Investigation of a GNN approach to mitigate congestion in a realistic MANET scenario

Maret, Yann; Raza, Mohsin; Legendre, Franck; Wang, Junyuan; Bessis, Nik

doi:10.1016/j.procs.2022.09.014

Cited by 4 publications

(2 citation statements)

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“…3 . The GNN approaches presented in [43], showed the difficulty to convert solutions designed for slowly varying fixed Software Defined Networks (SDN), i.e., a multi-commodity flow problems to a useful schemes for V2V communications. Fig.…”

Section: Machine Learning Based Attemptsmentioning

confidence: 99%

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Towards AI-Native Vehicular Communications

Rizzo,

Liotou,

Maret

et al. 2023

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)

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The role of fast yet reliable wireless communications in various application domains is getting ever more important. At the same time, as use cases are becoming more and more complex, application requirements are getting ever more stringent. One example is intelligent transportation, where the efficiency and reliability of wireless data delivery is essential for effective service support. As a consequence, in this context the adoption of AI techniques is widely considered crucial for enabling vehicular communications to adapt to dynamic changes of the environment. In this position paper, we discuss some representative applications of advanced AI tools in vehicular communications. In particular, we elaborate on the potential of distributed learning based on federated learning, of proactive service provisioning, and of graph neural network for enabling AI-native vehicular communications.

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Section: Machine Learning Based Attemptsmentioning

confidence: 99%

“…Alternative routes in presence of an all to all traffic were investigated using the GNN-t algorithm [43]. GNN-t was trained on the Anglova scenario using the number of traversals derived from the shortest paths (using the Dijkstra algorithm).…”

Section: E a Gnn Attempt To Improve Odrmentioning

confidence: 99%