Received Signal Strength‐Based Localization for Vehicle Distance Estimation in Vehicular Ad Hoc Networks (VANETs)

Ahmad, Waqas; Husnain, Ghassan; Ahmed, Sheeraz; Aadil, Farhan; Lim, Sangsoon

doi:10.1155/2023/7826992

Cited by 8 publications

(2 citation statements)

References 41 publications

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“…We chose the Manhattan grid topology [17] for our VANET simulations due to its desirable properties. Figure 6 shows the grid representation of the proposed work for the VANET simulation scenario.…”

Section: Simulation Scenariomentioning

confidence: 99%

Load-Aware and Priority Adaptive Traffic Congestion Control Method in Vehicular Ad Hoc Network

Tadesse,

Demilew,

Mengesha

et al. 2024

Preprint

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Vehicular ad hoc networks (VANET) are a subset of mobile ad hoc networks used to communicate between vehicles and infrastructure. During vehicular congestion communication, nodes compete to acquire channels, causing the channels to become congested. The congestion on the vehicle network results in increased delay and packet loss, resulting in reduced VANET performance. To address this problem, we developed a load-aware and priority adaptive traffic congestion control method in vehicular ad hoc networks (VANETs). The proposed scheme identifies less-congested road segments based on the network's load and reduces traffic congestion by suggesting other routes between nearby roadside units (RSUs). This research aims to improve the efficiency of the vehicular environment by utilizing the movement of vehicles with (RSUs) and sharing the traffic load between them. Simulation results demonstrate the effectiveness of the proposed protocol in reducing congestion and enhancing the overall performance of VANETs. To validate the proposed algorithm, we have implemented and tested the proposed algorithm using Network Simulator 3 (NS3) for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication scenarios and computed the performance of the algorithm on different parameters of the network. The simulation result of the proposed load aware and priority adaptive traffic congestion control method in VANET improved the packet delivery ratio, packet lost ratio, and end-to-end delay by 96%, 4.1%, and 1102 milliseconds, when compared to TDCCA value of 92%, 5.7%, and 1154 milliseconds, respectively

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Section: Simulation Scenariomentioning

confidence: 99%

Load-Aware and Priority Adaptive Traffic Congestion Control Method in Vehicular Ad Hoc Network

Tadesse,

Demilew,

Mengesha

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Simulation results showed that, under different vehicle density scenarios, this deployment of the travel matrix could reduce the number of roadside units while enhancing vehicle communication capabilities. Ahmad et al [8] proposed a positioning scheme based on received signal strength. By detecting signals within the range of roadside devices, establishing communication, and developing an algorithm, their positioning algorithm helped determine the precise location of vehicles.…”

Section: Introductionmentioning

confidence: 99%

Optimal Layout Method for Roadside LiDAR and Camera

Li,

Zhang,

Cheng

et al. 2024

IEEE Access

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With the widespread application of LiDAR and camera, the integration of LiDAR and camera has become an urgent issue. In this study, we proposed a optimal layout method for roadside LiDAR and camera. Firstly, the experimental design phase took into consideration various application scenarios, such as curved road sections and gradient road sections. Secondly, the video data and point cloud data collected from different experimental setups were subjected to object detection and recognition using YOLOv5s weights and PointPillars weights, respectively. These weights are applied to the video data under different layout schemes to output the mAP value of each scheme. By comparing the mAP values under different layout schemes, the optimal layout scheme for the road scene is determined. Thirdly, the four road scene parameters and six installation parameters from all scenarios were collected into a database. Furthermore, five machine learning algorithms were employed for optimal selection. Finally, the three regression algorithms with the highest accuracy are selected for the final prediction model based on different control groups. Through the field experiment, the results show that the optimized layout method can significantly reduce blind spot detection and vehicle occlusion problems for camera and LiDAR. The optimal deployment method can increase the Mean Average Precision (MAP) by over 4% through adjusting the installation parameters. The regression algorithm used can predict the optimal deployment scheme for roadside LiDAR and cameras in unknown scenarios with over 95% accuracy. This optimal deployment method improves the detection accuracy of roadside devices for road vehicles by changing the installation parameters and provides guidance for the future installation of roadside LiDAR and camera.

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Blockchain Based Multi-hop Routing and Cost-Effective Decentralized Storage System for Wireless Sensor Networks

Faisal

Husnain

2023

Wireless Pers Commun

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Received Signal Strength‐Based Localization for Vehicle Distance Estimation in Vehicular Ad Hoc Networks (VANETs)

Cited by 8 publications

References 41 publications

Load-Aware and Priority Adaptive Traffic Congestion Control Method in Vehicular Ad Hoc Network

Load-Aware and Priority Adaptive Traffic Congestion Control Method in Vehicular Ad Hoc Network

Optimal Layout Method for Roadside LiDAR and Camera

Blockchain Based Multi-hop Routing and Cost-Effective Decentralized Storage System for Wireless Sensor Networks

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