RSUs placement using cumulative weight based method for urban and rural roads

Makkawi, Ahmed; Daher, Robil; Rizk, Ramy

doi:10.1109/rndm.2015.7325245

Cited by 16 publications

(10 citation statements)

References 8 publications

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“…This process continues until it reaches the operator stopping criteria. Reference (Makkawi et al, 2015) continues the previous work, by introducing a cumulative weight-based method (CWM) for placing RSUs in urban, rural and mountain areas. Again, it is assumed that a sorted list of SoIs based on operator preferences is given to the RSU placement algorithm.…”

Section: Budget-constrained Rsu Deploymentmentioning

confidence: 95%

“…(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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Section: Budget-constrained Rsu Deploymentmentioning

confidence: 95%

Section: Related Workmentioning

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 approach is complemented in [311] by proposing the use of satellites to transmit downlink traffic to VANETs in rural areas without RSUs, until the number of RSU deployments increases. RSU placement can be optimized using the weighted approach of [312], where RSUs are placed first in locations with higher weights. It was shown in [312] that taking into account 3D space in rural mountainous areas leads to more accurate results.…”

Section: Using Local Contentmentioning

confidence: 99%

A Key 6G Challenge and Opportunity - Connecting the Remaining 4 Billions: A Survey on Rural Connectivity

Yaacoub¹,

Alouini²

2019

Preprint

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DRAFT 4) Hyper Connected: This scenario corresponds to people enjoying state-of-the-art connectivity, e.g., at least 4G and possibly 5G cellular connectivity, and/or with high speed fixed DSL or FTTH or FTTN connectivity. These subscribers typically live in urban areas, and it is extremely difficult to find them in rural areas, due to the numerous barriers discussed previously. This paper surveys the literature aiming to provide basic connectivity to the unconnected population, or aiming to increase the connectivity level of those with basic connectivity ("Under Hence, the challenges for FSO in space are less critical, and indeed significantly better performance can be achieved, especially due to the fact that the optical signal is not traversing the atmosphere. In 2008, an optical inter-satellite link achieved a data rate transmission of 5.6 Gbps for distances up to 5, 000 km in the European Data Relay satellite System (EDRS). Newer terminals can transmit at a data rate of 1.8 Gbps for distances up to 45, 000 km. The intersatellite Ka band link achieved 300 Mbps [44]. In EDRS, LEO satellites relay the data from ground stations to GEO satellites using FSO for the GEO-LEO satellite communication, and RF bands for communication with the ground stations (similarly to the satellite-satellite Ka link, the satellite-ground Ka link achieved 300 Mbps) [44]. Some of the recent satellite constellations discussed in Section II-E, e.g., Starlink, are also expected to use FSO for its inter-satellite communication [45,46,47]. D. HAPs/Drones/UAVs/BalloonsTo solve the coverage problem in large rural areas without relying on the deployment of costly wired infrastructure for backhaul, HAPs and UAVs are generally recommended [48].UAVs are proposed in [49] to provide backhaul connectivity for ground 5G base stations. A steering algorithm to allow the antennas of the UAV and the ground BS to be dynamically steered towards each other is proposed in [49], and tested via an experimental setup. In[50], a multihop network of HAPs is proposed for providing backhaul connectivity to TVWS deployments in rural areas. The HAPs communicate with each other using mmWave frequencies, and with the ground stations using FSO.An interference alignment scheme to maximize the sum-rate capacity of HAPs communicating with GSs is proposed in [51]. The scheme assumes M HAP drones and N GSs, with no CSI at the HAPs. A tethered balloon with (M − 1) × (N − 1) antennas is used as a decode and forward (DF) relay with half-duplex operation: The HAPs transmit their data to the GSs first, then the tethered balloon relays the data after performing pre-coding [51].Aerostats filled with lighter than air gas are tethered to the ground and used to provide connectivity to rural areas in [52]. Although these tethered aerostats can be used to provide backhaul connectivity by communicating with each other using directive antennas, they were also used in [52] to provide fronthaul WiFi access by using high gain omnidirectional antennas.

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“…As for the RSU deployment policies, several works have been proposed so far, including different approaches to determine the number of RSUs required to provide a functional RSU deployment in terms of connectivity within a given scenario [8]. This sort of approach is focused on reducing the number of RSUs required [9,10], improving the overall network performance [11][12][13] or guarantying the Quality-of-Service (QoS) in vehicular networks when delivering data [14].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, those works rely on different techniques such as clustering [3], cumulative weight [8], time continuous homogeneous Markov chains [9], game theoretic tools [19] and Integer Linear Programming (ILP) [15].…”

Section: Related Workmentioning

confidence: 99%

Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms

et al. 2018

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Vehicular networks make use of the Roadside Units (RSUs) to enhance the communication capabilities of the vehicles in order to forward control messages and/or to provide Internet access to vehicles, drivers and passengers. Unfortunately, within vehicular networks, the wireless signal propagation is mostly affected by buildings and other obstacles (e.g., urban fixtures), in particular when considering the IEEE 802.11p standard. Therefore, a crowded RSU deployment may be required to ensure vehicular communications within urban environments. Furthermore, some applications, notably those applications related to safety, require a fast and reliable warning data transmission to the emergency services and traffic authorities. However, communication is not always possible in vehicular environments due to the lack of connectivity even employing multiple hops. To overcome the signal propagation problem and delayed warning notification time issues, an effective, smart, cost-effective and all-purpose RSU deployment policy should be put into place. In this paper, we propose the genetic algorithm for roadside unit deployment (GARSUD) system, which uses a genetic algorithm that is capable of automatically providing an RSU deployment suitable for any given road map layout. Our simulation results show that GARSUD is able to reduce the warning notification time (the time required to inform emergency authorities in traffic danger situations) and to improve vehicular communication capabilities within different density scenarios and complexity layouts.

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RSUs placement using cumulative weight based method for urban and rural roads

Cited by 16 publications

References 8 publications

Combining Capital and Operating Expenditure Costs in Vehicular Roadside Unit Placement

Combining Capital and Operating Expenditure Costs in Vehicular Roadside Unit Placement

A Key 6G Challenge and Opportunity - Connecting the Remaining 4 Billions: A Survey on Rural Connectivity

Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms

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