Characterizing the Instantaneous Connectivity of Large-Scale Urban Vehicular Networks

Naboulsi, Diala; Fiore, Marco

doi:10.1109/tmc.2016.2591527

Cited by 50 publications

(28 citation statements)

References 47 publications

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“…To ensure the validity of simulations, we use the vehicular mobility dataset TAPASCologne [28] to build the simulation scenarios we need. This dataset covers a region of 400 square kilometers in Cologne, Germany, for a period of 24 hours in a typical working day, and comprises more than 700,000 individual car trips.…”

Section: A Vanets Scenariosmentioning

confidence: 99%

A New Affinity Propagation Clustering Algorithm for V2V-Supported VANETs

Baishun

Shi

et al. 2020

IEEE Access

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Clustering is an efficient method for improving the communication performance of Vehicular Ad hoc NETworks (VANETs) that adopt Vehicle to Vehicle (V2V) communications. However, how to maximize the cluster stability while accounting for the high mobility of vehicles remains a challenging problem. In this paper, we first reconstruct the similarity function of the Affinity Propagation (AP) clustering algorithm by introducing communication-related parameters, so the vehicles with low relative mobility and good communication performance can easily be selected as cluster heads. Then, by formally defining three scaling functions, a weighted mechanism is designed to quantitatively assess the effect on the cluster stability when a vehicle joins it. Base on them, from the perspective of global balance, a new AP clustering algorithm for the whole clustering process is proposed. To ensure the validity of simulations, we use the vehicular mobility data generated on the realistic map of Cologne, Germany, and perform a series of simulations for eleven metrics commonly adopted in similar works. The results show that our proposed algorithm performs better than other algorithms in terms of the cluster stability, and it also effectively improves throughput and reduces packet loss rate of VANETs over the classical APROVE algorithm and the NMDP-APC algorithm. INDEX TERMS VANETs, V2V, clustering, affinity propagation. NOTATIONS CR One-hop communication radius of a vehicle V i Vehicle i, i is the ID of the vehicle az i Azimuth angle of V i Th az Threshold value for the difference of two azimuth angles v i Velocity of V i (x i , y i) Position of V i Th v Threshold value for the difference of two velocities v max Maximum velocity limit for the current road OCR i Owned communication rate of V i RCR i Required communication rate of V i NN L i Normal neighbor list of V i NN L i,j The jth list item of NN L i The associate editor coordinating the review of this manuscript and approving it for publication was Muhammad Alam. ER One-hop effective range of a vehicle d i,j Distance between V i and V j s (i, j) Similarity function between V i and V j r (i, j) Responsibility sent from V i to V j R i Responsibility list sent from V i to its onehop neighbor vehicles a (i, j) Availability sent from V j to V i A j Availability list sent from V j to its one-hop neighbor vehicles CH i Cluster head of V i ID (CH i) ID of V i 's cluster head CCH L i Candidate cluster head list of V i C i Cluster i whose cluster head is V i CM N C i Number of cluster members of C i CM N max Maximum number of cluster members of a cluster CM L i Cluster member list of V i whose state is Cluster Head (CH)

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Section: A Vanets Scenariosmentioning

confidence: 99%

A New Affinity Propagation Clustering Algorithm for V2V-Supported VANETs

Baishun

Shi

et al. 2020

IEEE Access

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“…Ou seja, cada linha do arquivo de trace contém informações de tempo (em intervalos de 1s), do identificador do veículo, da posição do veículo em um plano bidimensional e a velocidade do veículo em metros por segundo. A comunidade acadêmica considera a base de dados TAPASColone como uma das mais realistas disponível publicamente para uso [Naboulsi and Fiore 2017.…”

Section: Bases De Dados Utilizadasunclassified

Avaliação da confiabilidade de modelos de mobilidade sintéticos aplicados em redes veiculares

Silva¹,

Oliveira²,

Aquino³

2020

Anais Do Simpósio Brasileiro De Computação Ubíqua E Pervasiva (SBCUP 2020)

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Avaliar aplicações e protocolos para redes veiculares é um requisito para implantá-los em ambientes reais. Geralmente, essa avaliação acontece por meio de simulações, por permitir que essas avaliações sejam feitas com baixo custo e em larga escala. Porém, para que as simulações produzam resultados realista é necessário que os modelos de mobilidade utilizados pelos simuladores representem fidedignamente o comportamento observado em cenários reais. Neste trabalho, nós apresentamos um algoritmo de roteamento epidêmico para redes veiculares e comparamos a confiabilidade do resultados produzidos por um modelo de mobilidade com os resultados produzidos em um cenário real. Os resultados mostram que o modelo de mobilidade não reproduz a mobilidade real, e consequentemente, as aplicações avaliadas em cenários sintéticos não apresentam os mesmos resultados quando avaliadas em cenários reais.

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“…Using the efficient storage technique and computation mechanism, the space-time complexity can be reduced obviously. In [14], Naboulsi et al studied the instantaneous topology of realistic urban scenarios, i.e., Cologne and Zurich. The results showed that a poor connectivity could cause limited availability and reliability.…”

Section: Related Workmentioning

confidence: 99%

Connectivity Analysis in Vehicular Networks with Slight Traffic Interference

Pan

2018

Wireless Communications and Mobile Computing

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This paper analyzes the network connectivity in vehicular networks with slight traffic interference, i.e., small-scale traffic accident. In this paper, we develop an analytical model for highway scenarios or sparse urban scenarios, where the slight traffic interference can make the vehicles slow down rather than block the traffic flow. When a traffic accident occurs, it is necessary to inform the nearby vehicles to slow down in order to reduce congestion at the accident location. Once they pass by, they can return to the normal value. Consequently, we can divide an entire road into two or three subsections, which helps us to analyze the connectivity performance. In addition, we analyze the impact of several key parameters on connectivity probability, including vehicle arrival rate, vehicle communication range, length of road, vehicle normal speed, and safe speed. All analytical results are verified through Monte Carlo simulation experiments. The simulation results are very close to analytical results, which means that the analytical results are accurate and the analytical model we propose is effective.

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Characterizing the Instantaneous Connectivity of Large-Scale Urban Vehicular Networks

Cited by 50 publications

References 47 publications

A New Affinity Propagation Clustering Algorithm for V2V-Supported VANETs

A New Affinity Propagation Clustering Algorithm for V2V-Supported VANETs

Avaliação da confiabilidade de modelos de mobilidade sintéticos aplicados em redes veiculares

Connectivity Analysis in Vehicular Networks with Slight Traffic Interference

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