CluRMA: A cluster-based RSU-enabled message aggregation scheme for vehicular ad hoc networks

Bhabani, Bidisha; Mahapatro, Judhistir

doi:10.1016/j.vehcom.2022.100564

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…To ensure diversity and coverage, the app incorporates different driving conditions, such as urban, rural, and highway scenarios. This is why the data generated by the app is widely used by the research community 43 – 45 .…”

Section: Range and Azimuth Parametersmentioning

confidence: 99%

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Radar sensor based machine learning approach for precise vehicle position estimation

Sohail,

Khan,

Sandhu

et al. 2023

Sci Rep

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Estimating vehicles’ position precisely is essential in Vehicular Adhoc Networks (VANETs) for their safe, autonomous, and reliable operation. The conventional approaches used for vehicles’ position estimation, like Global Positioning System (GPS) and Global Navigation Satellite System (GNSS), pose significant data delays and data transmission errors, which render them ineffective in achieving precision in vehicles’ position estimation, especially under dynamic environments. Moreover, the existing radar-based approaches proposed for position estimation utilize the static values of range and azimuth, which make them inefficient in highly dynamic environments. In this paper, we propose a radar-based relative vehicle positioning estimation method. In the proposed method, the dynamic range and azimuth of a Frequency Modulated Continuous Wave radar is utilized to precisely estimate a vehicle’s position. In the position estimation process, the speed of the vehicle equipped with the radar sensor, called the reference vehicle, is considered such that a change in the vehicle’s speed changes the range and azimuth of the radar sensor. For relative position estimation, the distance and relative speed between the reference vehicle and a nearby vehicle are used. To this end, only those vehicles are considered that have a higher possibility of coming in contact with the reference vehicle. The data recorded by the radar sensor is subsequently utilized to calculate the precision and intersection Over Union (IOU) values. You Only Look Once (YOLO) version 4 is utilized to calculate precision and IOU values from the data captured using the radar sensor. The performance is evaluated under various real-time traffic scenarios in a MATLAB-based simulator. Results show that our proposed method achieves 80.0% precision in position estimation and obtains an IOU value up to 87.14%, thereby outperforming the state-of-the-art.

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Section: Range and Azimuth Parametersmentioning

confidence: 99%

“…Moreover, the data generated are of real-time nature, and can directly be used to analyze the environment in real-time. It is because of this reason that the app has been widely used by the research community for validating their algorithms 43 – 45 . The radar sensor details are explained below.…”

Section: Range and Azimuth Parametersmentioning

confidence: 99%

Radar sensor based machine learning approach for precise vehicle position estimation

Sohail,

Khan,

Sandhu

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…a) Similarity: Similarity refers to the measure of how much two vehicles or clusters have in common based on specific criteria, such as location, speed, direction, etc. Many similarity-based clustering algorithms are proposed in the literature to group vehicles that exhibit similar behavior or are located in close proximity to each other [11], [7], [14] and [4]. This criteria enables efficient data sharing and facilities communication between vehicles that have similar driving patterns, preferences, or interests.…”

Section: B Cluster-head Selection Criteriamentioning

confidence: 99%

Enhancing Data Collection in Vehicular Network Through Clustering Optimization

Boucetta

Lassabe

Baala

2023

2023 International Wireless Communications and Mobile Computing (IWCMC)

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In this paper, we present a novel approach to enhance data collection in Vehicular Ad-Hoc NETworks (VANETs). VANETs are a growing area of interest due to their unique characteristics and challenges, such as rapidly changing topology and frequent network disruptions. Efficient data collection is a critical issue in vehicular networks and has therefore become a focus of research. To address this challenge, we propose a stable clustering optimization solution based on adaptive multiple metrics. The cluster head selection is done based on both mobility metrics, such as position and relative speed, and Quality of Service (QoS) metrics, such as neighborhood degree and link quality. The proposed solution has been tested and evaluated through simulations using a vehicular mobility simulator in a realistic urban environment. The results show that the proposed approach provides more stable clusters with higher QoS, and allows for the selection of the appropriate cluster head to collect data from the vehicles and forward it to the destination.

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A Comprehensive Review of Recent Developments in VANET for Traffic, Safety & Remote Monitoring Applications

Dutta,

Samaniego Campoverde,

Tropea

et al. 2024

J Netw Syst Manage

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Strategic integration of Wireless Sensor Networks (WSNs) and IoT (Internet of Things) into VANET infrastructure is crucial for ensuring vehicular safety, mobility management, and vehicular applications. The integration collects information on traffic and road conditions without relying on traditional internet connectivity. It also addresses applications such as early warnings in areas with limited coverage, safety and health emergency messages in highly congested zones, and air monitoring without depending on traditional TCP/IP internet connectivity. This article provides a comprehensive view of network technologies, data acquisition devices, clustering techniques, and energy-efficient routing protocols to optimize Vehicle-to-Everything (V2X) communications in VANETs. This study also addresses how to leverage the frequency channels of the 802.11p protocol and expands the possibilities for developing numerous applications dedicated to remote ambient, traffic and safety monitoring without compromising network performance. Moreover, the recent developments of clustering algorithms and energy-efficient schemes for these VANET applications are analyzed from a novel perspective.

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CluRMA: A cluster-based RSU-enabled message aggregation scheme for vehicular ad hoc networks

Cited by 11 publications

References 27 publications

Radar sensor based machine learning approach for precise vehicle position estimation

Radar sensor based machine learning approach for precise vehicle position estimation

Enhancing Data Collection in Vehicular Network Through Clustering Optimization

A Comprehensive Review of Recent Developments in VANET for Traffic, Safety & Remote Monitoring Applications

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