SDN-Enabled Social-Aware Clustering in 5G-VANET Systems

Qi, Weijing; Song, Qingyang; Wang, Xiaojie; Ning, Zhaolong

doi:10.1109/access.2018.2837870

Cited by 73 publications

(51 citation statements)

References 32 publications

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“…Reference [35] investigates the security and privacy issue using block chain in the transportation system and the vehicular IoT environment in SDN-enabled 5G-VANETs. To make use of the centralized function advantage of the SDN, a social-aware cluster algorithm is proposed in Reference [36] to model the vehicle movement in predicting the future routes. Although, SDN-based handover is mentioned in some studies, but they do not specify the handover process in detail.…”

Section: Sdn-based Vanetsmentioning

confidence: 99%

SDN-based Handover Scheme in Cellular/IEEE 802.11p Hybrid Vehicular Networks

Duo

Yoshinaga

et al. 2020

Sensors

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With the arrival of 5G, the wireless network will be provided with abundant spectrum resources, massive data transmissions and low latency communications, which makes Vehicle-to-Everything applications possible. However, VANETs always accompany with frequent network topology changes due to the highly mobile feature of vehicles. As a result, the network performance will be affected by the frequent handover. In this paper, a seamless handover scheme is proposed where the Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) technologies are employed to adapt to the dynamic topology change in VANETs. The introduction of SDN provides a global view of network topology and centralized control, which enables a stable transmission layer connection when a handover takes place, so that the upper layer performance is not influenced by the network changes. By employing MEC server, the data are cached in advance before a handover happens, so that the vehicle can restore normal communication faster. In order to confirm the superiority of our proposal, computer simulations are conducted from different aspects. The results show that our proposal can significantly improve the network performance when a handover happens.

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Section: Sdn-based Vanetsmentioning

confidence: 99%

SDN-based Handover Scheme in Cellular/IEEE 802.11p Hybrid Vehicular Networks

Duo

Yoshinaga

et al. 2020

Sensors

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“…In centralized clustering schemes, cluster formation is performed via a NB or Rode Side Unit (RSU) with periodical message exchanges to the target vehicles. Qi et al [22] proposed an SDN-based centralized clustering scheme by exploiting a social pattern, i.e., knowing the vehicles' traffic pattern to deduce an expected route, where the metrics of inter-vehicular distance, relative speed and vehicle attributes are used. In distributed clustering schemes, cluster formation and CH selection are performed only between the vehicles through message exchanges without involving managements of central node in a fully distributed manner.…”

Section: Related Work a Vanet Clustering Techniquesmentioning

confidence: 99%

Multi-Dimensional Affinity Propagation Clustering Applying a Machine Learning in 5G-Cellular V2X

et al. 2020

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Cellular systems are facing the ever-increasing demand for vehicular communication aimed at applications such as advanced driving assistance and ultimately fully autonomous driving. Cellular Vehicle to Anything (C-V2X) has become more applicable with the release of the first sets of 5G (5 th Generation) system specifications. The highly capable 5G systems will therefore support even a larger number of moving objects. This study aims to present a sophisticated clustering mechanism that enables cellular systems to accommodate a massive number of moving Machine Type Communication (MTC) objects with a minimum set of connections while maintaining system scalability. Specifically, we proposed Normalized Multi Dimension-Affinity Propagation Clustering (NMDP-APC) scheme and applied it for Vehicular Ad hoc Network (VANET) clustering. For VANET clustering formation, our study employed Machine Learning (ML) to determine the granularity, i.e., the size and span of clusters desirable for use in dynamic motion environments. The study achieved a sufficient level of prediction accuracy with fewer training data through a learned prediction function based on the selected key criteria. This paper also proposes a system sequence designed with a series of procedures fully compliant with C-V2X systems. We demonstrated substantial simulations and numerical experiments with theoretical analysis, specifically applying soft-margin-based Support Vector Machine (SVM) algorithm. The simulation results confirmed that the granularity parameter we applied fairly controls the size of VANET clusters although vehicles are in motion and that the prediction performance has been adjusted through controlling of key SVM parameters. INDEX TERMS 5G mobile communication, vehicular ad hoc networks, machine-to-machine communications, machine learning, mobile computing, clustering methods, heterogeneous networks.

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“…The nodes in VANET can be connected or disconnected to the network very quickly, causing a highly dynamic topology and a frequently disconnected network. They are also constrained by the route of the roads and traffic regulation [15]. These parameters have a massive influence on the communication stability.…”

Section: The Communication Entities Road Side Unit (Rsu) Andmentioning

confidence: 99%

A New Clustering Algorithm for Live Road Surveillance on Highways based on DBSCAN and Fuzzy Logic

Alabbas¹,

Huszák²

2020

IJACSA

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Video streaming over Vehicular Ad Hoc Networks is a promising technique (VANETs), and it has gained great importance in the last few years. The highly dynamic topology of VANETs makes high-quality video streaming very challenging. In order to provide the most useful camera views to the vehicles, clustering and cluster head selection techniques are used. Too frequent camera view changes can be annoying; therefore, we propose a new stable clustering algorithm to ensure a stable live road surveillance service without interruptions for vehicles that do not have enough vision area. In the proposed solution, we integrated Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with Fuzzy Logic Control (FLC). DBSCAN is used to form the clusters, while FLC is used to find the best cluster head for the cluster. Different parameters are utilized like density parameters for DBSCAN, and relative speed, acceleration, leadership degree and vision area for fuzzy logic. Our proposed algorithm showed better results in terms of cluster lifetime and vehicle status change. Our proposed algorithm has been compared with another clustering scheme to prove the effectiveness of our proposed algorithm.

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SDN-Enabled Social-Aware Clustering in 5G-VANET Systems

Cited by 73 publications

References 32 publications

SDN-based Handover Scheme in Cellular/IEEE 802.11p Hybrid Vehicular Networks

SDN-based Handover Scheme in Cellular/IEEE 802.11p Hybrid Vehicular Networks

Multi-Dimensional Affinity Propagation Clustering Applying a Machine Learning in 5G-Cellular V2X

A New Clustering Algorithm for Live Road Surveillance on Highways based on DBSCAN and Fuzzy Logic

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