5G Network Slicing for V2X Communications: Technologies and Enablers

Campolo, Claudia; Molinaro, Antonella; Sciancalepore, Vincenzo

doi:10.1002/9781119652434.ch13

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…V2X enables many applications like cooperative autonomous and teleoperated driving, traffic efficiency, and infotainment services. However, vehicular network requirements are challenging, including the need for ultra‐low latencies, high data rate, not to mention the added complexity of both mobility and density of the vehicles 81‐83 . Network slicing might be the sole technique to demonstrate the capacity, throughput, and latency necessary to handle the massive amount of high‐priority traffic demands as V2X becomes more prevalent 55,84 .…”

Section: Concepts Definitions and Recent Advancesmentioning

confidence: 99%

“…However, vehicular network requirements are challenging, including the need for ultra-low latencies, high data rate, not to mention the added complexity of both mobility and density of the vehicles. [81][82][83] Network slicing might be the sole technique to demonstrate the capacity, throughput, and latency necessary to handle the massive amount of high-priority traffic demands as V2X becomes more prevalent. 55,84 However, it is worth mentioning that this slice is neglected mostly due to its novelty and similarity of its requirements with URLLC.…”

Section: 43mentioning

confidence: 99%

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5G network slicing with unmanned aerial vehicles: Taxonomy, survey, and future directions

Bouzid

Chaib

Bensaad

et al. 2022

Trans Emerging Tel Tech

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With the ever‐expanding rise of network demands and user expectations, the fifth generation (5G) of cellular networks was envisioned to support a plethora of use cases and conflicting user demands. Next to providing traditional connectivity like its previous generations, 5G also promises to be a heterogeneous network connecting humans, vehicles, unmanned aerial vehicles (UAVs), smart devices, and more. These challenging expectations proved to be overwhelming for traditional network infrastructures to handle. Network slicing has emerged as a promising solution that can achieve such diverse, taxing, and sometimes conflicting requirements in a dynamic and programmable way. There is no denying that UAVs have attained significant focus and research in recent years, and with 5G already being deployed, UAVs can now exploit the capabilities of the new networks. Extensive research is being taken to integrate UAVs into networks, assisting and improving aspects like latency, coverage, and capacity. Motivated by these facts, this survey distinguishes itself from other works by jointly exploring 5G, network slicing, and UAVs. The main contributions of this article are to showcase how UAVs can assist networks, provide a taxonomy of UAVs in the context of network slicing, and survey works that contribute to network slicing with UAVs. In this article, we present a comprehensive survey on UAVs in the context of network slicing, covering contributions, and state‐of‐the‐art literature. We discuss network slicing in‐depth, focusing especially on the three major slices: enhanced Mobile BroadBand, massive machine type communications, and ultra‐reliable low‐latency communications. We provide an overview of 5G enablers, including software‐defined networking and network function virtualization. We cover UAVs and identify their roles in networks as both users and assistants. Furthermore, this survey provides insight into open issues and future research directions related to network slicing and UAVs before concluding.

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Section: Concepts Definitions and Recent Advancesmentioning

confidence: 99%

Section: 43mentioning

confidence: 99%

5G network slicing with unmanned aerial vehicles: Taxonomy, survey, and future directions

Bouzid

Chaib

Bensaad

et al. 2022

Trans Emerging Tel Tech

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A Survey on Machine Learning-Based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks

Boualouache

Engel

2023

IEEE Commun. Surv. Tutorials

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Federated Learning-based Inter-slice Attack Detection for 5G-V2X Sliced Networks

Boualouache

Engel

2022

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

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As a leading enabler of 5G, Network Slicing (NS) aims at creating multiple virtual networks on the same shared and programmable physical infrastructure. Integrated with 5G-Vehicle-to-Everything (V2X) technology, NS enables various isolated 5G-V2X networks with different requirements such as autonomous driving and platooning. This combination has generated new attack surfaces against Connected and Automated Vehicles (CAVs), leading them to road hazards and putting users' lives in danger. More specifically, such attacks can either intraslice targeting the internal service within each V2X Network Slice (V2X-NS) or inter-slice targeting the cross V2X-NSs and breaking the isolation between them. However, detecting such attacks is challenging, especially inter-slice V2X attacks where security mechanisms should maintain privacy preservation and NS isolation. To this end, this paper addresses detecting interslice V2X attacks. To do so, we leverage both Virtual Security as a Service (VSaS) concept and Deep learning (DL) together with Federated learning (FL) to deploy a set of DL-empowered security Virtual Network Functions (sVNFs) over V2X-NSs. Our privacy preservation scheme is hierarchical and supports FLbased collaborative learning. It also integrates a game-theorybased mechanism to motivate FL clients (CAVs) to provide highquality DL local models. We train, validate, and test our scheme using a publicly available dataset. The results show our scheme's accuracy and efficiency in detecting inter-slice V2X attacks.

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5G Network Slicing for V2X Communications: Technologies and Enablers

Cited by 4 publications

References 14 publications

5G network slicing with unmanned aerial vehicles: Taxonomy, survey, and future directions

5G network slicing with unmanned aerial vehicles: Taxonomy, survey, and future directions

A Survey on Machine Learning-Based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks

Federated Learning-based Inter-slice Attack Detection for 5G-V2X Sliced Networks

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