Vehicular Ad Hoc Networks Clustering Research Articles

Maximizing VANET performance in cluster head selection using Intelligent Fuzzy Bald Eagle optimization

Vehicular Ad-hoc Network (VANET) is a type of wireless network that allows communication among vehicles and roadside units to develop advanced intelligent transportation systems. The success of VANETs depends on the stability of wireless communication, among vehicles, which is challenging to achieve due to high vehicle speed, rapidly changing topology, and unstable communication links. Moreover, the instability of the network caused by the mobile nature of vehicles in VANET reduces the performance of the network. Clustering in VANETs is a crucial technique that organizes the network and forms the basis of the routing protocol. Clustering algorithms are designed for VANETs to work efficiently and require several phases that must be integrated into the process before a clustering decision can be made. Therefore, this paper presents a novel Intelligent Fuzzy Bald Eagle (IFBE) optimization to enhance the performance of VANETs by optimizing the cluster head selection (CHS) process. The experimental results prove that the IFBE approach outperforms other existing mechanisms in terms of energy consumption, end-to-end delay, and packet delivery ratio. The proposed mechanism utilizes an intelligent fuzzy system to optimize the CHS process. The fuzzy system uses a set of rules and membership functions to evaluate the candidate nodes' suitability for being cluster heads. The Bald Eagle Search (BES) optimization meta-heuristic algorithm is used to find the optimal values for the fuzzy system's membership functions. The proposed mechanism was evaluated using the MATLAB simulator. Finally, the experimental result proved that the IFBE approach achieved minimum delay and energy consumption of 13.58 ms, and 15.5 J, and higher clustering efficiency and packet delivery rate of 94.15% and 97.65% respectively, which show that it performs better than other existing approaches.

Stable dynamic feedback-based predictive clustering protocol for vehicular Ad hoc networks

Scalability presents a significant challenge in vehicular communication, particularly when there is no hierarchical structure in place to manage the increasing number of vehicles. As the number of vehicles increases, they may encounter the broadcast storm problem, which can cause network congestion and reduce communication efficiency. Clustering can solve these issues, but due to high vehicle mobility, clustering in vehicular ad hoc networks (VANET) suffers from stability issues. Most of the existing clustering protocols are optimized for highways only and some protocols are optimized for intersections only. The clusters that are created by the protocols which are optimized for highways breakdown at the intersections where clustering is needed more. On the other hand, the clusters that are created by the protocols optimized for intersections create extra clusters or break unnecessary on straight-roads which reduces cluster stability. Moreover, the lack of intelligent use of a combination of the mobility parameters, such as direction, movement, position, velocity, degree of vehicle, and movement at the intersection, also contributes to cluster stability problems. A dynamic clustering protocol that efficiently combines all the mobility parameters can resolve these issues in VANETs by providing stability in both intersections and highways. To achieve high stability in VANET clustering, a Stable Dynamic feedback-based Predictive Clustering (SDPC) protocol is proposed for VANET to ensure cluster stability in both highway and intersection scenarios. SDPC considers vehicle relative velocity, acceleration, position, distance, transmission range, moving direction at the intersection, and vehicle density to create a cluster. The cluster head is selected based on the future position of the vehicles, relative distance between vehicles, movement of vehicles at the intersection, degree of vehicles, and probable cluster head duration. The performance of SDPC is compared with four existing VANET clustering protocols in various road topologies, in terms of the average cluster head change rate, the average duration of cluster head, the average duration of cluster member, and the average clustering overhead. The simulation results show that SDPC outperforms the existing protocols, achieving higher clustering stability in terms of cluster head change rate (50%), cluster head duration (15%), cluster member duration (6%), and clustering overhead (35%).

Inspecting VANET with Various Critical Aspects – A Systematic Review

Due to its strategic significance in the area of smart transportation to facilitate Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, Vehicular Ad Hoc Networks (VANETs) have grown very popular in recent years. The rapid increase in the number of vehicles on the road has also led to the development of heterogeneous, large-scale, and highly dynamic VANETs, which introduce difficulty to fulfill the stringent requirements, which include low latency, high mobility, top security, and massive connectivity of the 5G network. Research works in VANETs mainly focus on message transmission within strict delay requirements based on different applications, data privacy and security. In this respect, a number of studies have been carried out by the researchers that propose models and solutions linked to the enhancement of VANET from several aspects, including applications, Quality of Service (QoS), security, physical layer fading, Artificial Intelligence (AI) techniques, Medium Access Control (MAC), and routing protocols. These factors serve as the driving force for this study's thorough examination of VANETs, which includes information on specific applications, QoS, channel fading, MAC protocols, channel access mechanisms, routing protocols, security, and difficulties. None of the surveys in existence today address all critical aspects of VANET in a single survey. In this paper, a complete taxonomy of VANETs has been provided based on various issues. First, an overview of VANET is presented with different applications. Then, QoS in VANETs and different proposed MAC protocols are discussed. Channel fading and access mechanisms are presented. After that, routing protocols, security, and clustering in VANETs are provided. AI approaches proposed in VANET are also summarized. Finally, a discussion of future research direction for all aspects is presented. This article might be used as a guide or a point of reference while designing and creating applications, networking and communication systems, and data security for VANETs.

Clustering Algorithms and Comparisons in Vehicular Ad Hoc Networks

Vehicular Ad hoc Network (VANET) is a new era in the transmission of dynamic information across communities. Intelligent Transportation Systems is only one of the many applications for VANET (ITS). The topology of VANET is extremely dynamic, and connections are irregular. These features cause information transmission in the VANET to be unreliable. Vehicle clustering is a successful strategy to increase the network's scalability and connection dependability. Characteristics of the VANET have an impact on clustering performance as well. An extensive explanation of VANET clustering algorithms is given in this article. A complete evaluation of clustering in VANETs is provided based on the clustering procedure. Most methods examine the clustering process in terms of Cluster Head selection metrics, formation, and its maintenance. The clustering methods are contrasted based on factors such as stability, convergence, overhead, and latency. There is also discussion of some of the most typical issues and the solutions used. Also, a summary of the performance metrics used to assess clustering algorithms is provided.

Harris Hawks Optimization-Based Clustering Algorithm for Vehicular Ad-Hoc Networks

Vehicular ad-hoc network (VANET) is highly dynamic due to the high speed and sparse distribution of vehicles on the road. This creates major challenges (e.g., network fragmentation, packet routing) for the researchers to enable robust, reliable, and scalable communication, especially in a highly dense network. Clustering in VANET is one of the remedies to address the scalability issue. However, it is observed in the literature, that existing clustering techniques produce a high number of clusters for the vehicular environment. Consequently, it increases the consumption of scarce resources in a wireless network. Furthermore, it also increases the communication overhead as well as the number of hops for data routing. As a result communication latency also increases and the reliability of communication protocol decreases. So it is highly desirable to find out the optimal clusters for a given vehicular environment. As finding optimal clusters is a multi-objective combinatorial optimization problem, therefore by employing nature-inspired meta-heuristic algorithms we can optimize the multi-objective problem. To this end, we proposed a novel clustering algorithm based on the Harris Hawks Optimization (HHO) algorithm for VANET (HHOCNET). HHO algorithm is a nature-inspired meta-heuristic algorithm inspired by the foraging maneuver of hawks called surprise pounce. The proposed framework imitates the cooperative foraging maneuver of hawks (i.e., surprise pounce for creating optimized vehicular clusters). The stochastic operators of the HHO algorithm and proper maintenance of the equilibrium state between the operations of exploration and exploitation enable the proposed algorithm to escape from the local optima and provide a globally optimal solution (i.e., the optimal number of vehicular clusters). Simulations are performed in MATLAB and the results are compared with the state-of-art schemes (i.e., Gray Wolf optimization-based clustering algorithm (GWOCNET), Multi-objective Particle Swarm Optimization (MO, PSO), and Comprehensive Learning Particle Swarm Optimization (CLPSO)) using different performance metrics. The results demonstrate that the proposed approach is an effective approach for clustering in VANET and outer performs the other benchmark algorithms in terms of optimizing the multi-objective clustering problem. HHOCNET algorithm selects 36.04% of nodes as cluster heads while the existing state-of-the-art schemes are providing 50.42%, 56.7%, and 60.89% for GWOCNET, CLPSO, and Multi-objective Particle Swarm Optimization (MOPSO). The proposed HHOCNET algorithm enhances the performance of the vehicular network by up to 15%. Consequently, it increases network efficiency by reducing the consumption of the required wireless resources. It also reduces the number of hops for packet routing. Hence it achieves a minimum end-to-end communication latency.

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>

EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET

A smart city’s vehicular communication strategy is important. A significant problem with vehicular communication is scalability. Clustering can help with vehicular ad hoc network (VANET) problems; however, clustering in VANET faces stability problems because of the rapid mobility of the vehicles. To achieve high stability for the VANET, this paper presents a new efficient Eigen-trick-based hypergraph stable clustering algorithm (EtHgSC). This algorithm has a twofold scheme for stable CH selection. In the first part of the proposed scheme, the cluster generation is handled using an improved hypergraph-based spectral clustering algorithm using the Eigen-trick method. The “Eigen-trick” method is used to partition both vertices and hyperedges, which provides an approach for reducing the computational complexity of the clustering. The cluster head (CH) is chosen in the second part, taking into account the requirements for keeping a stable connection with most neighbors. In addition to relative speed, neighboring degree, and eccentricity that are used to select the CH, the vehicle time to leave metric is introduced to increase the CH stability. The grey relational analysis model is used to find each vehicle’s score, and the CH is selected based on the maximum vehicle’s score. The results show the supremacy of our proposed scheme in terms of CH lifetime, cluster member (CM) lifetime, and the change rate of CH. Also, the proposed scheme achieves a considerable reduction in terms of packet delay.

A Region-Based Collaborative Management Scheme for Dynamic Clustering in Green VANET

Green Vehicular Ad-hoc Network (VANET) is a newly-emerged research area that focuses on reducing harmful impacts of vehicular communication equipment on the natural environment. Recent studies have shown that grouping vehicles into clusters for green communications in VANETs can significantly improve networking efficiency and reduce infrastructure costs. As a dynamic network system, maintaining the network connectivity and reducing the communication overlap are two critical challenges for green VANET clustering. However, most existing work studies connectivity and overlap separately, lacking a deep understanding of the relationship between them. To address this issue, we present a comprehensive analysis that jointly considers the two critical factors in one model. Specifically, we first design a state resemblance prediction (SRP) model based on the historical trajectory feature relevance between vehicles; Combined with the SRP model, we propose the region-based collaborative management scheme (RCMS) to establish the dynamic clustering; Lastly, we take extensive experiments to verify the region-based collaborative management scheme for dynamic clustering. The results demonstrate that the proposed clustering algorithm can achieve high networking efficiency and better communication stability.

Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and Solutions

The Vehicular Ad-hoc Network (VANET) represents a new future for dynamic information dissemination between societies. VANET has a wide range of applications in a variety of aspects, including Intelligent Transportation Systems (ITS). VANET has some characteristics, like highly dynamic topology and intermittent connections. These characteristics lead to untrustworthy information transmission in VANET. Vehicle clustering is an efficient approach to improve the scalability of the network and connection reliability. The performance of the clustering is also affected by VANET characteristics. This article provides a comprehensive description of VANET clustering algorithms. The most notable clustering algorithms introduced between 1999 and 2021 are reviewed. A complete survey on clustering in VANETs is provided based upon the clustering process. The clustering process in most algorithms is explored in the aspects of CH selection metrics, cluster formation, and cluster maintenance. The clustering techniques based on some parameters like stability, convergence, overhead, and latency are compared. Some of the most common problems, as well as the approaches employed to solve them, are also discussed. Also, the performance parameters which evaluate the clustering approaches are summarized.

Cauchy Density-Based Algorithm for VANETs Clustering in 3D Road Environments

Vehicular ad hoc networks (VANETs) are emerging to serve various types of applications for serving smart cities and intelligent transportation systems. There are several challenging factors for ensuring reliable and stable VANETs communications. VANETs clustering is essential functionality to serve routing protocols and in enable reliable VANETs. Clustering algorithms for VANETs operate in decentralized mode, which requires incorporating additional stages before deciding the clustering decisions and might create suboptimality due to the local nature of the decentralized approach. In addition, the challenging architecture of the road environment can cause confusing clustering decisions. This problem becomes more challenging due to the evolving nature of clusters in VANETs in general and in 3D VANETs in particular. This paper tackles the problem of 3D VANETs using a centralized clustering model based on the developed Cauchy density model. The development has included a traffic generation model, mobility model for generating driving behavior, and an algorithm for enabling modeling of road curvature based on an adjacency list that defines the road’s points that define a straight-line segment. In addition, a clustering algorithm that defines mobility vector and uses Cauchy-based density for enabling adding vehicles to their respective clusters. For evaluation, conducted scenarios in three locations with 3D architecture are considered, and a comparison with benchmarks shows the superiority of our developed over the benchmarks with an improvement percentage of 1%, 10%, and 3% for average cluster head duration, average cluster member duration, and clustering efficiency respectively.

Remora optimization algorithm-based optimized node clustering technique for reliable data delivery in VANETs

Vehicular ad hoc Network (VANET) is one of the recently growing trends which motivate the provision of several service providers in the urban areas. In VANETs, the vehicles represent the nodes in the network topology that needs to guarantee better cooperation when there is a higher node density. Moreover, the problem of determining an optimal route and achieving network scalability is a herculean task. In this context, the incorporation of a potential clustering algorithm has the possibility of improving the road safety and facilitating a reliable option of promoting message routing. The clustering protocols are determined to be the ideal candidate for solving the problems of network scalability to guarantee reliable data dissemination. In this paper, Remora optimization algorithm-based Optimized Node Clustering (ROAONC) Technique is proposed for node clustering in VANETs to achieved optimal CH selection process. This ROAONC scheme was proposed for minimizing network overhead in the scenarios of unpredictable node density. The simulation results of this ROAONC scheme confirmed better performance in terms of transmission range, node density, network area and number of clusters compared to the competitive ant colony, grey wolf, grasshopper, and dragonfly optimization algorithm-based clustering protocols.

Intrusion Detection System for Energy Efficient Cluster Based Vehicular Adhoc Networks

A vehicular ad hoc network (VANET), a subfield of mobile adhoc network (MANET) is defined by its high mobility by demonstrating the dissimilar mobility patterns. So, VANET clustering techniques are needed with the consideration of the mobility parameters amongst the nearby nodes for constructing the stable clustering techniques. At the same time, security is also a major design issue in VANET, this can be resolved by the intrusion detection systems (IDS). In contrast to the conventional IDS, VANET based IDS are required to be designed in such a way that the functioning of the system does not affect the real-time efficiency of the performance of VANET applications. With this motivation, this paper presents an efficient Fuzzy Logic based Clustering with optimal fuzzy support vector machine (FSVM), called FLC-OFSVM based on the Intrusion Detection System for VANET. The proposed FLC-OFSVM model involves two stages of operations namely clustering and intrusion detections. Primarily, FLC technique is employed for selecting an appropriate set of cluster heads (CHs) and for constructing the clusters. Besides, a lightweight anomaly IDS model named FSVM optimized with krill herd (KH) optimization algorithm is developed for detecting the existence of malevolent attacks in VANET. The KH algorithm based on the herding behavior of krills is used for optimally tuning the parameters of the FSVM model. In order to investigate the performance of the FLC-OFSVM model, an extensive set of simulations have been carried out and the results thus showcased that the OFSVM model has gained maximum outcome with an accuracy of 99.98%.

Design and performance evaluation of Improved DFACO protocol based on dynamic clustering in VANETs

This study focuses on Vehicular Ad-hoc Networks (VANETs) stability in an environment that is dynamic which often leads to major challenges in VANETs, such as dynamic topology changes, shortest routing paths and also scalability. One of the best solutions for such challenges is clustering. In this study, we present five novel routing protocols based on Dynamic Flying Ant Colony Optimization (DFACO) algorithm to achieve minimum number of clusters, high accuracy, minimum time and solution cost by selecting the best cluster-head which is obtained from a new mechanism of dynamic metaheuristic-based clustering. In this regard, major improvements are applied on classical DFACO by adjusting the procedure for updating the pheromone and tuning the evaporation rate that has a major role in DFACO. In this research two individual phases of experiments are conducted for performance evaluation of proposed routing protocols. The presented solution is verified and compared to classic Ant Colony Optimization (ACO), DFACO and ACO Based Clustering Algorithm for VANET (CACONET) algorithms in phase one; and compared to clustering algorithms such as Center Position and Mobility CPM), Highest-Degree algorithm (HD), Angle-based Clustering Algorithm (ACA) in phase two through NS-2 and SUMO simulation tools. Simulation results have confirmed the expected behaviour and show that our proposed protocols achieve better node connectivity and cluster stability than the former.

Clustering in vehicular ad hoc network: Algorithms and challenges

Clustering is an important concept in vehicular ad hoc network (VANET) where several vehicles join to form a group based on common features. Mobility-based clustering strategies are the most common in VANET clustering; however, machine learning and fuzzy logic algorithms are also the basis of many VANET clustering algorithms. Some VANET clustering algorithms integrate machine learning and fuzzy logic algorithms to make the cluster more stable and efficient. Network mobility (NEMO) and multi-hop-based strategies are also used for VANET clustering. Mobility and some other clustering strategies are presented in the existing literature reviews; however, extensive study of intelligence-based, mobility-based, and multi-hop-based strategies still missing in the VANET clustering reviews. In this paper, we presented a classification of intelligence-based clustering algorithms, mobility-based algorithms, and multi-hop-based algorithms with an analysis on the mobility metrics, evaluation criteria, challenges, and future directions of machine learning, fuzzy logic, mobility, NEMO, and multi-hop clustering algorithms.

State-of-the-art approach to clustering protocols in VANET: a survey

Vehicular ad hoc network (VANET) assists in improving road safety, traveller comfort, and intelligent transportation systems to a great extent. Dedicated short-range communications technology is specially designed for VANET to form communication among vehicles and vehicles to infrastructure. The lack of router devices in the flat vehicle-to-vehicle network structure of VANET may raise complicated issues like scalability, resource scarcity, reliability, and hidden terminal problem inside the network. The concept of vehicle clustering is introduced in VANET to improve network performance by handling all such issues. This paper provides an in-depth classification of clustering protocols in VANET based on their design objectives. Two broad categories, generalized and application dependent, are considered to review on clustering protocols in VANET. In generalized clustering protocol, cluster design aims to achieve their primary objective, i.e., the formation of a robust cluster having a long sustainable life, While in application dependent clustering protocols, cluster design aims to improve the performance of specific applications (i.e., target tracking, traffic estimation, misbehaviour detection, privacy preservation, certificate revocation, etc.) on various performance metrics. After this, the paper explores essential research contributions of each category comprehensively. Detailed analysis of existing research contributions is also provided after comparing them on many important metrics. Finally, the merits and demerits of existing protocols are also listed. Our attempt to encourage researchers of the concerned field by providing an extensive analysis of clustering in VANET.

Optimized Node Clustering in VANETs by Using Meta-Heuristic Algorithms

In a vehicular ad-hoc network (VANET), the vehicles are the nodes, and these nodes communicate with each other. On the road, vehicles are continuously in motion, and it causes a dynamic change in the network topology. It is more challenging when there is a higher node density. These conditions create many difficulties for network scalability and optimal route-finding in VANETs. Clustering protocols are being used frequently to solve such type of problems. In this paper, we proposed the grasshoppers’ optimization-based node clustering algorithm for VANETs (GOA) for optimal cluster head selection. The proposed algorithm reduced network overhead in unpredictable node density scenarios. To do so, different experiments were performed for comparative analysis of GOA with other state-of-the-art techniques like dragonfly algorithm, grey wolf optimizer (GWO), and ant colony optimization (ACO). Plentiful parameters, such as the number of clusters, network area, node density, and transmission range, were used in various experiments. The outcome of these results indicated that GOA outperformed existing methodologies. Lastly, the application of GOA in the flying ad-hoc network (FANET) domain was also proposed for next-generation networks.

Combination of HF set and MCDM for stable clustering in VANETs

Fast-moving nodes in vehicular ad hoc network (VANET) make network topology very dynamic, which deteriorates communication reliability and scalability. To overcome this problem, a hierarchical topology can be created using a clustering algorithm. This study presents a novel hesitant fuzzy (HF) multi-criteria ranking framework to deal with cluster-heads (CHs) election problem in VANET. An analogy is suggested between ‘ranking of HF elements’ and ‘CH selection in VANET’. Within the proposed framework, a multiple criteria decision-making (MCDM) method should be used to calculate vehicle eligibility to become CH. In this study, technique for order of preference by similarity to ideal solution (TOPSIS), Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) and EVAluation of MIXed data (EVAMIX) MCDM methods are applied. A simulation study, under a highway scenario, is conducted to investigate the performance of HF-TOPSIS, HF-VIKOR and HF-EVAMIX-based clustering algorithms. Obtained results show that CH election based on HF-EVAMIX leads to more stable clustering in comparison with HF-TOPSIS, HF-VIKOR and the conventional threshold-based method.

Multi-Dimensional Affinity Propagation Clustering Applying a Machine Learning in 5G-Cellular V2X

Cellular systems are facing the ever-increasing demand for vehicular communication aimed at applications such as advanced driving assistance and ultimately fully autonomous driving. Cellular Vehicle to Anything (C-V2X) has become more applicable with the release of the first sets of 5G (5 th Generation) system specifications. The highly capable 5G systems will therefore support even a larger number of moving objects. This study aims to present a sophisticated clustering mechanism that enables cellular systems to accommodate a massive number of moving Machine Type Communication (MTC) objects with a minimum set of connections while maintaining system scalability. Specifically, we proposed Normalized Multi Dimension-Affinity Propagation Clustering (NMDP-APC) scheme and applied it for Vehicular Ad hoc Network (VANET) clustering. For VANET clustering formation, our study employed Machine Learning (ML) to determine the granularity, i.e., the size and span of clusters desirable for use in dynamic motion environments. The study achieved a sufficient level of prediction accuracy with fewer training data through a learned prediction function based on the selected key criteria. This paper also proposes a system sequence designed with a series of procedures fully compliant with C-V2X systems. We demonstrated substantial simulations and numerical experiments with theoretical analysis, specifically applying soft-margin-based Support Vector Machine (SVM) algorithm. The simulation results confirmed that the granularity parameter we applied fairly controls the size of VANET clusters although vehicles are in motion and that the prediction performance has been adjusted through controlling of key SVM parameters.

Coalitional Game Theoretical Approach for VANET Clustering to Improve SNR

Clustering is considered as the potential approach for network management in vehicular ad hoc network (VANET). The performance of clustering is often assessed based on the stability of the clusters. Hence, most of the clustering methods aim to establish stable clusters. However, besides the stability of cluster, good link quality must be provided, especially when reliable and high-capacity transmission is demanded. Therefore, this paper proposes a clustering method based on coalitional game theory with the purpose to improve the average of vehicle-to-vehicle (V2V) signal-to-noise ratio (SNR) and channel capacity while maintaining the stability of the cluster. In the proposed method, each vehicle attempts to form a cluster with other vehicles according to coalition value. To attain the purpose of clustering, the value of coalition is formulated based on the V2V SNR, connection lifetime, and speed difference between vehicles. In fast-changing network topology, the higher average of SNR can be achieved but the stability of cluster becomes hard to be maintained. Based on the simulation results, SNR improvement can be adjusted in order to balance with the cluster stability by setting the parameters in the proposed method accordingly. Further simulation results show that the proposed method can obtain a higher average of V2V SNR and channel capacity than other relevant methods.

Enhanced Cluster Stability in Vehicular ad hoc Network

Traffic management has been and remains to be a major problem especially in urban areas with high vehicle density. Adoption of intelligent transport system (ITS) has been widely experimented with the intention of curbing traffic menace with mixed experiences as the outcome. By communicating with other vehicles travelling on the same road in form of clusters, vehicular ad hoc network (VANET) forms an ITS that can allow cooperation of vehicles with less human input. By considering the high mobility nature of the vehicles in VANET, this paper presents a solution to the main menace of VANET clustering by adopting the flexibility of fuzzy logics for cluster formation on a multilane urban highway. It also shows that the stability of clusters is improved by performing the cluster head (CH) selection process based on a combination of fuzzy logics, lane weighting, and utility function with the fuzzy membership function adjusted to increase stability of clusters.