Optimized Node Clustering in VANETs by Using Meta-Heuristic Algorithms

Ahsan, Waleed; Khan, Muhammad Fahad; Aadil, Farhan; Maqsood, Muazzam; Ashraf, Staish; Nam, Yunyoung; Rho, Seungmin

doi:10.3390/electronics9030394

Cited by 58 publications

(30 citation statements)

References 37 publications

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“…Their trust mechanism comprises the experience, reputation, and knowledge of the node. The optimization technique recently applied for VANET clustering in [34] is called Grasshoppers Optimization Algorithm (GOA). The GOA has designed for optimal CH selection using inter-vehicle distance and route length parameters for fitness computation.…”

Section: A State-of-art Methodsmentioning

confidence: 99%

TACA: Trust Aware Clustering using ACO for Secure and Reliable Vehicular Ah hoc Network Routing

Kadam

Vaze

Todmal

2021

Preprint

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In the modern era, the Vehicular Ad-hoc Network (VANET) received significant attention for information sharing among the societies. The emerging Internet of Things (IoT) for smart city perspective boosts the development of VANET based applications such as road safety and Intelligent Transport System (ITS). The efficiency of such networks is a widely studied research problem. The clustering has shown an efficient technique to address the challenges of VANET QoS and computational efficiency. The vehicles are grouped according to certain conditions to form the cluster. In this way, the entire network divides into different clusters. Each cluster consists of limited vehicles with its leader called Cluster Head (CH). But the major challenge for VANET clustering has related to the stability of the cluster. Due to high network dynamics, the unreliability for CH selection and data relaying becomes a security threat in VANET. To address such a security threat of VANET clustering, we proposed Trust-Aware Clustering using Ant Colony Optimization (TACA) protocol. For each cluster, an ACO-based optimal CH selection algorithm applying different trust components of the vehicle. The ACO solves the problem of optimal CH selection with minimum control overhead and maximum CH lifetime. The optimal CH selected has been selected based on trust-aware ACO fitness function using the parameters such as vehicle speed, Degree of Connectivity (DoC), vehicle congestion, and Packet Relaying Probability (PRP). This mechanism enables clusters to select reliable CH to address the security concerns of VANET communications. The TACA protocol has been evaluated with recent similar methods, and the results demonstrate efficiency in terms of QoS and computational overhead of clustering.

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Section: A State-of-art Methodsmentioning

confidence: 99%

TACA: Trust Aware Clustering using ACO for Secure and Reliable Vehicular Ah hoc Network Routing

Kadam

Vaze

Todmal

2021

Preprint

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“… Researchers have made clusters and selected the CH by calculating the vehicles' behaviour in the network, velocity, moving direction and position in lanes. Fuzzy logic has been used, such as in [30,32], along with other heuristic algorithms, such as in [31,36]. Fuzzy logic schemes require tuned membership functions to decide for CH selection, which necessitate considerable experience and behaviour analysis of vehicles on a particular road [37].…”

Section: B Research Problemsmentioning

confidence: 99%

A Novelty of Hypergraph Clustering Model (HGCM) for Urban Scenario in VANET

Jabbar

Trabelsi

2022

IEEE Access

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A vehicular ad hoc network is a dynamic and constantly changing topology that requires reliable clustering to prevent connection failure. A stable cluster head (CH) prevents packet delay (PD) and maintains high throughput in the network. This article presents a two-fold novel scheme for stable CH selection. In the first part of the proposed scheme, the vehicle network is considered a one-to-many connection network, which is near to a practical scenario. The cluster generation is handled using a newly proposed vehicular-hypergraph-based spectral clustering model. In the second part, the CH is selected considering the criteria for maintaining a stable connection with the maximum number of neighbours. The new rewarding/penalising relative speed and neighbourhood degree fulfil the condition. Eccentricity assesses that the vehicle should be at the centre of the cluster. Another metric with deep learning spectrum sensing is introduced for CH selection. Trust calculation is performed using deep learning-trained spectrum sensing as a model. The primary vehicle in noisy and noiseless environments is recognised using layers of long short-term memory. A high trust score is awarded to the vehicle which vacates the spectrum in the sensing of the primary vehicle. The stable CH selected by these metrics reduces the overhead that occurs due to the frequent shifting of the CH from one vehicle to another. This has been validated by the improved CH stability; increased cluster member (CM) lifetime and reduced rate of change of CH. The proposed scheme also demonstrates a considerable improvement in PD and throughput.

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“…It was further applied to the objective of double head clustering algorithm in order to identify the superior configuration that attribute toward better clustering performance. A grasshopper optimization algorithm‐based node clustering technique (GOACT) was proposed by Ahsan et al 29 for optimal cluster head selection process in order to sustain network stability. This GOACT minimized the network overhead, even in the scenarios of unpredictable node density.…”

Section: Related Workmentioning

confidence: 99%

Border Collie optimization algorithm‐based node clustering technique in vehicular ad hoc networks

Godi¹,

Janakiraman²

2022

Trans Emerging Tel Tech

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In vehicular ad hoc networks (VANETs), the nodes are vehicles and these vehicles communicate with one another on the road. The vehicular nodes on the road move continuously and it introduces dynamic changes in the topology of the network. When the node density in the vehicular network is high, then it is still more challenging as these situations introduce different challenges for network scalability and hurdles the process of determining an optimal route in the vehicular network. At this juncture, clustering protocols are often utilized for resolving the issue of network scalability and the factors that hinders process of identifying optimal routes in vehicular network. In this article, Border Collie optimization algorithm‐based node clustering technique (BCOA‐NCT) is proposed for achieving optimal cluster head selection that minimizes network overhead in uncertain node density situations. This BCOA‐NCT is proposed for facilitating equal concentration on both exploitation and exploration during the process of clustering. It utilized the strategy of eyeing for preventing the algorithm from being stuck into local optima during the cluster head selection process. The simulation experiments of the proposed BCOA‐NCT and the benchmarked schemes confirmed better throughput of 6.19%, minimized end‐to‐end delay of 7.24%, reduced mean energy consumptions of 6.89%, and reduction in the number of beacon messages utilized by 8.26% under the impact of different density of vehicular node. The outcome of the results indicated that the proposed BCOA‐NCT on par with the baseline approaches is capable enough in handling the issue of scalability and better estimation of optimal routes under the increase of node density in the vehicular network.

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Optimized Node Clustering in VANETs by Using Meta-Heuristic Algorithms

Cited by 58 publications

References 37 publications

TACA: Trust Aware Clustering using ACO for Secure and Reliable Vehicular Ah hoc Network Routing

TACA: Trust Aware Clustering using ACO for Secure and Reliable Vehicular Ah hoc Network Routing

A Novelty of Hypergraph Clustering Model (HGCM) for Urban Scenario in VANET

Border Collie optimization algorithm‐based node clustering technique in vehicular ad hoc networks

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