A Connectivity-Based Clustering Scheme for Intelligent Vehicles

Khan, Zahid; Koubâa, Anis; Fang, Sangsha; Lee, Mi Young; Muhammad, Khan

doi:10.3390/app11052413

Cited by 23 publications

(21 citation statements)

References 25 publications

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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%

“…Vehicle cluster generation is not a novel but a populated concept, and various scholars have already addressed it. A network of vehicles is presented as a graph, in which a vehicular node is connected with two other vehicular nodes [29,36,38]. This graphical representation may be suitable in sparse density, such as highways or minimally populated cities.…”

Section: A Why Is Vanet a Hypergraph Networkmentioning

confidence: 99%

“…For spectral clustering, this study employs the eigenvalues derived using VANET's hypergraph presentation. The idea is motivated by the connectivity graph eigenvalues in [29] and [36]. Both works in [29]and [36]were designed for the highway scenario, whereas our work is designed for the urban environment.…”

Section: ) Effect Of Different Traffic Densities On Hgcm Stabilitymentioning

confidence: 99%

“…The idea is motivated by the connectivity graph eigenvalues in [29] and [36]. Both works in [29]and [36]were designed for the highway scenario, whereas our work is designed for the urban environment. The eccentricity parameter in our work is inspired by the connectivity-based CH selection in [29].…”

Section: ) Effect Of Different Traffic Densities On Hgcm Stabilitymentioning

confidence: 99%

“…Khan et al [36] Highway Spectral clustering reduces the computational cost due to specified inter and intra clustering.…”

mentioning

confidence: 99%

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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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Section: B Research Problemsmentioning

confidence: 99%

Section: A Why Is Vanet a Hypergraph Networkmentioning

confidence: 99%

Section: ) Effect Of Different Traffic Densities On Hgcm Stabilitymentioning

confidence: 99%

Section: ) Effect Of Different Traffic Densities On Hgcm Stabilitymentioning

confidence: 99%

“…Khan et al [36] Highway Spectral clustering reduces the computational cost due to specified inter and intra clustering.…”

mentioning

confidence: 99%

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

Jabbar

Trabelsi

2022

IEEE Access

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Modularized hypergraph clustering scheme model for stableVANET

Kumar

Dahiya

Singh³

2022

Concurrency and Computation

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Summary VANET transforms vehicles into a social environment, allowing for maximum security and cooperative data transmission. Each vehicle is assigned a cluster, which transfers data to the cluster head (CH). The efficiency of a VANET network is determined by two factors: efficient vehicle groupings and optimal CH selection. This article proposes a novel modularized hypergraph clustering scheme in which the VANET is represented as a hypergraph network clustered using tensor trace maximization with the modularity matrix as the feed. CH is chosen from each cluster based on four vehicle parameters: vehicle speed about neighbors, consensus trust score, link lifetime, and connectivity level. For the final CH selection, all parameters are converted to eigenvalues. The fuzzy decision‐making scheme selects the best CH for all vehicles in that cluster based on a set of four parameters. The proposed algorithm for selecting the stable CH for improved network performance has also been validated and significantly improved using state‐of‐the‐art schemes.

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