A Traffic Management Service Based on V2I Communication for Vehicular Ad-hoc Networks

Lourenço, Massilon; Gomides, Thiago S.; Souza, Fernanda Sumika Hojo de; Meneguette, Rodolfo I.; Guidoni, Daniel L.

doi:10.1145/3277103.3277132

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…It assumes that critical sections are known in advance to limit those communications, being implicit the existence of a centralized TMS. V2I based mechanisms were also proposed in [15] but they have similar constraints as distributed TMS.…”

Section: Related Workmentioning

confidence: 99%

“…Several mechanisms have been proposed to deal with it: Centralized ones use hyperpaths and K-shortest-hyperpath variations to obtain route diversification [10][11][12][13]. On the other hand, distributed mechanisms provide ad-hoc local routing based on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications but they assume high computing and ideal connectivity capabilities in the vehicles and the infrastructure [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Paricio

López-Carmona

2019

Sensors

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Urban traffic routing is deemed to be a significant challenge in intelligent transportation systems. Existing implementations suffer from several intrinsic issues such as scalability in centralized systems, unnecessary complexity of mechanisms and communication in distributed systems, and lack of privacy. These imply force intensive computational tasks in the traffic control center, continuous communication in real-time with involved stakeholders which require drivers to reveal their location, origin, and destination of their trips. In this paper we present an innovative urban traffic routing framework and reference architecture (multimap traffic control architecture, MuTraff), which is based on the strategical generation and distribution of a set of traffic network maps (traffic weighted multimaps, TWM) to vehicle categories or fleets. Each map in a TWM map set has the same topology but a different distribution of link weights, which are computed by considering policies and constraints that may apply to different vehicle groups. MuTraff delivers a traffic management system (TMS), where a traffic control center generates and distributes maps, while routing computation is performed at the vehicles. We show how this balance between generation, distribution, and routing computation improves scalability, eases communication complexities, and solves former privacy issues. Our study presents case studies in a real city environment for (a) global congestion management using random maps; (b) congestion control on road incidents; and c) emergency fleets routing. We show that MuTraff is a promising foundation framework that is easy to deploy, and is compatible with other existing TMS frameworks.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Paricio

López-Carmona

2019

Sensors

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“…Lourenço et al [21] present a traffic management strategy based on vehicle-to-infrastructure communication or V2I communication. At each RSU, vehicles transmit a message, and when they encounter a jam, vehicles ask for information on nearby streets from the closest RSU.…”

Section: Related Workmentioning

confidence: 99%

A Fog Computing Model for VANET to Reduce Latency and Delay Using 5G Network in Smart City Transportation

et al. 2022

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Connected vehicles are a vital part of smart cities, which connect over a wireless connection and bring mobile computation and communication abilities. As a mediator, fog computing resides between vehicles and the cloud and provides vehicles with processing, storage, and networking power through Vehicular Ad-hoc networks (VANET). VANET is a time-sensitive technology that requires less time to process a request received from a vehicle. Delay and latency are the notorious issues of VANET and fog computing. To deal with such problems, in this work, we developed a priority-based fog computing model for smart urban vehicle transportation that reduces the delay and latency of fog computing. To upgrade the fog computing infrastructure to meet the latency and Quality of Service (QoS) requirements, 5G localized Multi-Access Edge Computing (MEC) servers have also been used, which resulted tremendously in reducing the delay and the latency. We decreased the data latency by 20% compared to the experiment carried using only cloud computing architecture. We also reduced the processing delay by 35% compared with the utilization of cloud computing architecture.

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“…Por outro lado, em soluções descentralizadas, as Road-Side Units (RSUs) são implantadas nas ruas para receber, processar e disseminar informações de tráfego e rotas alternativas. No entanto, o uso da RSU introduz um custo extra para o gerenciamento de tráfego [Lourenço et al 2018, Brennand et al 2017. Finalmente, as soluções distribuídas são baseadas apenas na comunicação entre veículos.…”

Section: Trabalhos Relacionadosunclassified

“…Dessa forma, os veículos com capacidade de comunicação e sensoriamento são capazes de monitorar e compartilhar as condições do seu deslocamento considerando os problemas da mobilidade urbana, como: a redução dos congestionamentos e dos acidentes, redução da emissão de gases poluentes, a diminuição do tempo de viagem dos veículos e o aumento da vazão do fluxo das vias. A utilização de VANETs no projeto de soluções para minimizar congestionamentos em centros urbanos vem sendo pesquisada pelos autores em vários trabalhos da literatura, tais como [Wang et al 2014], [de Souza et al 2017], [Lourenço et al 2018], [Brennand et al 2017] e [Pan et al 2017].…”

Section: Introductionunclassified

Um Sistema Adaptativo e Colaborativo para Minimizar Congestionamentos utilizando Comunicação entre Veículos

Gomides¹,

Grande²,

Souza³

et al. 2020

Anais XXXVIII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2020)

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O gerenciamento de tráfego de veículos se tornou um desafio importante para as grandes cidades devido ao constante crescimento de veículos. Como a malha viária não aumenta como o número de veículos nas ruas, soluções tecnológicas para o gerenciamento do tráfego surgem como aplicações alternativas e fáceis de usar. Este trabalho apresenta um sistema de gerenciamento de tráfego adaptável e distribuído para VANETS. A solução proposta é baseada na comunicação V2V e na visão local do congestionamento do tráfego. Durante o deslocamento em uma rua, o veículo monitora a distância percorrida e a esperada considerando condições de tráfego em fluxo livre. Essa medida é usada para classificar o congestionamento do tráfego. O sistema proposto utiliza protocolos de disseminação proativa e reativa para criar um banco de dados distribuído contendo informações sobre congestionamento de tráfego entre veículos. Usando apenas informações locais, os veículos verificam rotas alternativas com menor congestionamento de tráfego. A solução proposta é comparada com três soluções da literatura, denominadas DIVERT, PANDORA e SGTD. Nossos resultados mostraram que nossa proposta apresenta melhores resultados em relação às métricas de congestionamento de rede e tráfego veicular.

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A Traffic Management Service Based on V2I Communication for Vehicular Ad-hoc Networks

Cited by 11 publications

References 9 publications

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

A Fog Computing Model for VANET to Reduce Latency and Delay Using 5G Network in Smart City Transportation

Um Sistema Adaptativo e Colaborativo para Minimizar Congestionamentos utilizando Comunicação entre Veículos

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