Mobility Prediction in Vehicular Networks: An Approach Through Hybrid Neural Networks Under Uncertainty

Banerjee, Soumya; Bouzefrane, Samia; Mühlethaler, Paul

doi:10.1007/978-3-319-67807-8_14

Cited by 4 publications

(1 citation statement)

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“…Addressing the above problems, we propose mobility-aware proactive caching in ICN for IoV. The real-time status of vehicles and their interaction with other vehicles and RSUs is modeled using a Markov process [ 8 , 9 ]. We propose an in-network edge caching that maximizes network performance while minimizing transmission delay.…”

Section: Introductionmentioning

confidence: 99%

Mobility-Aware Proactive Edge Caching Optimization Scheme in Information-Centric IoV Networks

Musa

Zennaro

Libsie

et al. 2022

Sensors

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Edge caching is a promising approach to alleviate the burden on the backhaul of network links. It has a significant role in the Internet of Vehicle (IoV) networks performance by providing cached data at the edge and reduce the burden of the core network caused by the number of participating vehicles and data volume. However, due to the limited computing and storage capabilities of edge devices, it is hard to guarantee that all contents are cached and every requirement of the device are satisfied for all users. In this paper, we design an Information-Centric Network (ICN) with mobility-aware proactive caching scheme to provide delay-sensitive services on IoV networks. The real-time status and interaction of vehicles with other vehicles and Roadside Units (RSU) is modeled using a Markov process. Mobility aware proactive edge caching decision that maximize network performance while minimizing transmission delay is applied. Our numerical simulation results show that the proposed scheme outperforms related caching schemes in terms of latency by 20–25% in terms of latency and by 15–23% in cache hits.

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Section: Introductionmentioning

confidence: 99%