Deployment of roadside units based on partial mobility information

Silva, Cristiano M.; Aquino, André L. L.; Meira, Wagner

doi:10.1016/j.comcom.2015.01.021

Cited by 52 publications

(24 citation statements)

References 26 publications

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“…A heuristic algorithm based on the work of (Trullols et al, 2010) is introduced, which starts with the cell with the highest vehicle concentration and then iteratively projects the flow of uncovered vehicles between the cells and selects the cells with the highest projection of the vehicle flow, until the given number of RSUs is opened. Reference (Silva et al, 2016) improves the work of (Silva et al, 2015) significantly. Unlike the work of (Silva et al, 2015) that uses only the migration ratios of 4 out of 8 adjacent cells to project the flow of uncovered vehicles, Reference (Silva et al, 2016) considers the migration ratios between all pairs of urban cells.…”

Section: Budget-constrained Rsu Deploymentmentioning

confidence: 99%

“…To protect the privacy of drivers during the planning stage, Reference (Silva et al, 2015) introduces a probabilistic maximum coverage problem (PMCP) for allocating RSUs using only partial mobility information, instead of full knowledge of vehicle trajectories. Partial mobility information consists of the turning probability of vehicles at intersections, or in general term, the probability of migrating vehicles from an urban cell to its adjacent cell.…”

Section: Budget-constrained Rsu Deploymentmentioning

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: Budget-constrained Rsu Deploymentmentioning

confidence: 99%

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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“…This method only considers communication among the buses and thus cannot be applied to roads with high traffic volume. P. Patil et al in [4] proposed Voronoibased RSU deployment algorithm, and C. M. Silva et al [5] suggested RSU deployment algorithm derived from partitioning the roads into grid cells. Using the migration ratios between the adjacent cells has been suggested.…”

Section: Related Workmentioning

confidence: 99%

“…Recently many researches on efficient RSU deployment have emerged [2][3][4][5], and we have also proposed a couple of RSU allocation strategies [6][7][8] which can cover intersections based on the concept of intersection connectivity. The intersection connectivity means the probability of message transmission between any two intersections [2].…”

Section: Introductionmentioning

confidence: 99%

Influence Maximized MCL based RSU Deployment

Whang¹,

Park²,

Hee

2016

IJFGCN

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“…Patil and Gokhale [8] proposed Voronoi-based RSU-deployment algorithms. C. M. Silva et al [7] partitioned roads into grid cells, and then found RSU locations using both the traffic volume in a grid cell and the migration ratios between adjacent cells.…”

Section: Related Workmentioning

confidence: 99%