SAMNET: Self-adaptative multi-kernel clustering algorithm for urban VANETs

Sellami, Lamaa; Alaya, Bechir

doi:10.1016/j.vehcom.2021.100332

Cited by 23 publications

(12 citation statements)

References 35 publications

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“…Sellami and Alaya [56] inquest the unpredictable density of conveyance and also furnish the attestation in the determinate load balance and other resources attainable between the distinct VANET networks for conveyances. The self-adaptive multikernel clustering for urban VANET (SAMNET) approach is postulated on a designated data that can be measured by depicting the ambivalent density of conveyance nodes, deceleration, acceleration, and bounded radio ranges for communication.…”

Section: Related Workmentioning

confidence: 99%

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Kothai¹,

Poovammal²,

Dhiman³

et al. 2021

Wireless Communications and Mobile Computing

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The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short-term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance.

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Section: Related Workmentioning

confidence: 99%

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Kothai¹,

Poovammal²,

Dhiman³

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…The common approach is to define a kernel for each view and then combine these kernels in a convex combination ( e.g. , Sellami & Alaya, 2021 ; Lu, Liu & Wei, 2020 ; Nithya, Appathurai & Venkatadri, 2020 ). Therefore, one main problem of this method is to choose kernel functions and optimal.…”

Section: Related Workmentioning

confidence: 99%

Dynamic guided metric representation learning for multi-view clustering

Zheng

Zhang

Wang

2022

PeerJ Computer Science

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Multi-view clustering (MVC) is a mainstream task that aims to divide objects into meaningful groups from different perspectives. The quality of data representation is the key issue in MVC. A comprehensive meaningful data representation should be with the discriminant characteristics in a single view and the correlation of multiple views. Considering this, a novel framework called Dynamic Guided Metric Representation Learning for Multi-View Clustering (DGMRL-MVC) is proposed in this paper, which can cluster multi-view data in a learned latent discriminated embedding space. Specifically, in the framework, the data representation can be enhanced by multi-steps. Firstly, the class separability is enforced with Fisher Discriminant Analysis (FDA) within each single view, while the consistence among different views is enhanced based on Hilbert-Schmidt independence criteria (HSIC). Then, the 1st enhanced representation is obtained. In the second step, a dynamic routing mechanism is introduced, in which the location or direction information is added to fulfil the expression. After that, a generalized canonical correlation analysis (GCCA) model is used to get the final ultimate common discriminated representation. The learned fusion representation can substantially improve multi-view clustering performance. Experiments validated the effectiveness of the proposed method for clustering tasks.

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“…The packet loss ratio is the number of lost packets divided by the total number of packets sent [ 27 ]. All packets have the most time before they must be executed, and if they do not execute, packet loss is minimized [ 28 , 29 ].…”

Section: Simulation Modelmentioning

confidence: 99%

An optimized location service for the fifth generation VANETs inspired by traffic lights

2021

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Vehicular ad-hoc networks (VANETs) address a steadily expanding demand, particularly for public emergency applications. Real-time localization of destination vehicles is important for determining the route to deliver messages. Existing location administration services in VANETs are classified as flooding-based, flat-based, and geographic-based location services. Existing localization techniques suffer from network disconnection and overloading because of 5G VANET topology changes. 5G VANETs have low delay and support time-sensitive applications. A traffic light-inspired location service (TLILS) is proposed to manage localization inspired by traffic lights. The proposed optimized localization service uses roadside units (RSUs) as location servers. RSUs with the maximum traffic weight metrics were chosen. Traffic weight metrics are based on speed of vehicles, connection time and density of neighboring vehicles. The proposed TLILS outperforms both Name-ID Hybrid Routing (NIHR) and Zoom-Out Geographic Location Service (ZGLS) for packet delivery ratio (PDR) and delay. TLILSs guarantee the highest PDR (0.96) and the shortest end-to-end delay (0.001 s) over NIHR and ZGLS.

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SAMNET: Self-adaptative multi-kernel clustering algorithm for urban VANETs

Cited by 23 publications

References 35 publications

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Dynamic guided metric representation learning for multi-view clustering

An optimized location service for the fifth generation VANETs inspired by traffic lights

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