Relay Selection Based on Deep Learning for Broadcasting in VANET

Mchergui, Abir; Moulahi, Tarek; Nasri, Salem

doi:10.1109/iwcmc.2019.8766487

Cited by 19 publications

(16 citation statements)

References 15 publications

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“…In [10], the authors propose to use deep learning to select the best relays to enhance dissemination of data. The studied case is made of a road network with static Road Side Units (RSU) near the roads and vehicles moving around on the roads.…”

Section: B Machine Learning Based Algorithmsmentioning

confidence: 99%

Dynamic selection of relays based on classification of mobility profile in a highly mobile context

Fabian

Rachedi

2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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With an increasing amount of connected cars and devices having more and more sensors, the development of smart architectures and algorithms to efficiently transport data is a major concern. The selection of relays to allow users to connect to Internet is an important aspect in networks with high mobility, particularly in low-population areas having poor network coverage. Furthermore, cellular connectivity can be expensive for users. The solution proposed in this paper uses a machine learning based classification algorithm to select the best relays amongst any user based on their mobility profile. Not only this solution can be used on its own to enhance network performance without requiring a dedicated architecture, but it can be coupled with other algorithms as well to increase performance even more. Simulation results will show the proposition is able to scale up to several hundreds of users simultaneously, it improves the delivery rate of packets by up to a factor 2, it increases connectivity, generates less signaling and yields a more stable topology compared to a random selection or the use of static relays.

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Section: B Machine Learning Based Algorithmsmentioning

confidence: 99%

Dynamic selection of relays based on classification of mobility profile in a highly mobile context

Fabian

Rachedi

2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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“…Mchergui et al 15 propose the use of deep learning to select relays in order to increase performance. The solution is studied on a road network in the context of vehicular ad hoc network (VANET).…”

Section: Related Workmentioning

confidence: 99%

Selection of relays based on the classification of mobility‐type and localized network metrics in the Internet of Vehicles

Fabian

Rachedi

Gueguen

2021

Trans Emerging Tel Tech

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The ever increasing amount of connected mobile devices and users in the Internet of vehicles and the fact bicycles, electric scooters and users' smartphones are also connected poses a real challenge in terms of ensuring quality of service. The constantly changing topology due to the high‐mobility of devices and users greatly impacts its stability and the connectivity of devices. Furthermore, to ensure safety of people in the case of autonomous vehicles it is of paramount importance to ensure excellent reliability. This is why we have developed a solution that is based on machine learning to classify devices according to their mobility profile and that uses a scoring system to select the best candidates to act as mobile relays amongst devices with a suitable mobility profile. The scoring system allows to find critical locations in terms of user density. This solution does not require a dedicated infrastructure such as road side units. Simulations results will show the proposed solution increases the packet delivery ratio by up to 6%, reduces the energy consumption by up to 30% and increases the efficiency of bandwidth usage without sacrificing the end delay of users and devices compared with the state‐of‐the‐art.

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“…Existing literature shows improved packet delivery ratio by machine learning algorithms in multi-hop V2V communications [6]. However, artificial intelligence methods require huge processing power and are often not suitable for a fully distributed architecture [7].…”

Section: Introductionmentioning

confidence: 99%

A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks

Ayaz

Sheng

Tian

et al. 2022

IEEE Trans. Veh. Technol.

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Relay Selection Based on Deep Learning for Broadcasting in VANET

Cited by 19 publications

References 15 publications

Dynamic selection of relays based on classification of mobility profile in a highly mobile context

Dynamic selection of relays based on classification of mobility profile in a highly mobile context

Selection of relays based on the classification of mobility‐type and localized network metrics in the Internet of Vehicles

A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks

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