A multi-objective optimization system for mobile gateways selection in vehicular Ad-Hoc networks

Retal, Sara; Idrissi, Abdellah

doi:10.1016/j.compeleceng.2018.12.004

Cited by 18 publications

(2 citation statements)

References 6 publications

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“…This section exhibits the performance evaluation of the proposed algorithm. For comparison purposes, the well-known gateway selection method (A multiobjective optimization system for mobile gateways selection in vehicular Ad-Hoc networks) [19] is simulated, namely the MOO algorithm. The MOO technique has similar properties as this work.…”

Section: Resultsmentioning

confidence: 99%

“…However, the gateway selection mechanism in this study lacks the use of optimization techniques. Sara Retal and Abdellah Idrissi proposed a method to improve the mobile gateways selection [19]. Multi-Objective Optimization is considered to overcome the weakness of the previous study by maximizing the number of connected vehicles and minimizing the overload of the gateways.…”

Section: Related Workmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%