Roadside unit deployment for information dissemination in a VANET

Cavalcante, Evellyn S.; Aquino, André L. L.; Pappa, Gisele L.; Loureiro, Antônio A. F.

doi:10.1145/2330784.2330789

Cited by 69 publications

(39 citation statements)

References 24 publications

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“…In 2011-2012, we notice works addressing infrastructure deployment in terms of probabilistic models [85,86], linear programming formulations [87][88][89], heuristics [90], genetic algorithms [91], and game-theory [92]. In terms of architecture, we notice analytic models for the selection of communicating devices [88,93], proposals for light-weight infrastructures employing relay nodes [13], virtual infrastructures using the publish-subscribe paradigm [52], biologically inspired solutions [94], and reputation mechanisms [45].…”

Section: Overview Of the Research In Infrastructure-based Vehicular Nmentioning

confidence: 99%

“…The minimal initial deployment of RSUs is handled by a genetic algorithm based on the travel time savings. Cavalcante et al [91] apply genetic programming to solve the deployment of RSUs in vehicular networks. Such technique starts with an initial set of possible solutions combined across generations until some stop condition is reached.…”

Section: Deployment Strategies Based On Evolutionary Approachesmentioning

confidence: 99%

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A Survey on Infrastructure-Based Vehicular Networks

Silva¹,

Masini²,

Ferrari³

et al. 2017

Mobile Information Systems

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The infrastructure of vehicular networks plays a major role in realizing the full potential of vehicular communications. More and more vehicles are connected to the Internet and to each other, driving new technological transformations in a multidisciplinary way. Researchers in automotive/telecom industries and academia are joining their effort to provide their visions and solutions to increasingly complex transportation systems, also envisioning a myriad of applications to improve the driving experience and the mobility. These trends pose significant challenges to the communication systems: low latency, higher throughput, and increased reliability have to be granted by the wireless access technologies and by a suitable (possibly dedicated) infrastructure. This paper presents an in-depth survey of more than ten years of research on infrastructures, wireless access technologies and techniques, and deployment that make vehicular connectivity available. In addition, we identify the limitations of present technologies and infrastructures and the challenges associated with such infrastructure-based vehicular communications, also highlighting potential solutions.

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Section: Overview Of the Research In Infrastructure-based Vehicular Nmentioning

confidence: 99%

Section: Deployment Strategies Based On Evolutionary Approachesmentioning

confidence: 99%

A Survey on Infrastructure-Based Vehicular Networks

Silva¹,

Masini²,

Ferrari³

et al. 2017

Mobile Information Systems

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“…For more of these approaches, see References (Campolo et al, 2011;Barrachina et al, 2013;Cavalcante et al, 2012;Xie et al, 2013;Jiang et al, 2014;Brahim et al, 2014;Eftekhari et al, 2015). …”

Section: Budget-constrained Rsu Deploymentmentioning

confidence: 99%

Combining Capital and Operating Expenditure Costs in Vehicular Roadside Unit Placement

Nikookaran

Karakostas

Todd

2017

IEEE Trans. Veh. Technol.

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Vehicular ad-hoc networks will be the next step towards intelligent transportation systems. Roadside infrastructure is a key component of these systems that will eventually support various applications such as road safety, transportation services, infotainment, and in-vehicle Internet access.This thesis considers the problem of roadside unit (RSU) placement and configuration in vehicular networks. The goal is to select the RSU locations and configurations such that the sum of capital and operational expenditure costs is minimized. Historical vehicular traffic traces and a set of RSU candidate locations are used as inputs.First, the problem is formulated as an integer linear program (ILP), which provides a lower bound on the total cost, and can be found for moderate size problems. A practical algorithm called Minimum Cost Route Clustering (MCRC) is then introduced that solves a relaxed version of the ILP and uses a novel rounding procedure to obtain real RSU placements. The algorithm takes into account the energy costs incurred by transmitting vehicular requests when the latter are scheduled using a minimum energy online scheduler. Performance results are presented that show that the proposed algorithm performs well compared to the case where placements are done without considering both capital and operational expenditure costs.iii The problem of capacity augmentation is then addressed, as a way of adjusting the initial RSU network design, and serves to counterbalance its failure to take causality into account. The objective is to find an RSU radio capacity assignment that minimizes the long-term operating expenditure costs subject to meeting packet deadline constraints, subject to a given packet loss rate target. A procedure, referred to as the Capacity Augmentation (CA) Algorithm, is proposed that iterates over the RSUs, selecting candidates for capacity augmentation based on their packet loss rate sensitivities. A variety of results is presented that characterize and compare the performance of the CA Algorithm using a greedy online packet scheduler. It is shown that the CA Algorithm is an efficient way to assign RSU radio capacity that achieves the desired performance objectives.

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“…In [36], the authors optimize the deployment of RSU in VANET scenarios. They define the optimization problem as a Maximum Coverage with Time Threshold Problem (MCTTP).…”

Section: Topology Managementmentioning

confidence: 99%

A Survey on the Application of Evolutionary Algorithms for Mobile Multihop Ad Hoc Network Optimization Problems

Reina

Ruiz

Ciobanu

et al. 2016

International Journal of Distributed Sensor Networks

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Evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been applied to many optimization problems in a high number of scientific areas. In this survey paper, we focus on the application of evolutionary algorithms to solve optimization problems related to a type of complex network like mobile multihop ad hoc networks. Since its origin, mobile multihop ad hoc network has evolved causing new types of multihop networks to appear such as vehicular ad hoc networks and delay tolerant networks, leading to the solution of new issues and optimization problems. In this survey, we review the main work presented for each type of mobile multihop ad hoc network and we also present some innovative ideas and open challenges to guide further research in this topic.

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Roadside unit deployment for information dissemination in a VANET

Cited by 69 publications

References 24 publications

A Survey on Infrastructure-Based Vehicular Networks

A Survey on Infrastructure-Based Vehicular Networks

Combining Capital and Operating Expenditure Costs in Vehicular Roadside Unit Placement

A Survey on the Application of Evolutionary Algorithms for Mobile Multihop Ad Hoc Network Optimization Problems

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