Data congestion in VANETs: research directions and new trends through a bibliometric analysis

Bhatia, Tarandeep Kaur; Ramachandran, Ramkumar Ketti; Doss, Robin; Pan, Lei

doi:10.1007/s11227-020-03520-7

Cited by 4 publications

(2 citation statements)

References 88 publications

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“…Matplotlib emerges as the key tool for plotting and visualization in Fig 3 . The method establishes scatter plots designed to represent network nodes and overlapping traffic paths between source and destination nodes Using rich Matplotlib functionality, the method ensures a visualization of network connectivity visual beauty In addition, source and destination nodes. Furthermore, annotations are added to denote the roles of source and destination nodes, enhancing interpretability [7][10] [16].…”

Section: ) Plotting and Annotationmentioning

confidence: 99%

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Network Congestion Visualization for Vehicular Communication using Streamlit Approach

Kall,

Karamtoti

2024

Preprint

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In the modern realm of Mobile Communication with various velocities of mobility, vehicular networks play a vital role in ensuring seamless communication without loss in the network connection. However, the efficacy and reliability of these networks are significantly impacted by ubiquitous network congestion. Understanding the dynamics of network congestion is essential for optimizing the intricate complexities inherent in vehicular systems. This research undertakes a comparative analysis of two distinct methods for visualizing network congestion within vehicular networks. The first approach utilizes Streamlit, a Python library, while the second employs MATLAB based for visualization. Through detailed implementation and evaluation, this study elucidates the strengths, limitations, and advantages of each method. Such insights are invaluable for practitioners and researchers specializing in vehicle networking and associated visualizations, offering a comprehensive understanding of the subject matter. This research developed the algorithms for verification of congestion in the vehicular network, the same has been cross verified with two methods.

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Section: ) Plotting and Annotationmentioning

confidence: 99%

“…[10] [15][16]. Method : start simulation nodes ← generate random nodes(); num nodes ← nodes.shape[0]; num pairs ← min(5, num nodes); source nodes ← random choice(num nodes, num pairs); dest nodes ← random choice(num nodes, num pairs); plot graph and paths();…”

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confidence: 99%

Network Congestion Visualization for Vehicular Communication using Streamlit Approach

Kall,

Karamtoti

2024

Preprint

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An Intelligent Probabilistic Whale Optimization Algorithm (i-WOA) for Clustering in Vehicular Ad Hoc Networks

Husnain

Anwar

2022

Int J Wireless Inf Networks

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Enhancing VANET Efficiency with ACRR Technique for Improved Traffic Management and Network Scalability

Karthick,

Talari,

et al. 2024

2024 International Conference on Science Technology Engineering and Management (ICSTEM)

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Data congestion in VANETs: research directions and new trends through a bibliometric analysis

Cited by 4 publications

References 88 publications

Network Congestion Visualization for Vehicular Communication using Streamlit Approach

Network Congestion Visualization for Vehicular Communication using Streamlit Approach

An Intelligent Probabilistic Whale Optimization Algorithm (i-WOA) for Clustering in Vehicular Ad Hoc Networks

Enhancing VANET Efficiency with ACRR Technique for Improved Traffic Management and Network Scalability

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