A Routing Scheme Using an Adaptive K-Harmonic Means Clustering for VANETs

Kandali, Khalid; Bennis, Hamid

doi:10.1109/iscv49265.2020.9204257

Cited by 9 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the number of transmission ranges was multiplied by the ratio of the road length to the summation of transmission ranges in [27,28] to estimate the K number. The algorithm presented in [29] picks the K number from a maximum function of the road length to transmission range ratio and the number of vehicles to the maximum number of CM ratio. Furthermore, the work in [30] formulated a Maximum Stable Set Problem (MSSP) using the vehicles' similarity scores and solving it with the Continuous Hopfield Network (CHN) to estimate the K number and select the initial medoids.…”

Section: Background and Related Workmentioning

confidence: 99%

Examining the Use of Scott’s Formula and Link Expiration Time Metric for Vehicular Clustering

Samann

2024

CMES

View full text Add to dashboard Cite

Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms. K-clustering algorithms are simplistic, with fast performance and relative accuracy. However, their implementation depends on the initial selection of clusters number (K), the initial clusters' centers, and the clustering metric. This paper investigated using Scott's histogram formula to estimate the K number and the Link Expiration Time (LET) as a clustering metric. Realistic traffic flows were considered for three maps, namely Highway, Traffic Light junction, and Roundabout junction, to study the effect of road layout on estimating the K number. A fast version of the PAM algorithm was used for clustering with a modification to reduce time complexity. The Affinity propagation algorithm sets the baseline for the estimated K number, and the Medoid Silhouette method is used to quantify the clustering. OMNET++, Veins, and SUMO were used to simulate the traffic, while the related algorithms were implemented in Python. The Scott's formula estimation of the K number only matched the baseline when the road layout was simple. Moreover, the clustering algorithm required one iteration on average to converge when used with LET.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

Examining the Use of Scott’s Formula and Link Expiration Time Metric for Vehicular Clustering

Samann

2024

CMES

View full text Add to dashboard Cite

show abstract

“…Tests conducted on NS2 revealed that the proposed technique outperformed previous algorithms. 42) presented an RPKHM to lengthen the links between automobiles and boost the stability of the vehicular network ("Routing protocol employing an adaptive K-Harmonic Means"). A mathematical model was put into practice to determine the number and initial placements of the centroids while taking into account the network's topology and the overall number of cars.…”

Section: Geo-cast Routing Protocolsmentioning

confidence: 99%

A Survey of Different VANET Routing Protocols

Shandil¹

2023

Evergreen

View full text Add to dashboard Cite

Vehicle nodes can join or exit the VANET, which is a decentralized sort of network. Due to the network's dynamic nature, reactive routing methods are used for path establishment from source to destination. Reviewing current protocols that are regularly utilized in various proposals and application scenarios is the goal of this work. This review helps when developing selection criteria or metrics for a new protocol. The systematic literature review (SLR) is used in this survey on VANET routing protocols to analyze the most recent state-of-art recommendations. Additionally, in this study, a number of proactive routing and reactive routing protocols are defined. Reactive routing protocols construct a route from source to destination using the most recent network data. The proactive routing protocols establish paths using network-defined information. Routing tables are used by proactive routing technologies to construct paths. Analysis shows that reactive routing strategies outperform proactive routing techniques. The research shows that speed and distance are the most frequent and adaptable metrics in a procedure. Finally, the application of various methods is discussed along with some feasible directions for further research.

show abstract

“…Routing optimization based on evolutionary or genetic algorithms is also proposed for other types of ad hoc networks such as similar techniques for Flying Ad hoc Networks and are available in [25,26] and in [27] for body area networks. Some recent work on clustering in VANETs has been proposed in [28], where a K-Harmonic means clustering technique is introduced to improve communication links between vehicles. A recent study discusses population size which is usually set between 20 and 100 in bio-inspired clustering, considering only nodes for communication that have relatively high energy as compared to other nodes in a networking group [29].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Husnain

Anwar

2021

PLoS ONE

View full text Add to dashboard Cite

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.

show abstract

A Routing Scheme Using an Adaptive K-Harmonic Means Clustering for VANETs

Cited by 9 publications

References 11 publications

Examining the Use of Scott’s Formula and Link Expiration Time Metric for Vehicular Clustering

Examining the Use of Scott’s Formula and Link Expiration Time Metric for Vehicular Clustering

A Survey of Different VANET Routing Protocols

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

Contact Info

Product

Resources

About