Message coverage maximization in infrastructure-based urban vehicular networks

Jalooli, Ali; Song, Min; Wang, Wenye

doi:10.1016/j.vehcom.2019.02.001

Cited by 22 publications

(12 citation statements)

References 32 publications

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“…Similarly, to perform urban sensing for a desired coverage, given a limited budget, the authors of [11] proposed the recruitment of high reputation users. Additionally, the authors of [12] have focused on how to maximize message coverage in urban vehicular networks by the optimal deployment of roadside units. In their proposed message coverage maximization algorithm, namely MCMA, the authors have considered traffic stream and delay constraint of applications to attentively identify the appropriate site for RSUs.…”

Section: Related Workmentioning

confidence: 99%

“…Regarding aforementioned studies, it is important to note that data collection approaches remain ineffective in a dynamic environment, since they are not based on stable criteria for the selection of candidate vehicles that should be important and available to meet the relevant user interests in the network. In addition, they are not scalable data collection for a given coverage and budget constraints [11,12]. Despite that the authors of [10] have adopted the Information-Centric Networking (ICN) concept, in their work, the information importance remains location-based, depends on the content popularity and computed through the observation of the amount and frequency of user interests it received, where they neglected whether the receiver is satisfied or not with this content.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, there is no proper metric to classify and identify the participants' eligibility that has been used by the authors of [11]. In [12], the proposed deployment strategy can notably be influenced by large-scale mobility model and road layout.…”

Section: Related Workmentioning

confidence: 99%

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BSM-Data Reuse Model Based on In-Vehicular Computing

2020

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Basic Safety Messages that are frequently generated from multiple connected vehicles can play a primordial role in providing transport data see credible and reliable information they contain. Otherwise, when considering the way Basic Safety Messages (BSMs) are treated, multiple deficiencies prevent the latter to be capable of constituting a precious data source. As we know, data become more useful the more widely are used, which is the exact opposite of what happens with the BSMs that exist only temporarily, used locally, considered disposable, and are never stored. In this paper, we introduce a data reuse model that retains collected BSMs, stores, and processes them inside the vehicle constituting a continuous data source holding retained snapshots along the roadway. Our model provided a primary data source available on a large scale, considered to be a worthy dataset for machine learning tasks, capable of visualizing different traffic-related indicators to enhance analytics and support decisions-making. In the study case, we set up an in-vehicle data platform, where we achieved an 80% of BSMs size reduction and provided a rich set of APIs to serve applications. We also adopted the Artificial Neural Networks (ANN) as an information processing paradigm for performing traffic volume prediction, where the obtained results have reached over 99% of accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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BSM-Data Reuse Model Based on In-Vehicular Computing

2020

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“…From the recent decade, Vehicular ad-hoc network (VANET) is considered to be the emerging technology that has invited huge amount of attention in the field of academics and industry [1]. The inclusion of VANETs in the Intelligent Transportation System (ITS) is determined to wide opened the way for predominant enhancement of the road safey and guaranteed reliable real time services with efficient through vehicle-to-vehicle (V2V) communications and vehicle-to-infrastructure (V2I) communications [2].…”

Section: Introductionmentioning

confidence: 99%

“…The maximum network coverage area of RSUs is considered to be achieved, if the total area covered by the RSUs is at least 3 times of the complete area covered by the CDT as the total area is partitioned into a number of CDTs. Thus, the total network area coverage facilitated by the three deployed RSUs are determined based on Equation(1) The total angle enveloped by a CFDTR riangularization method is 180 degrees, thus the the total network area coverage facilitated by the three deployed RSUs is estimated based on Equation…”

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confidence: 99%

Chews First Delaunay Triangulation refinement Scheme-Based Positioning of RSUs for Optimal Network Coverage in VANETs

Selvakumari¹,

Lakshmi²,

Sheela³

et al. 2021

Preprint

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The temporal network fragmentation and uncertain vehicle mobility are considered to impact the communication connectivity among the vehicular nodes of the network. In this context, Road Side Units (RSUs) play an anchor role in enhancing the vehicle-to-vehicle (V2V) communication connectivity and support Vehicle-to-Infrastructure (V2I) communication. In the current scenario, deploying a huge number of RSUs in the vehicular networks at an initial stage is completely impossible as it incurs high installation cost and authority restriction. Moreover, the optimal placement of RSUs in the vehicular network needs to be enforced for attaining maximized network coverage. In this paper, Chews First Delaunay Triangulation Refinement Scheme (CFDTRS) is proposed for optimal positioning of RSUs in order to attain optimal network coverage in the vehicular network with minimized cost. This CFDTRS considered the factors of vehicular density, number of obstacles in the map, intersection popularity and global coverage into account during the placement of RSUs. It is proposed with the objective of deploying required number of RSUs within the range of data transmission in order to achieve maximal coverage in the convex map, such that each area of the convex map is completely covered with at least a single RSU in the existence of multiple number of obstacles. The simulation results of the proposed CFDTRS attained from the real time situations of simplex, moderate and complex maps confirmed improved packet delivery rate of 9.32%, throughput by 10.74% with minimized packet loss of 9.14% and reduced end-to-end delay of 19.31$, compared to the benchmarked schemes considered for investigation.

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An enhanced pseudonym certificates distribution mechanism for connected vehicles

Ercan

Ayaida

Messai

2022

Int J Communication

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Summary Cooperative Intelligent Transportation System (C‐ITS) is a growing research field since it improves traffic safety and efficiency. However, the security of C‐ITS messages is an important issue to deal with. This paper proposes a cost‐efficient solution that can help in the deployment of C‐ITS. The idea herein presented is to introduce trusted road operator vehicles (ROVs), to provide certificates for other vehicles when needed. These certificates, which are generally provided by roadside units (RSUs), are mandatory for the security and the anonymity of the communication. The main objective of this work is to help in enhancing the security of C‐ITS by allowing connected vehicles to download the needed certificates without using a physical fixed infrastructure of RSUs. The latter cannot be, in fact, widely deployed due to their high cost. The alternative herein proposed is to delegate the task of providing certificates to some trusted vehicles, namely, ROVs, which are in all the cases moving around the city to ensure some assigned missions. A mathematical model, considering both ROVs and RSUs, is proposed to quantify the effects of introducing ROVs on the probability of downloading certificates. The results show that ROVs almost double the communication probability between the vehicles and the certificate providers from 40.1% to 78.7% on a highway with a high traffic density. The higher the communication probability, the easier downloading certificates is. Moreover, realistic simulations are conducted using Simulation of Urban Mobility (SUMO) traffic simulator together with Network Simulator 3 (NS3) network simulator to show the efficiency of our proposal. The simulation results demonstrate a significant enhancement in the number of downloaded certificates and the number of exchanged messages, even when a few numbers of ROVs are introduced.

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Message coverage maximization in infrastructure-based urban vehicular networks

Cited by 22 publications

References 32 publications

BSM-Data Reuse Model Based on In-Vehicular Computing

BSM-Data Reuse Model Based on In-Vehicular Computing

Chews First Delaunay Triangulation refinement Scheme-Based Positioning of RSUs for Optimal Network Coverage in VANETs

An enhanced pseudonym certificates distribution mechanism for connected vehicles

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