Solutions for the Deployment of Communication Roadside Infrastructure for Streaming Delivery in Vehicular Networks

Silva, Cristiano M.; Souza, Fernanda Sumika Hojo de; Pitsillides, Andreas; Guidoni, Daniel L.

doi:10.1007/s10922-021-09600-0

Cited by 4 publications

(1 citation statement)

References 56 publications

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“…Existing studies on RSU deployment methods generally focus on determining the optimal locations, interval, minimum number of deployments, maximum coverage range, or maximum connectivity within cost constraints [12][13][14][15]. Lehsaini M. et al [16] proposed the use of metaheuristic methods, Guerna A. et al [17] proposed the use of a bio-inspired RSU placement system using ant colony optimization, Zhang L. et al [18] proposed an improved multi-objective quantum particle swarm optimization (MOQPSO) algorithm for RSU deployment, and Silva C. M. et al [19] presented an integer linear programming formulation and heuristic methods based on taboo search, all of which maximize network coverage with lower cost. Magsino E. R. et al [20] proposed the Enhanced Information SHAring scheme using RSU allocation (EISHA-RSU) to address the coverage and connectivity between vehicles and infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

Zhang

Lu²,

Chen

et al. 2023

Sustainability

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With the increasing number of Connected and Autonomous Vehicles (CAVs), the heterogeneous traffic flow on highways now consists of a mix of CAVs and Non-networked Autonomous Vehicles (NAVs). The current deployment of Roadside Units (RSUs) on highways is mostly based on uniform or hotspot locations. However, when the permeability of CAVs on the road varies, the communication network may face challenges such as excessive energy consumption due to closely spaced RSU deployments at high CAV permeability or communication interruptions due to widely spaced RSU deployments at low CAV permeability. To address this issue, this paper proposes an improved D-LEACH clustering algorithm based on vehicle clustering; analyzes the impact of RSU and vehicle communication radius, mixed traffic density, and different CAV permeabilities in the heterogeneous traffic flow on the RSU deployment interval; and calculates the rational and effective RSU deployment interval schemes under different CAV permeabilities on highways in the heterogeneous traffic flow. When the heterogeneous traffic flow density is stable and CAV continues to penetrate, the RSU communication radius and deployment interval can be adjusted to ensure that the network connectivity is maintained at a high level. When the RSU and vehicle communication radius are stable, the mixed traffic density is 0.05, and the CAV permeability is 0.2, the RSU deployment interval can be set to 1235 m; when the mixed traffic density is 0.08 and the CAV penetration rate is 0.7, the RSU deployment interval can be set to 1669 m to ensure that the network connectivity is maintained at a high level.

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Section: Introductionmentioning

confidence: 99%

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

Zhang

Lu²,

Chen

et al. 2023

Sustainability

View full text Add to dashboard Cite

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Edge intelligence based digital twins for internet of autonomous unmanned vehicles

Yang

You

et al. 2022

Softw Pract Exp

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It aims to explore the efficient and reliable wireless transmission and cooperative communication mechanism of Internet of Vehicles (IoV) based on edge intelligence technology. It first proposes an intelligent network architecture for IoV services by combining network slicing and deep learning (DL) technology, and then began to study the key technologies needed to achieve the architecture. It designs the cooperative control mechanism of unmanned vehicle network based on the full study of wireless resource allocation algorithm from the micro level. Second, in order to improve the safety of vehicle driving, deep reinforcement learning is used to configure the wireless resources of IoV network to meet the needs of various IoV services. The research results show that the accuracy rate of the improved AlexNet algorithm model can reach 99.64%, the accuracy rate is more than 80%, the data transmission delay is less than 0.02 ms, and the data transmission packet loss rate is less than 0.05. The algorithm model has practical application value for solving the data transmission related problems of vehicular internet communication, providing an important reference value for the intelligent development of unmanned vehicle internet.

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Genetic Algorithm for Multi-hop VANET Clustering Based on Coalitional Game

Charoenchai,

Siripongwutikorn

2023

J Netw Syst Manage

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Solutions for the Deployment of Communication Roadside Infrastructure for Streaming Delivery in Vehicular Networks

Cited by 4 publications

References 56 publications

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

Edge intelligence based digital twins for internet of autonomous unmanned vehicles

Genetic Algorithm for Multi-hop VANET Clustering Based on Coalitional Game

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