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

Koshimizu, Taka‐aki; Gengtian, Shi; Wang, Huan; Pan, Zhenni; Liu, Jiang; Shimamoto, Shigeru

doi:10.1109/access.2020.2994132

Cited by 18 publications

(7 citation statements)

References 27 publications

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“…In this study, SVM-based ML was used to determine the desired clustering size and range. The simulation results have proven that the system performs successfully with less access delay in the Public Land Mobile Network (PLMN) system [36]. https://doi.org/10.35193/bseufbd.1102897 VANETs.…”

Section: B Data Transmissionmentioning

confidence: 86%

A Review on Machine Learning Techniques Used in VANET and FANET Networks

MUTİ

Ülkü

2022

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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The widespread use of the Internet and the increase in the number and variety of devices connected to the internet have led to the emergence of new methods in wireless communication. Dynamic and temporary Ad-Hoc networks, which do not require a fixed infrastructure as in traditional wireless network communication, are one of these new methods. The fact that Ad-Hoc networks do not need a fixed infrastructure has revealed a network structure with a lower cost and less configuration. Mobile Ad-Hoc networks play an important role, especially in the communication of nodes on the move. FANET (Flying Ad-Hoc Networks) networks, which are called flying ad hoc networks, are mobile Ad-Hoc networks used for communication of unmanned aerial vehicles (UAV), and VANET (Vehicular Ad-Hoc Networks) networks, which are called vehicular ad hoc networks, are mobile Ad-Hoc networks used for communication of road vehicles. The development and dissemination of these networks make a significant contribution to the development of autonomous vehicles and UAVs. The increase in the use of FANET and VANET networks, which are specialized subnets of mobile Ad-Hoc networks, and the increase in the number of nodes in these networks have caused problems related to security, efficiency, and sustainability in these networks. Machine learning methods, one of today' s effective and common approaches, are one of the ways that are frequently used in solving the problems specified in FANET and VANET networks. The rapid topology change, which is one of the most important features of these networks, makes it difficult to provide traffic management, trust management, routing, and data transmission. In this direction, machine learning approaches play an active role. In this study, it is presented by examining which machine learning techniques are used in the literature to perform important tasks such as traffic management, trust management, routing, and data transfer. Thus, it is aimed for those who will work in these fields to acquire information about machine learning approaches that can be used. Since the FANET network type is a new approach, it has been observed that there are few studies using machine learning. In VANET systems, studies using machine learning methods are especially intense in 2021. This study was carried out to give the reader an idea about which machine learning methods can be used in which problems in FANET and VANET networks.

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Section: B Data Transmissionmentioning

confidence: 86%

A Review on Machine Learning Techniques Used in VANET and FANET Networks

MUTİ

Ülkü

2022

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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“…The work carried out by Bega et al [28] have developed an analytical model towards admission control with network slicing using a Markov model that can obtain the best acceptance policy in 5G network. Adoption of machine learning is also witnessed in 5G based vehicular network using clustering algorithm as noted in work of Koshimizu et al [29]. The technique has used Support Vector Machine for obtaining the accuracy in clustering process speci cally aiming towards autonomous driving system.…”

Section: Review Of Literaturementioning

confidence: 99%

Novel Baseline Computational Predictive Model for Seamless Transmission in 5G

Archana

Shahabadkar²

2023

Preprint

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5G technologies is known for its beneficial characteristic of supporting largely connected network and high-speed data transmission. With increasing number of services and application meant for hosting over 5G network, there is also an increasing concern towards accomplishing better quality of service and quality of experience in global telecommunication sector. In this line of communication advancement, it is also noted that machine learning is one of the contributories and enabling technology towards boosting the performance of value-added services and applications running on 5G networks. Existing review of literature exhibited multiple variants of methodologies meant for performing predictive performance towards leveraging quality of data delivery services in 5G. However, there are quite many research challenges too that is directly associated with deploying a cost-effective learning scheme in 5G. Therefore, the proposed scheme contributes towards developing a novel and yet simplified baseline architecture which targets to accomplishing seamless and reliable data dissemination services in 5G. The proposed model constructs a novel deployment scenario where a user handheld device is considered as a mobile node with an agenda to considered routing in allocated multiple paths to reach its destination. Further, RFC 8822 is used for deployment 5G standard along with a specific mobility model sync with a real-time server via access point and gateway node in large deployment scenario. Finally, an enhanced Long Short-Term Memory is implemented towards performing identification of predictive routes that are shared to users directly over 5G network. The study outcome is benchmarked with existing learning schemes to exhibit that proposed scheme offers approximately 35% of reduced losses, 19% of higher throughput, 23% of reduced delay, 37% of reduced memory, and 41% of reduced processing time.

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“…This concept had better performance on overhead. Takashi Koshimizu et al [20] presented a sophisticated clustering mechanism for cellular systems to provide effective communication between huge numbers of mobile Machine Type Communication (MTC) objects. The author proposed Normalized Multi Dimension-Affinity Propagation Clustering (NMDP-APC) approach and applied it to cluster-based VANETs.…”

Section: Related Workmentioning

confidence: 99%

Self-Adaptive Efficient Dynamic Multi-Hop Clustering (SA-EDMC) Approach for Improving VANET’s Performance

Abbas¹,

Mansour²,

Al-Fatlawi

2022

Int. J. Interact. Mob. Technol.

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Vehicular ad hoc networks (VANETs) are mainly used in the intelligent transportation field for smart applications. VANETS received maximum concentration among the researchers in academic research. Providing traffic safety at the time of high mobility in the network is the major issue. Also, it consists of few general issues namely self-organization, link failure and speedy variation in topology. In order to address this issue, we developed a novel approach namely, Self-Adaptive Efficient Dynamic Multi-Hop Clustering (SA-EDMC) approach which is designed to reduce the delay and energy consumption in the VANETs that leads to increase the efficiency and overall Quality of Service (QoS) of the network. This approach is composed into four parts (1) network construction, (2) prime cluster head selection, (3) dynamic prime CH selection and (4) gateway creation. Network simulator is utilized to demonstrate the behavior of our SA-EDMC approach. The simulation evaluation exhibits that our proposed work achieved high packet delivery ratio, low delay and high throughput when compared with earlier research outputs like MCA-V2I and QMM-VANET. The comparative analysis expressed that performance of the proposed SA-EDMC is ~5% increase in packet delivery ratio, produced 300 to 500 kbps extra throughput compared with the earlier methods and delay is comparatively low.

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

Cited by 18 publications

References 27 publications

A Review on Machine Learning Techniques Used in VANET and FANET Networks

A Review on Machine Learning Techniques Used in VANET and FANET Networks

Novel Baseline Computational Predictive Model for Seamless Transmission in 5G

Self-Adaptive Efficient Dynamic Multi-Hop Clustering (SA-EDMC) Approach for Improving VANET’s Performance

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