Accurate Cluster Head Selection Technique for Software Defined Network in 5G VANET

Maan, Ujjawal; Chaba, Yogesh

doi:10.1007/s11277-021-08072-4

Cited by 17 publications

(10 citation statements)

References 38 publications

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“…The work presented in Reference 17 by Maoli et al paved the way for the VANET to be presented as a hypergraph, despite the fact that the authors discussed it in the context of fog computing and left the discussion gap on network performance parameters. Reference 4 used the same random mobility model as SUMO to simulate a custom urban scenario with a junction. Four RSUs are placed on a single road segment, for a total of 16 clusters in the network.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Because of the frequent join/disjoin of new vehicles in a cluster, the dynamic nature of VANET poses a consistent threat of link failure. Fuzzy‐based CH selection, 1 weighted CH selection, 2‐4 heuristic selection, 5,6 QoS‐based, 7 and other approaches have been used to address these issues. Several vehicular properties have been identified by the researchers in CH selection.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modularized hypergraph clustering scheme model for stableVANET

Kumar

Dahiya

Singh³

2022

Concurrency and Computation

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“…Quality of Service (QoS) and identification of malicious nodes for improved clustering in VANETs have been proposed in [31]. A new approach [32] using Software Defined Network (SDN) has been proposed to optimize cluster heads in an urban area. A new fuzzy logicbased clustering scheme has been proposed in [33].…”

Section: Literature Reviewmentioning

confidence: 99%

An intelligent cluster optimization algorithm based on Whale Optimization Algorithm for VANETs (WOACNET)

Husnain

Anwar

2021

PLoS ONE

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Vehicular Ad hoc Networks (VANETs) an important category in networking focuses on many applications, such as safety and intelligent traffic management systems. The high node mobility and sparse vehicle distribution (on the road) compromise VANETs network scalability and rapid topology, hence creating major challenges, such as network physical layout formation, unstable links to enable robust, reliable, and scalable vehicle communication, especially in a dense traffic network. This study discusses a novel optimization approach considering transmission range, node density, speed, direction, and grid size during clustering. Whale Optimization Algorithm for Clustering in Vehicular Ad hoc Networks (WOACNET) was introduced to select an optimum cluster head (CH) and was calculated and evaluated based on intelligence and capability. Initially, simulations were performed, Subsequently, rigorous experimentations were conducted on WOACNET. The model was compared and evaluated with state-of-the-art well-established other methods, such as Gray Wolf Optimization (GWO) and Ant Lion Optimization (ALO) employing various performance metrics. The results demonstrate that the developed method performance is well ahead compared to other methods in VANET in terms of cluster head, varying transmission ranges, grid size, and nodes. The developed method results in achieving an overall 46% enhancement in cluster optimization and an F-value of 31.64 compared to other established methods (11.95 and 22.50) consequently, increase in cluster lifetime.

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“…The node strength, number of hops, and maximum speed were considered as the system inputs to determine which node represented the next hop. Maan and Chaba [27] adopted a method relying on fuzzy logic for selecting head-clusters with maximum lifetime to achieve communication in 5G VANET networks. Most of the aforementioned research did not take into account the effect of vehicles density and its continuous change when choosing the communication routes.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Route lifetime in Vehicular Ad Hoc ‎Networks Based on Skellam Distribution Model

Tawfeeq

2021

Int. j. commun. netw. inf. secur.

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The emergence of smart cities and the need to use intelligent transportation systems has led to an increased reliance on vehicle ad hoc networks (VANET). The topology of VANET is highly dynamic, which results in a short effective routing time. This paper presents a two-stage algorithm to select a route that can sustain communication between vehicles for as long as possible while taking into account the variables that affect the VANET topology. The first stage uses Skellam distribution model to assess the connectivity probability of paths in ‎a 2d road network based on traffic-flow and the number of vehicles ‎joining and leaving the ‎network, accordingly, the path with the highest connectivity is chosen. In the second stage, the control packets sent only to vehicles on the selected path to detect routes between source and destination, thus reducing the overhead of control packets and increasing network stability. ‎ the algorithm adopts the principle of global evaluation to ‎estimate the lifetime ‎of the ‎detected ‎routes within the chosen path. ‎the route with the ‎best estimated ‎lifetime ‎is ‎chosen to be ‎the active route. ‎in the event of route failure, the validity of the next route in lifetime is confirmed to be adopted as the alternate route. The proposed algorithm was compared with both on-‎demand distance ‎vector routing protocol (AODV) protocol and the modified location-aided routing ‎‎(LAR) ‎protocol. The proposed algorithm showed greater network stability, higher performance in terms of longer lifetime route detection, less energy consumption and higher throughput.

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Accurate Cluster Head Selection Technique for Software Defined Network in 5G VANET

Cited by 17 publications

References 38 publications

Modularized hypergraph clustering scheme model for stableVANET

Modularized hypergraph clustering scheme model for stableVANET

An intelligent cluster optimization algorithm based on Whale Optimization Algorithm for VANETs (WOACNET)

Enhancing Route lifetime in Vehicular Ad Hoc ‎Networks Based on Skellam Distribution Model

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