Preliminary Performance Benchmarking of OLSRd2 Using Emulated TDMA MANETs and an ODR

Maret, Yann; Wagen, J.-F.

doi:10.1109/vtc2020-fall49728.2020.9348780

Cited by 4 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Repeated emulations confirm that OLSRv2 (Completion Ratio CR=76%) is outperformed by our centralized Omniscient Dijkstra Routing (ODR, CR=94%) [44]. ODR computes all possible shortest paths and then choose a set of single paths to distribute evenly the number of node traversals.…”

Section: An Omniscient Dijkstra Routing For Benchmarkingmentioning

confidence: 91%

Towards AI-Native Vehicular Communications

Rizzo,

Liotou,

Maret

et al. 2023

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: An Omniscient Dijkstra Routing For Benchmarkingmentioning

confidence: 91%