Tomographic Reconstruction of RF Propagation to Aid VANET Routing in Urban Canyons

Kopp, Carlo; Tyson, Michael Russell; Pose, Ronald

doi:10.1109/tvt.2016.2528341

Cited by 8 publications

(4 citation statements)

References 47 publications

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“…GVANET Intelligent Transformation (GVITi) is represented mathematically as in Eq. (9). Z1 53 43 51 44 37 54 51 23 Z2 21 13 15 13 13 17 17 12 Z3 30 13 26 21 23 26 22 13 Z4 51 44 47 43 56 53 48 47 Z5 57 54 46 53 37 54 54 43 Z6 15 12 12 23 20 17 12 13 Z7 37 17 14 13 13 37 37 21 Fig.…”

Section: And Infrastructure and Vehicle And Vehicle To Vehiclementioning

confidence: 98%

“…A computational model for flow and boundary shear stress fields in natural channels is combined with a bedload transport algorithm in order to investigate the genesis and finite-amplitude characteristics of riverine bars. [9] Since the time scales associated with bar growth and migration are typically much larger than the time scales of the flow, the flow is treated in a quasisteady manner, and the modification of the bed topography due to convergences and divergences in the sediment flux field is coupled to the flow model using a simple iterative technique [10,11].…”

Section: Introductionmentioning

confidence: 99%

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2M2C-R2ED: Multi-Metric Cooperative Clustering Based Routing for Energy Efficient Data Dissemination in Green-VANETs

Dhasarathan¹,

Dumka

Loganathan

2020

Technol Econ Smart Grids Sustain Energy

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The aim of this paper is to focus on energy dissemination for green vehicle transportation. In current scenario population of urban areas keeps increasing as the people move towards the cities in search of jobs and betterlife style. This leads to an increase in the number of vehicles on the road. But the transport network which is accessible to the citizens is less when compared to their demand. This situation results in more vehicles on the road. The result of which is one of the factors that aggravate the traffic congestion. It is identified the vehicle volume on road is 91.7% which increases the CO2 emission to 93.25% every month. 2M2C-R2ED shows its efficiency in identification and segregation of alternative route by IWS with an average frequency of 3.82%. Traffic congestion occurs when the available transport resources are less when compared to the number of vehicles that share the resource. As the number of vehicles increases the resources become scarce and congestion is more. The demand for the resources is higher than the actual capacity of the roads and the streets. There are some circumstances that which will aggravate the traffic congestion. The circumstances can be the road condition (potholes, road repair), accidents and some natural calamities. There is a lot of research being done to predict the traffic and model it in order to find a solution that will make the condition better. But still it is an open issue. The accuracy of the predictions done is less.

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Section: And Infrastructure and Vehicle And Vehicle To Vehiclementioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

2M2C-R2ED: Multi-Metric Cooperative Clustering Based Routing for Energy Efficient Data Dissemination in Green-VANETs

Dhasarathan¹,

Dumka

Loganathan

2020

Technol Econ Smart Grids Sustain Energy

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“…The event dissemination from the road side units are controlled by the service providers. The event dissemination is done in such a way that it maximizes the subscription and the same time it minimizes the cost of event disseminations (Kopp et al, 2016). The communication is done between the roadside units and the vehicles, but this paper does not address the communication between V2V communications.…”

Section: Review On Energy Efficient Data Disseminationmentioning

confidence: 99%

Data dissemination for green-VANETs communication: an opportunistic optimization approach

Dhasarathan¹,

Dumka

Dhilipkumar

et al. 2020

IJPCC

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Purpose This paper aims to predict the traffic and helps to find a solution. Unpredictable traffic leads more vehicles on the road. The result of which is one of the factors that aggravate traffic congestion. Traffic congestion occurs when the available transport resources are less when compared to the number of vehicles that share the resource. As the number of vehicles increases the resources become scarce and congestion is more. Design/methodology/approach The population of the urban areas keeps increasing as the people move toward the cities in search of jobs and a better lifestyle. This leads to an increase in the number of vehicles on the road. However, the transport network, which is accessible to the citizens is less when compared to their demand. Findings The demand for resources is higher than the actual capacity of the roads and the streets. There are some circumstances, which will aggravate traffic congestion. The circumstances can be the road condition (pot holes and road repair), accidents and some natural calamities. Originality/value There is a lot of research being done to predict the traffic and model it to find a solution, which will make the condition better. However, still, it is an open issue. The accuracy of the predictions done is less.

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“…Presently, there are various geographical routing protocols [15, 16] available considering the traffic condition as one of the metrics, such protocols are known as traffic‐aware routing (TAR) protocols. This routing is a developing field in VANET that takes decisions depending on the number of vehicles available [17, 18]. Based on the inspiration obtained from the existing VANET routing protocols, we propose a TAR protocol in VANET by the multi‐objective auto‐regressive whale optimisation (ARWO), which considers the multiple objectives, such as end‐to‐end delay (EED), link life time (LLT), packet delay, and distance, in the fitness function.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective auto‐regressive whale optimisation for traffic‐aware routing in urban VANET

Rewadkar¹,

Doye²

2018

IET Information Security

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Tomographic Reconstruction of RF Propagation to Aid VANET Routing in Urban Canyons

Cited by 8 publications

References 47 publications

2M2C-R2ED: Multi-Metric Cooperative Clustering Based Routing for Energy Efficient Data Dissemination in Green-VANETs

2M2C-R2ED: Multi-Metric Cooperative Clustering Based Routing for Energy Efficient Data Dissemination in Green-VANETs

Data dissemination for green-VANETs communication: an opportunistic optimization approach

Multi‐objective auto‐regressive whale optimisation for traffic‐aware routing in urban VANET

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