicles within a cluster. In fact, acceleration has a significant impact on the network sparsely connection. This matter shows that the appropriate data dissemination protocol should be used to study the acceleration effect which is applicable in sparse networks. The analysis in this research provides the necessary background for better understanding and accurate calculations for the evaluation of data dissemination in VANETs.