Vehicular ad-hoc networks (VANETs) have become an integral part of modern intelligent transportation systems, playing a crucial role in enhancing road safety and efficiency. However, as the number of vehicles on the roads continues to rise, the challenge of network congestion has become increasingly pressing. Researchers have now developed a dynamic clustering-based technique that effectively mitigates this problem, paving the way for safer and more reliable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. This innovative approach, known as the Dynamic Vehicle Grouping Scheme (DVGS), utilizes advanced machine learning algorithms to organize vehicles into dynamic clusters, optimizing communication and reducing overall network congestion. By adapting transmission rates based on real-time channel conditions and the proximity of neighboring vehicles, the DVGS technique significantly improves key performance metrics such as throughput, packet delivery ratio, and end-to-end latency in dense vehicular environments. This breakthrough has the potential to revolutionize the way vehicles communicate, ultimately enhancing road safety and the overall efficiency of intelligent transportation systems.
Tackling the Challenge of Network Congestion in VANETs
Vehicular ad-hoc networks (VANETs) have emerged as a critical component of intelligent transportation systems (ITS), enabling direct communication between vehicles and roadside infrastructure. This technology plays a pivotal role in enhancing road safety, traffic management, and the delivery of various services to drivers and passengers. As the number of vehicles on the roads continues to grow, the challenge of network congestion has become increasingly pressing, posing a significant threat to the reliable and efficient operation of VANET-based applications.
Pioneering a Dynamic Clustering Approach
To address this challenge, researchers have developed a novel technique called the Dynamic Vehicle Grouping Scheme (DVGS), which utilizes advanced machine learning algorithms to dynamically organize vehicles into clusters. By leveraging the DBSCAN and Click Here
![Click Here](https://en.wikipedia.org/wiki/K-means<em>clustering’>K-Means</a> clustering algorithms, the DVGS approach creates virtual regions around vehicles, allowing them to communicate directly with others within the same cluster. This eliminates the need to broadcast messages across the entire network, effectively reducing the overall network congestion.</p>
<p><figure class="wp-block-image size-large"><img decoding="async" src="https://famefreq.s3.us-east-2.amazonaws.com/1729684178_41598_2024_75648_Fig1_HTML.png" alt="figure 1" class="wp-image-1" /><figcaption class="wp-element-caption">Fig. 1</figcaption></figure>
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<p><h3>Adaptive Transmission Rates for Optimal Performance</h3>
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<p>A key aspect of the DVGS technique is its ability to adaptively adjust the transmission rates of vehicles based on real-time channel conditions and the proximity of neighboring vehicles. By comparing the instantaneous separation distance between vehicles to the required safety distance, the DVGS algorithm determines the optimal transmission rate for each vehicle, ensuring a balanced performance between packet delivery and channel congestion.</p>
<p><h3>Impressive Results in Dense Vehicular Environments</h3>
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<p>The researchers conducted extensive simulations to evaluate the performance of the DVGS technique, comparing it to the existing Decentralized Congestion Control (DCC) approach. The results demonstrated that the DVGS technique, particularly when using the K-Means clustering algorithm, significantly outperformed the DCC method in terms of <b>throughput</b>, <b>packet delivery ratio</b>, and <b>end-to-end latency</b> in dense vehicular environments.</p>
<p><figure class="wp-block-image size-large"><img decoding="async" src="https://famefreq.s3.us-east-2.amazonaws.com/1729684181_41598_2024_75648_Fig2_HTML.png" alt="figure 2" class="wp-image-2" /><figcaption class="wp-element-caption">Fig. 2</figcaption></figure>
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<p><h3>Enhancing Road Safety and Intelligent Transportation Systems</h3>
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<p>The DVGS technique’s ability to effectively mitigate network congestion in VANETs has far-reaching implications for the future of intelligent transportation systems. By ensuring reliable and efficient communication between vehicles and roadside infrastructure, this innovative approach has the potential to enhance various safety-critical applications, such as emergency response, traffic management, and accident prevention. As the number of connected vehicles continues to grow, the DVGS technique represents a significant step forward in realizing the full potential of VANET technology and its ability to improve overall road safety and transportation efficiency.</p>
<p><h3>Exploring the Methodology and Simulation Setup</h3>
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<p>The researchers employed a multi-pronged approach to develop and evaluate the DVGS technique. They first designed a detailed road network using the SUMO (Simulation of Urban Mobility) software, incorporating various parameters such as the number of lanes, edge lengths, and maximum vehicle speeds. This simulated road network served as the foundation for the subsequent communication simulations conducted in NS2 (Network Simulator 2).</p>
<p><figure class="wp-block-image size-large"><img decoding="async" src="https://famefreq.s3.us-east-2.amazonaws.com/1729684184_41598_2024_75648_Fig3_HTML.png" alt="" class="wp-image-3" /><figcaption class="wp-element-caption">Table 1 Comparison of machine learning based congestion control techniques.</figcaption></figure>
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<p>In the NS2 simulation, the researchers utilized a mobility file generated by SUMO, which contained information about the vehicles’ speed, position, and other relevant data. This allowed for the accurate modeling of vehicle-to-vehicle and vehicle-to-infrastructure communication. The simulation setup included 50 mobile vehicles and 7 static roadside units, all equipped with IEEE 802.11p-based communication capabilities.</p>
<p><h3>Evaluating the DVGS Technique’s Performance</h3>
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<p>The researchers conducted a series of simulations to assess the performance of the DVGS technique, with a particular focus on comparing it to the existing DCC approach. They analyzed key metrics such as <b>throughput</b>, <b>packet delivery ratio</b>, and <b>end-to-end latency</b> under various conditions, including different transmission powers and packet sizes.</p>
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<p>The results of the simulations demonstrated that the DVGS technique, especially when utilizing the K-Means clustering algorithm, consistently outperformed the DCC method in dense vehicular environments. This superiority was evident in the significantly higher throughput, improved packet delivery ratio, and reduced end-to-end latency achieved by the DVGS approach.</p>
<p><h3>Unlocking the Future of Intelligent Transportation</h3>
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<p>The development of the DVGS technique represents a significant breakthrough in the field of vehicular communication, offering a promising solution to the pressing challenge of network congestion in VANETs. By dynamically organizing vehicles into clusters and adaptively adjusting transmission rates, this innovative approach has the potential to revolutionize the way vehicles communicate, ultimately enhancing road safety and the overall efficiency of intelligent transportation systems.</p>
<p>As the number of connected vehicles continues to grow, the DVGS technique’s ability to mitigate network congestion will become increasingly crucial. This advancement paves the way for the seamless integration of various safety-critical applications, such as emergency response, traffic management, and accident prevention, further solidifying the role of VANETs in the future of transportation.</p>
<p>Author credit: This article is based on research by Bhupendra Dhakad, Sadhana Mishra, Shailendra Singh Ojha, Jai Kumar Sharma, Sateesh Kumar, Mubarak Alrashoud, Jayant Giri, S. M. Mozammil Hasnain.</p>
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