RSUs placement using overlap based greedy method for urban and rural roads

Rizk, Ramy; Daher, Robil; Makkawi, Ahmed

doi:10.1109/nets4carsfall.2014.7000905

Cited by 15 publications

(8 citation statements)

References 9 publications

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“…During the driving process between two RSUs, the inhibition distance is expressed by the overlap time coverage ratio O t ij � L 1 /L 2 , that is, the length of the overlap divided by the distance between the two centers. Rizk et al [21] pointed out that as the vehicle moves away from the center of the RSU, the coverage signal gradually attenuates, and appropriate overlap can ensure the switching time between the vehicle and the RSU. As shown in Figures 1(b) and 1(c), the value range of O t ij is from 0 to 1.…”

Section: Improved Hotspot Discovery Algorithmmentioning

confidence: 99%

Roadside Unit Deployment Mechanism Based on Node Popularity

Mao

Peng

Zhang

et al. 2021

Mobile Information Systems

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Internet of Vehicles (IoV) can significantly improve the driving conditions of vehicles, but the mobility characteristics of vehicles put forward higher requirements for the robustness of the IoV system. As an important component of the IoV, the deployment of RoadSide Unit (RSU) directly influences the service performance of the IoV system to vehicles, and reasonable RSU deployment can save resource costs and improve system operating efficiency. This paper proposes an RSU deployment mechanism based on the popularity of road intersections, which uses three traffic parameters, vehicle contact time, intersection connectivity, and intersection coverage capacity, as the main reference indicators. Meanwhile, an improved hotspot discovery algorithm (IHDA) is utilized to introduce inhibition distance during deployment to reduce interference between RSUs. The simulation results demonstrate that, compared with the existing typical deployment methods, this program can improve the coverage time ratio and the contacts of vehicles per trip.

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Section: Improved Hotspot Discovery Algorithmmentioning

confidence: 99%

Roadside Unit Deployment Mechanism Based on Node Popularity

Mao

Peng

Zhang

et al. 2021

Mobile Information Systems

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“…There are many factors that influence RSU placement decisions, such as the topological and topographical characteristics of road networks, the temporal and spatial traffic characteristics of the roads, present and projected traffic patterns, the availability of the communication and power supply networks along the roads, the variety of services that are emerging and their communication profile, and the requirements of the road operator (Patil and Gokhale, 2013;Rizk et al, 2014).…”

Section: Phd Thesis -Naby Nikookaranmentioning

confidence: 99%

“…In Reference (Rizk et al, 2014), the RSU placement problem is considered as a maximum coverage problem in urban and rural areas. In this work, any site of interest (SoI) can be a candidate location for RSU placement, including intersections, road curves, or any other points that can benefit from installing RSUs.…”

Section: Budget-constrained Rsu Deploymentmentioning

confidence: 99%

“…(Wu et al, 2012), the number of vehicles that get in contact with at least one vehicle (Trullols et al, 2010;Silva et al, 2015Silva et al, , 2016, coverage (Patil and Gokhale, 2013;Rizk et al, 2014;Makkawi et al, 2015;Cheng et al, 2015), delay (Aslam and Zou, 2011;Aslam et al, 2012). In this category, either the total number of RSUs that is allowed to be deployed or the total available budget for RSU deployment is limited to what authorities determined.…”

Section: Related Workmentioning

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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“…Rizk [4] presented a greedy method for RSU placement in urban and rural roads, in which covers throughout roads district and minimizes the overlap area between RSUs.…”

Section: Introductionmentioning

confidence: 99%

Binary programming model to optimize RSU placement for information dissemination

Eftekhari

Bashirzadeh

Ghatee

2015

2015 International Conference on Connected Vehicles and Expo (ICCVE)

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Vehicular communication systems are developed not only to increase safety but also for mobility of road transportation. Roadside units (RSU) are the prominent elements of this technology. This equipment is installed in roadside and intersections to gather traffic information from vehicles and to send messages and alarms to vehicles. Due to costly implementation and maintenance of this equipment, determining the number of RSUs and placement of them is an important problem. In this paper, we proposed a novel binary programming (BP) model to a placement of RSUs beside a road for maximizing information dissemination to vehicles. This approach makes decisions based on the number of curves, the number of on-ramps, accident rate, weather condition, and cost limitations. The proposed model is applied on Tehran to Pardis freeway. According to computational experiments, four operational phases to equip the whole road for information dissemination is obtained.

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RSUs placement using overlap based greedy method for urban and rural roads

Cited by 15 publications

References 9 publications

Roadside Unit Deployment Mechanism Based on Node Popularity

Roadside Unit Deployment Mechanism Based on Node Popularity

Combining Capital and Operating Expenditure Costs in Vehicular Roadside Unit Placement

Binary programming model to optimize RSU placement for information dissemination

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