<i>RoadGate</i>: Mobility-Centric Roadside Units Deployment for Vehicular Networks

Xiong, Yu; Ma, Jian; Wang, Wendong; Tu, Dengbiao

doi:10.1155/2013/690974

Cited by 25 publications

(20 citation statements)

References 24 publications

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“…(Rashidi et al, 2012;Abdrabou and Zhuang, 2011;Xiong et al, 2013;Patil and Gokhale, 2013). In a typical vehicular network, vehicles can be equipped with various sensors to collect real-time traffic and environmental data.…”

Section: Intelligent Transportation System (Its) Related Servicesmentioning

confidence: 99%

“…All sensor readings will be tagged with a timestamp and the geographical coordinates, which can be obtained, e.g., from a GPS, and will be reported to a remote control center through an infrastructure. The processed data can be accessed by vehicles through the same infrastructure (Xiong et al, 2013;Rashidi et al, 2012;Abdrabou and Zhuang, 2011).…”

Section: Intelligent Transportation System (Its) Related Servicesmentioning

confidence: 99%

“…and commercial applications (e.g., location-based advertising, etc.) (Trullols et al, 2010;Abdrabou and Zhuang, 2011;Xiong et al, 2013;Campolo et al, 2011;Patil and (Liu et al, 2013).…”

Section: Information and Entertainment (Infotainment)mentioning

confidence: 99%

“…RSU infrastructure is another solution that can provide mobile vehicles with high-speed and low-cost Internet access services (Liu et al, 2013). As reported in (Xiong et al, 2013), the feasibility of 15 Ph.D. Thesis -Naby Nikookaran…”

Section: Roadside Units As Infrastructurementioning

confidence: 99%

“…In fact, deploying more RSUs can guarantee a better connectivity, coverage and performance (Campolo et al, 2011;Patra et al, Ph.D. Thesis -Naby Nikookaran McMaster -Electrical & Computer Engineering 2014). However, installing a sufficient number of RSUs to provide full coverage such that every vehicle can always be connected to at least one RSU leads to large installation and maintenance costs (Abdrabou and Zhuang, 2011;Xiong et al, 2013;Li et al, 2015). A simple RSU, for example, has a CAPEX of 13, 000 to 15, 000 USD per unit, and OPEX of up to 2, 400 USD per unit per year (Tonguz and Viriyasitavat, 2013).…”

Section: Assumptions and Challengesmentioning

confidence: 99%

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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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Section: Intelligent Transportation System (Its) Related Servicesmentioning

confidence: 99%

Section: Intelligent Transportation System (Its) Related Servicesmentioning

confidence: 99%

“…and commercial applications (e.g., location-based advertising, etc.) (Trullols et al, 2010;Abdrabou and Zhuang, 2011;Xiong et al, 2013;Campolo et al, 2011;Patil and (Liu et al, 2013).…”

Section: Information and Entertainment (Infotainment)mentioning

confidence: 99%

Section: Roadside Units As Infrastructurementioning

confidence: 99%

Section: Assumptions and Challengesmentioning

confidence: 99%

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Combining Capital and Operating Expenditure Costs in Vehicular Roadside Unit Placement

Nikookaran

Karakostas

Todd

2017

IEEE Trans. Veh. Technol.

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A roadside unit deployment framework for enhancing transportation services in Maghrebian cities

Chaabene

Yeferny

Yahia

2019

Concurrency and Computation

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Summary Roadside units (RSUs) have a crucial role in maintaining vehicular ad hoc networks (VANETs) connectivity and coverage, especially, for applications gathering or disseminating nonsafety information. In big cities with complex road network topology, a huge number of costly RSUs must be deployed to collect data gathered by all moving vehicles. In this respect, several research works focusing on RSUs deployment have been proposed. The thriving challenge would be to (1) reduce the deployment cost by minimizing as far as possible the number of used RSUs and (2) to maximize the coverage ratio. In this paper, we introduce a spatiotemporal RSU deployment framework including three methods, namely, SPaCov, SPaCov+, and HeSPic. SPaCov starts by mining frequent mobility patterns of moving vehicles from their trajectories; then, it computes the best RSU locations that cover the extracted patterns. Nonetheless, SPaCov+ extracts the frequent mobility patterns as well as the rare ones to enhance the coverage ratio. HeSPiC is a budget‐constrained spatiotemporal coverage method that aims to maximize the coverage ratio subject to a budget constraint, which is defined in terms of RSU number. Performed simulations highlight the efficiency and the effectiveness of the proposed RSU deployment framework in terms of coverage ratio, deployment cost, network latency and overhead.

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MPC: A RSUs deployment strategy for VANET

Yeferny

Allani

2018

Int J Communication

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Summary A Vehicle AdHoc Network is mainly composed of mobile vehicles and fixed Road Site Units (RSUs). The latter is usually very expensive to deploy and has a crucial role in maintaining the network connectivity. Therefore, the design of an efficient RSU deployment strategy that enables a high coverage ratio and a lower deployment cost has been of paramount importance. In this respect, we introduce in this paper a new spatiotemporal coverage strategy for nonsafety Vehicle AdHoc Network applications like driving assistance and business promotion, called Minimal Mobility Patterns Coverage (MPC). The main thrust of MPC is to (1) depict the mobility patterns of moving vehicles from their trace files and then (2) compute the adequate RSU locations in order to cover the extracted mobility patterns by the minimal possible number of RSUs. To this end, we firstly provide a new method to depict the mobility patterns of vehicles by mining the correlations between the kept track connections of vehicle trajectories versus crossed junctions. Secondly, we introduce a new way to compute the adequate RSU locations through the instantiation of the well‐known problem of extracting minimal transversals of a hypergraph. Experimental results show that our RSUs deployment strategy performs better than baseline strategies.

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RoadGate: Mobility-Centric Roadside Units Deployment for Vehicular Networks

Cited by 25 publications

References 24 publications

Combining Capital and Operating Expenditure Costs in Vehicular Roadside Unit Placement

Combining Capital and Operating Expenditure Costs in Vehicular Roadside Unit Placement

A roadside unit deployment framework for enhancing transportation services in Maghrebian cities

MPC: A RSUs deployment strategy for VANET

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