A Survey of all Existing Clustering Protocols in VANETS but Main Emphasis of Survey Laid on Currently using Protocol i. e TCDGP

Dawande, Jyotsna Rao; Silakari, Sanjay; Deen, Anjna Jayant

doi:10.5120/20751-3146

Cited by 4 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“….+h n represents numerous observations of honesty that the RSU utilizes to evaluate the honesty of vehicles. (21) After evaluating the honesty parameter, the RSU calculates the behavior of a vehicle, which is a significant parameter because it ensures that a particular vehicle will not behave malicious after getting the higher degree of trust. Equation 21 demonstrates the process of evaluating the behavior factor of a vehicle.…”

Section: ) Reputationmentioning

confidence: 99%

“…The composition of clusters includes cluster heads (CH), cluster gateways, and members of clusters. These approaches are further divided into several classes that include predictive clustering [13], back-bone clustering [14], MAC-based clustering [15], traditional clustering [16], Hybrid clustering [17]- [19], and secure clustering [20], [21]. The predictive clustering utilizes geographic positions and behaviors of a node to determine the formation of a cluster.…”

Section: Introductionmentioning

confidence: 99%

“…There are many approaches proposed based on Medium Access Control (MAC) [15], [22], [23]. These MAC-based approaches use IEEE 802.11 MAC protocol [24] for the formulation of clusters [21]. A traditional clustering algorithm is sub-divided into active-based [25] and passive-based clustering [26], [27].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

StabTrust—A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks

et al. 2020

View full text Add to dashboard Cite

Vehicular Ad-hoc Network (VANET) is a modern era of dynamic information distribution among societies. VANET provides an extensive diversity of applications in various domains, such as Intelligent Transport System (ITS) and other road safety applications. VANET supports direct communications between vehicles and infrastructure. These direct communications cause bandwidth problems, high power consumption, and other similar issues. To overcome these challenges, clustering methods have been proposed to limit the communication of vehicles with the infrastructure. In clustering, vehicles are grouped together to formulate a cluster based on certain rules. Every cluster consists of a limited number of vehicles/nodes and a cluster head (CH). However, the significant challenge for clustering is to preserve the stability of clusters. Furthermore, a secure mechanism is required to recognize malicious and compromised nodes to overcome the risk of invalid information sharing. In the proposed approach, we address these challenges using components of trust. A trust-based clustering mechanism allows clusters to determine a trustworthy CH. The novel features incorporated in the proposed algorithm includes trust-based CH selection that comprises of knowledge, reputation, and experience of a node. Also, a backup head is determined by analyzing the trust of every node in a cluster. The major significance of using trust in clustering is the identification of malicious and compromised nodes. The recognition of these nodes helps to eliminate the risk of invalid information. We have also evaluated the proposed mechanism with the existing approaches and the results illustrate that the mechanism is able to provide security and improve the stability by increasing the lifetime of CHs and by decreasing the computation overhead of the CH re-selection. The StabTrust also successfully identifies malicious and compromised vehicles and provides robust security against several potential attacks. INDEX TERMS Intelligent transport system, security, vehicular ad-hoc networks, trust-based clustering, VANET attacks.

show abstract

Section: ) Reputationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

StabTrust—A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Clustering has been widely studied in VANETs: for clustering of vehicles (Chai et al, 2014;Hadded et al, 2015;Hussain and Bingcai, 2017;Rawashdeh and Mahmud, 2012;Yang et al, 2015) and improving routing (Cooper et al, 2017;Dawande et al, 2015;Malathi and Sreenath, 2017) and security (Bansal et al, 2016). The current work is in a way different from these works, as it clusters the messages rather than the vehicles, and focuses on feature selection, intuitive analysis and new approach to prototype machine learning algorithms in Veins (Sommer et al, 2011).…”

Section: Clusteringmentioning

confidence: 99%

Performance analysis of clustering for message classification and congestion control in DSRC/WAVE-based vehicular ad-hoc networks

Reddy

Ramanathan

2019

IJVICS

View full text Add to dashboard Cite

Vehicular Ad-Hoc Networks (VANETs) play a significant role in Intelligent Transportation Systems (ITS). The vehicles exchange awareness and safety information with surrounding vehicles using IEEE 802.11p Dedicated Short Range Communications (DSRC) and IEEE 1609 Wireless Access in Vehicular Environment (WAVE)-based short-range communication networks in 5.9 GHz band. These safety messages include periodic beacon messages and event driven emergency messages that are shared in Control Channel (CCH). By collecting these messages and applying clustering we can facilitate new applications like network congestion control, traffic control etc. The partitioning-based clustering methods like k-means and the Partitioning Around Medoids (PAM) are well known in data science. This paper focuses on understanding the message clustering intuitively. The clustering quality is measured using silhouette plot and average silhouette width. The simulation is carried out in OMNeT++ and SUMO-based Veins framework. A simple approach using a package called RInside is explored for fast prototyping of machine learning algorithms in OMNeT++ simulations.

show abstract

“…Traditionally, a way to choose a CH is to regard the first vehicle moving in a certain direction as CH, then Vehicles, within the predefined parameters to CH are grouped together, thus forming a multi-hop cluster. However, recently instead of just choosing the first vehicle as a CH, clustering mechanisms are calculated based on efficiency and stability of a vehicle (node) to its surrounding environment [4], [5], [6], [7], [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Vehicular Communication using Gaussian Interpolation of Cluster Head Selection (GI-CHS)

Iskandarani¹

2022

IJACSA

View full text Add to dashboard Cite

Decentralized and centralized vehicular communication is investigated in this work using Gaussian interpolation function with cluster head (CH) selection technique. The work uncovered that the best communication approach is to use both centralized and decentralized vehicular communication as combining them will achieve a much more uniform results as a function of communication radius values and vehicular speed. It is also found that vehicular speed contributes negatively to the efficiency of data communication if the relative speed of the vehicles to the communication radius is limited by their ratios. Mathematical expression is presented that relates probability of successful transmission to communication radius for both centralized and decentralized techniques with data proving the importance of the spread parameter within the Gaussian interpolation in a tabulated form, and explained to prove the adaptability of the function used. It is also shown in this work that weights affecting CH selection, thus using Gaussian interpolation is proved to be important as a weighting function in an a adaptive and dynamic vehicular ad-hoc networks (VANETS) covering both vehicle to vehicle (V2V), and vehicle to infrastructure (V2I) communication through cluster head selection.

show abstract

A Survey of all Existing Clustering Protocols in VANETS but Main Emphasis of Survey Laid on Currently using Protocol i. e TCDGP

Cited by 4 publications

References 45 publications

StabTrust—A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks

StabTrust—A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks

Performance analysis of clustering for message classification and congestion control in DSRC/WAVE-based vehicular ad-hoc networks

Dynamic Vehicular Communication using Gaussian Interpolation of Cluster Head Selection (GI-CHS)

Contact Info

Product

Resources

About