Gateway selection in Vehicular Ad-hoc Network

Idrissi, Abdellah; Retal, Sara; Rehioui, Hajar; Laghrissi, Abdelquoddouss

doi:10.1109/icta.2015.7426937

Cited by 4 publications

(2 citation statements)

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“…However, the proactive and reactive strategies used can decrease the throughput when the vehicle's number increases. Idrissi et al [18] used the vehicular cloud architecture to develop the gateway discovery system. They adapted a multi-criteria decision approach known as Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) to find the best gateway.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning based Gateway Selection in VANETs

Alabbas

Huszák

2022

Int. j. electr. comput. eng. syst. (Online)

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In vehicular ad hoc networks (VANETs), providing the Internet has become an urgent necessity, where mobile gateways are used to ensure network connection to all customer vehicles in the network. However, the highly dynamic topology and bandwidth limitations of the network represent a significant issue in the gateway selection process. Two objectives are defined to overcome these challenges. The first objective aims to maximize the number of vehicles connected to the Internet by finding a suitable gateway for them depending on the connection lifetime. The second objective seeks to minimize the number of connected vehicles to the same gateway to overcome the limitation of gateways' bandwidth and distribute the load in the network. For this purpose, A gateway discovery system assisted by the vehicular cloud is implemented to find a fair trade-off between the two conflicting objectives. Proximal Policy Optimization, a well-known reinforcement learning strategy, is used to define and train the agent. The trained agent was evaluated and compared with other multi-objective optimization methods under different conditions. The obtained results show that the proposed algorithm has better performance in terms of the number of connected vehicles, load distribution over the mobile gateways, link connectivity duration, and execution time.

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Section: Related Workmentioning

confidence: 99%

Reinforcement Learning based Gateway Selection in VANETs

Alabbas

Huszák

2022

Int. j. electr. comput. eng. syst. (Online)

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“…Figure 9 shows 22% used 15 nodes, 21% used 50 nodes, 14% used 25 and 30 nodes,11% used 100 nodes,6% used 60 nodes, 3% used 12,40,45 and 200 nodes as calculate base on Table 11. The lowest number of nodes was 3 [56] used a topology size of 1000x800m 2 , and the highest one was 200 [67,75] used in VANET. Table 10 shows authors who used a fixed number of the node with fixed topology.…”

Section: Number Of Nodesmentioning

confidence: 99%

A Review on Parameters of Internet Gateway Discovery in MANETS

2021

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<p class="0abstract"><span lang="EN-US">Mobile ad hoc Networks researchers need to apply specific parameters in the simulation tools chosen to analyze and discuss their results. The challenge faced by multiple authors is on how to decide on the choice of particular parameters. A wrong choice could lead to a no credible result and a doubtful result by other researchers. A best practice is to follow what other authors are using as input parameters in their paper. This review analyses 72 proceeding papers and articles in different digital libraries: google scholar, IEEE Xplore, Elsevier, Springer, and snowball from 2010-2020. We present the result of our survey in this paper. We recommend the input parameters research should use base on the high utilization. Our review will also help the community in MANET and Internet gateway to improve the credibility of the input parameters.</span></p>

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