Junction-based stable clustering algorithm for vehicular ad hoc network

Mukhtaruzzaman, Mohammad; Atiquzzaman, Mohammed

doi:10.1007/s12243-021-00881-9

Cited by 14 publications

(19 citation statements)

References 13 publications

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“…Although the CH stability of the method proposed by Mukhtaruzzaman et al [34] is 76%, we can conclude that our HGCM using hypergraph theory improves the clustering efficiency compared with other algorithms in terms of the number of clusters constructed with 72% of CH stability. The CH stability for the maximum number of clusters formed is reported in Table V.…”

Section: ) State-of-the-art Comparisonmentioning

confidence: 69%

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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: ) State-of-the-art Comparisonmentioning

confidence: 69%

“…In the article proposed by M. Mukhtaruzzama [34], clusters were generated by considering the moving direction of a vehicle at the junctions, vehicles' density, and transmission range. The CH was selected by relative position and time spent on the road.…”

Section: ) State-of-the-art Comparisonmentioning

confidence: 99%

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

Jabbar

Trabelsi

2022

IEEE Access

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“…Many clustering approaches are relying on a combination of multiple metrics rather than a single metric for selecting the CH, like DHC in [47], CBSC in [45], TCAR in [21], AWCP in [30], and EWCA in [44]. Some of these algorithms and their metrics used for CH selection are tabulated in Table 2: Proposed in [48] Trust, relative speed, and position [54] JCV Relative position, movement at the junction, degree of a node, and time.…”

Section: Clustering Process In Vanetsmentioning

confidence: 99%

“…In [54], the authors suggested a robust and dynamic mobility-based clustering approach JCV. It takes into account the moving direction at the next junction in the cluster formation process.…”

Section: Single-hop Clustering Algorithmmentioning

confidence: 99%

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Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and Solutions

Jabbar¹,

Trabelsi

2022

Int. J. Interact. Mob. Technol.

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The Vehicular Ad-hoc Network (VANET) represents a new future for dynamic information dissemination between societies. VANET has a wide range of applications in a variety of aspects, including Intelligent Transportation Systems (ITS). VANET has some characteristics, like highly dynamic topology and intermittent connections. These characteristics lead to untrustworthy information transmission in VANET. Vehicle clustering is an efficient approach to improve the scalability of the network and connection reliability. The performance of the clustering is also affected by VANET characteristics. This article provides a comprehensive description of VANET clustering algorithms. The most notable clustering algorithms introduced between 1999 and 2021 are reviewed. A complete survey on clustering in VANETs is provided based upon the clustering process. The clustering process in most algorithms is explored in the aspects of CH selection metrics, cluster formation, and cluster maintenance. The clustering techniques based on some parameters like stability, convergence, overhead, and latency are compared. Some of the most common problems, as well as the approaches employed to solve them, are also discussed. Also, the performance parameters which evaluate the clustering approaches are summarized.

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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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Junction-based stable clustering algorithm for vehicular ad hoc network

Cited by 14 publications

References 13 publications

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

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

Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and Solutions

Modularized hypergraph clustering scheme model for stableVANET

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