Connectivity analysis of directed highway vehicular ad hoc networks using graph theory

Elaraby, Samar; Abuelenin, Sherif M.

doi:10.1002/dac.4745

Cited by 13 publications

(13 citation statements)

References 60 publications

(96 reference statements)

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“…Graph theory is a promising approach for modeling and representing the connectivity analysis of vehicular networks [41,42]. A random geometric graph (RGG) is a particular model of traditional graph theory that accurately characterizes randomly deployed networks, such as wireless sensor networks [43][44][45][46][47] or VANETs [48].…”

Section: Connectivity Phasementioning

confidence: 99%

An Efficient Dynamic Solution for the Detection and Prevention of Black Hole Attack in VANETs

Malik

Khan

Faisal

et al. 2022

Sensors

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Rapid and tremendous advances in wireless technology, miniaturization, and Internet of things (IoT) technology have brought significant development to vehicular ad hoc networks (VANETs). VANETs and IoT together play a vital role in the current intelligent transport system (ITS). However, a VANET is highly vulnerable to various security attacks due to its highly dynamic, decentralized, open-access medium, and protocol-design-related concerns. Regarding security concerns, a black hole attack (BHA) is one such threat in which the control or data packets are dropped by the malicious vehicle, converting a safe path/link into a compromised one. Dropping data packets has a severe impact on a VANET’s performance and security and may cause road fatalities, accidents, and traffic jams. In this study, a novel solution called detection and prevention of a BHA (DPBHA) is proposed to secure and improve the overall security and performance of the VANETs by detecting BHA at an early stage of the route discovery process. The proposed solution is based on calculating a dynamic threshold value and generating a forged route request (RREQ) packet. The solution is implemented and evaluated in the NS-2 simulator and its performance and efficacy are compared with the benchmark schemes. The results showed that the proposed DPBHA outperformed the benchmark schemes in terms of increasing the packet delivery ratio (PDR) by 3.0%, increasing throughput by 6.15%, reducing the routing overhead by 3.69%, decreasing the end-to-end delay by 6.13%, and achieving a maximum detection rate of 94.66%.

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Section: Connectivity Phasementioning

confidence: 99%

An Efficient Dynamic Solution for the Detection and Prevention of Black Hole Attack in VANETs

Malik

Khan

Faisal

et al. 2022

Sensors

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“…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%

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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“…In recent articles, 13,14,16,17 VANET is represented as an undirected network graph for spectral clustering; however, information is lost only in pair connections, as shown in Figure 1. (a) The vehicle is only connected to two other vehicles in the graph, implying that driving and information sharing occurs only with these two vehicles.…”

Section: Network Modeling As Hypergraph Via Modularity Matrixmentioning

confidence: 99%

“…The problem of CH selection is twofold: identifying and generating an optimal number of clusters, and selecting the CH selection parameters for improved stability. The VANET was defined by researchers in References 13,14,16,17 as a graph in which a vehicle is connected to two other vehicles with an edge weight of distance between them. In these works, spectral clustering is used to find the best clusters and to generate new clusters.…”

Section: Introductionmentioning

confidence: 99%

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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Connectivity analysis of directed highway vehicular ad hoc networks using graph theory

Cited by 13 publications

References 60 publications

An Efficient Dynamic Solution for the Detection and Prevention of Black Hole Attack in VANETs

An Efficient Dynamic Solution for the Detection and Prevention of Black Hole Attack in VANETs

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

Modularized hypergraph clustering scheme model for stableVANET

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