A Study on Communication System in VANET

Adrian, Ronald; Sulistyo, Selo; Mustika, I Wayan

doi:10.1109/icstc.2018.8528640

Cited by 10 publications

(6 citation statements)

References 45 publications

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“…Te simulation is done using MATLAB as a network simulator, SUMO as a trafc simulator [35], and with the help of TraCI [36]. Te area considered for the study is the real map of Iraq's capital, Baghdad, with latitude � 33.3730 °N and longitude � 44.3960 °W; it is extracted from OSM, Figure 4.…”

Section: Performance Evaluation and Simulation Resultsmentioning

confidence: 99%

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

Jabbar

Trabelsi

2023

Journal of Electrical and Computer Engineering

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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.

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Section: Performance Evaluation and Simulation Resultsmentioning

confidence: 99%

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

Jabbar

Trabelsi

2023

Journal of Electrical and Computer Engineering

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“…The determination of cluster size in VANET may use a bio-inspired algorithm [10]. The latest algorithm in the bio-inspired algorithm is moth flame optimization (MFO) invented by Mirjalili in 2015 [11].…”

Section: Related Workmentioning

confidence: 99%

“…It ignored the phenomenon of border clusters, although this algorithm had the best convergence time among previous bio-inspired algorithms such as particle swarm optimization and bat algorithm [2]. A comprehensive study of the bio-inspired algorithm told that PSO is the forerunner of the other intelligent swarm algorithm such as bee foraging optimization (BFO) and ant colony optimization (ACO) in VANET [10]. Moreover, a method in cluster quality improvement introduced in 2019 used the coalitional game technique with an aim to improve the quality of the signal to noise ratio (SNR) and channel capacity by using the Shannon theorem [13].…”

Section: Related Workmentioning

confidence: 99%

ABNC: Adaptive Border Node Clustering Using Genes Fusion Based on Genetic Algorithm to Support the Stability of Cluster in VANET

Adrian¹,

Sulistyo²,

Mustika³

et al. 2020

IJIES

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The clustering method in the vehicular ad-hoc network provides an opportunity for a cluster head to improve the network connections, but it still remains a problem in the border cluster. The vehicles, in view of their position in the slices of two clusters, receive a weak signal from the cluster head. The intersection area causes a throughput decrease for cluster members. To provide the network connection in the border cluster, we, in turn, proposed an adaptive border node clustering by utilizing the combination of K-Medoids algorithm, modified Genetic Algorithm and modified Tabu Search. We ameliorated the recent model in Enhanced Model of Weighted K-Medoids Clustering Algorithm by adding the fusion process to the best gene pieces into one individual temporary. The effects of this fusion were on the production of a temporary cluster that can make the main cluster environment more stable. This temporary cluster was adapted to the position of the two closest main cluster heads. Our model was found able to increase throughput and to keep the stability of cluster members at any velocities. The increase in throughput represents an improvement parameter in the network quality of service (QoS). We achieved the overall throughput at 93.97% (throughput vs cluster member) compared with the previous methods and the cluster member stable at around 25 vehicles (cluster member vs transmission range) in varied conditions.

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“…Our approach exhibits a low communication overhead compared to the existing approaches used for comparison. 33 The communication overhead is determined based on the number of broadcast packets that are created to report other nodes about the Sybil nodes. 10 Our approach does not increase the number of packets required for attacker information propagation as it reports about the Sybil attacker nodes only to the central VANET server, not to the neighboring nodes.…”

Section: Comparison Of Our Approach With Various Other Existing Mecmentioning

confidence: 99%

“…The approach performs the on‐road verification of the vehicle by checking the presence of the vehicle's identity in the reward table, and hence there is no key maintenance or key exchange. Our approach exhibits a low communication overhead compared to the existing approaches used for comparison 33 . The communication overhead is determined based on the number of broadcast packets that are created to report other nodes about the Sybil nodes 10 .…”

Section: Simulation and Performance Evaluationmentioning

confidence: 99%

A collaborative strategy for detection and eviction of Sybil attacker and Sybil nodes in VANET

Krishnan

Kumar

2020

Int J Communication

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Summary Vehicular ad hoc network (VANET) is a part of the intelligent transportation system (ITS) that provides safety and nonsafety applications. The high mobility of vehicles and the wireless communication environment in VANET makes it vulnerable to various attacks. One among them is the Sybil attack, where a Sybil attacker creates multiple fake identities called Sybil nodes that disrupt the functionality of VANET. Most of the existing solutions in the literature discuss identifying the Sybil nodes (virtual); very few works exist to determine the Sybil attacker (source node that generates Sybil nodes). In this paper, we propose a computation less heuristic approach that focuses on detecting the Sybil attacker and its Sybil nodes using signal strength measurements and Euclidean distance as the detection parameters. The central VANET server, Road Side Units (RSUs), and vehicles collaborate in the detection process, which improves the accuracy of our approach. The core of the approach is a reward‐based system, where the vehicle rewards are determined by collecting RSUs' feedback about the vehicle behavior. From simulation experiments, it is evident that our proposed approach achieves a maximum detection rate of 99.89% and a false positive rate of 0.012% than the existing techniques.

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A Study on Communication System in VANET

Cited by 10 publications

References 45 publications

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

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

ABNC: Adaptive Border Node Clustering Using Genes Fusion Based on Genetic Algorithm to Support the Stability of Cluster in VANET

A collaborative strategy for detection and eviction of Sybil attacker and Sybil nodes in VANET

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