An RSU Deployment Strategy Based on Traffic Demand in Vehicular <i>Ad Hoc</i> Networks (VANETs)

Yu, Haiyang; Liu, Runkun; Li, Zhiheng; Jiang, Han

doi:10.1109/jiot.2021.3111048

Cited by 39 publications

(10 citation statements)

References 42 publications

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“…As the prevalence of connected vehicles continues to expand, there is a growing recognition of the essential role that infrastructure, particularly RSUs, plays in ensuring the reliability and trustworthiness of vehicular communication systems [35]. RSUs are pivotal in verifying message authenticity and confirming the eligibility of the sender, contributing significantly to the overall integrity of VANETs [36].…”

Section: Disconnected Areas Of Vehicular Networkmentioning

confidence: 99%

Enabling Vehicle-to-Vehicle Trust in Rural Areas: An Evaluation of a Pre-Signature Scheme for Infrastructure-Limited Environments

Almani,

Muller,

Carpent

et al. 2024

Future Internet

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This research investigates the deployment and effectiveness of the novel Pre-Signature scheme, developed to allow for up-to-date reputation being available in Vehicle-to-Vehicle (V2V) communications in rural landscapes, where the communications infrastructure is limited. We discuss how existing standards and specifications can be adjusted to incorporate the Pre-Signature scheme to disseminate reputation. Addressing the unique challenges posed by sparse or irregular Roadside Units (RSUs) coverage in these areas, the study investigates the implications of such environmental factors on the integrity and reliability of V2V communication networks. Using the widely used SUMO traffic simulation tool, we create and simulate real-world rural scenarios. We have conducted an in-depth performance evaluation of the Pre-Signature scheme under the typical infrastructural limitations encountered in rural scenarios. Our findings demonstrate the scheme’s usefulness in scenarios with variable or constrained RSUs access. Furthermore, the relationships between the three variables, communication range, amount of RSUs, and degree of home-to-vehicle connectivity overnight, are studied, offering an exhaustive analysis of the determinants influencing V2V communication efficiency in rural contexts. The important findings are (1) that access to accurate Reputation Values increases with all three variables and (2) the necessity of Pre-Signatures decreases if the amount and range of RSUs increase to high numbers. Together, these findings imply that areas with a low degree of adoption of RSUs (typically rural areas) benefit the most from our approach.

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Section: Disconnected Areas Of Vehicular Networkmentioning

confidence: 99%

Enabling Vehicle-to-Vehicle Trust in Rural Areas: An Evaluation of a Pre-Signature Scheme for Infrastructure-Limited Environments

Almani,

Muller,

Carpent

et al. 2024

Future Internet

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“…illustrates a possible RSUs' deployment over an urban scenario. In recent years, several works focusing on different aspects related to the planning of vehicular networks, and specifically with the location of RSUs, have been published [8,11,12].…”

Section: Introductionmentioning

confidence: 99%

“…[8] and [11] assume the full knowledge of trajectories for each vehicle whereas in our work we will make the less restrictive/demanding assumption that only the average density of vehicles over the area of interest is known. [11] and [12] consider a multi-hop network where vehicles forward traffic for the other vehicles. In this paper, traffic is transmitted exclusively between RSUs and vehicles which is a more realistic assumption for a multi-service network providing non-free services.…”

Section: Introductionmentioning

confidence: 99%

Strategies to Plan the Number and Locations of RSUs for an IEEE 802.11p-based Infrastructure in Urban Environment

Astudillo León,

Busson,

de la Cruz Llopis

et al. 2023

Proceedings of the Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…Autonomous vehicles have become increasingly popular recently and are expected to remarkably improve transportation efficiency in innovative ways, which is enabled by a series of emerging technologies [1,2]. The fact is that the premise of deploying autonomous vehicles is the need to ensure the absolute safety of all traffic participants.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Prediction with Attention-Based Spatial–Temporal Graph Convolutional Networks for Autonomous Driving

Li,

Ren,

et al. 2023

Applied Sciences

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Accurate and reliable trajectory prediction is crucial for autonomous vehicles to achieve safe and efficient operation. Vehicles perceive the historical trajectories of moving objects and make predictions of behavioral intentions for a future period of time. With the predicted trajectories of moving objects such as obstacle vehicles, pedestrians, and non-motorized vehicles as inputs, self-driving vehicles can make more rational driving decisions and plan more reasonable and safe vehicle motion behaviors. However, due to traffic environments such as intersection scenes with highly interdependent and dynamic attributes, the task of motion anticipation becomes challenging. Existing works focus on the mutual relationships among vehicles while ignoring other potential essential interactions such as vehicle–traffic rules. These studies have not yet deeply explored the intensive learning of interactions between multi-agents, which may result in evaluation deviations. Aiming to meet these issues, we have designed a novel framework, namely trajectory prediction with attention-based spatial–temporal graph convolutional networks (TPASTGCN). In our proposal, the multi-agent interaction mechanisms, including vehicle–vehicle and vehicle–traffic rules, are meticulously highlighted and integrated into one homogeneous graph by transferring the time-series data of traffic lights into the spatial–temporal domains. Through integrating the attention mechanism into the adjacency matrix, we effectively learn the different strengths of interactive association and improve the model’s ability to capture critical features. Simultaneously, we construct a hierarchical structure employing the spatial GCN and temporal GCN to extract the spatial dependencies of traffic networks. Profiting from the gated recurrent unit (GRU), the scene context in temporal dimensions is further attained and enhanced with the encoder. In such a way, the GCN and GRU networks are fused as a features extractor module in the proposed framework. Finally, the future potential trajectories generation tasks are performed by another GRU network. Experiments on real-world datasets demonstrate the superior performance of the scheme compared with several baselines.

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An RSU Deployment Strategy Based on Traffic Demand in Vehicular Ad Hoc Networks (VANETs)

Cited by 39 publications

References 42 publications

Enabling Vehicle-to-Vehicle Trust in Rural Areas: An Evaluation of a Pre-Signature Scheme for Infrastructure-Limited Environments

Enabling Vehicle-to-Vehicle Trust in Rural Areas: An Evaluation of a Pre-Signature Scheme for Infrastructure-Limited Environments

Strategies to Plan the Number and Locations of RSUs for an IEEE 802.11p-based Infrastructure in Urban Environment

Trajectory Prediction with Attention-Based Spatial–Temporal Graph Convolutional Networks for Autonomous Driving

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