Convergence of Software-Defined Vehicular Cloud and 5G Enabling Technologies: A Survey

Nkenyereye, Lionel; Nkenyereye, Lewis; Jang, Jong-Wook

doi:10.3390/electronics12092066

Cited by 4 publications

(4 citation statements)

References 74 publications

(114 reference statements)

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“…The recent literature on CVs focused on advancing security, traffic management, and cooperative control systems. Nkenyereye et al (2023) examined the integration of 5G networks in vehicular cloud computing, highlighting the potential of vehicle clusters to share resources and data in a mobile cloud environment [5].…”

Section: Connected Vehicles Trendsmentioning

confidence: 99%

Alignment of Unsupervised Machine Learning with Human Understanding: A Case Study of Connected Vehicle Patents

Bridgelall

2024

Applied Sciences

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As official public records of inventions, patents provide an understanding of technological trends across the competitive landscape of various industries. However, traditional manual analysis methods have become increasingly inadequate due to the rapid expansion of patent information and its unstructured nature. This paper contributes an original approach to enhance the understanding of patent data, with connected vehicle (CV) patents serving as the case study. Using free, open-source natural language processing (NLP) libraries, the author introduces a novel metric to quantify the alignment of classifications by a subject matter expert (SME) and using machine learning (ML) methods. The metric is a composite index that includes a purity factor, evaluating the average ML conformity across SME classifications, and a dispersion factor, assessing the distribution of ML assigned topics across these classifications. This dual-factor approach, labeled the H-index, quantifies the alignment of ML models with SME understanding in the range of zero to unity. The workflow utilizes an exhaustive combination of state-of-the-art tokenizers, normalizers, vectorizers, and topic modelers to identify the best NLP pipeline for ML model optimization. The study offers manifold visualizations to provide an intuitive understanding of the areas where ML models align or diverge from SME classifications. The H-indices reveal that although ML models demonstrate considerable promise in patent analysis, the need for further advancements remain, especially in the domain of patent analysis.

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Section: Connected Vehicles Trendsmentioning

confidence: 99%

Alignment of Unsupervised Machine Learning with Human Understanding: A Case Study of Connected Vehicle Patents

Bridgelall

2024

Applied Sciences

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“…Arthurs et al (2021) examined edge cloud computing that enables vehicles to connect existing sensors such as road cameras, traffic density sensors, traffic speed sensors, emergency vehicle transmissions, and public transport transponders [7]. Nkenyereye et al (2023) explored the fusion of fifth-generation (5G) cellular networks to enable software-defined vehicular cloud computing, an emerging technology where clusters of vehicles can share resources, sensor data, and complete tasks in a mobile ad hoc cloud environment [8].…”

Section: Networkingmentioning

confidence: 99%

A Systematic Patent Review of Connected Vehicle Technology Trends

Bridgelall

2024

Future Transportation

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Governments and manufacturers anticipate that connected vehicle deployments will reduce accidents, optimize traffic flow, and enhance the driving experience. Although extensive studies focus on the technical aspects of connected vehicles, a gap exists in systematically analyzing the invention trends shaping the field. Insights into these trends is a strategic imperative for policymakers, researchers, and investors alike. This study presents a systematic patent review (SPR) as a robust and adaptable methodological framework for patent analysis. Adapted from the established systematic literature review (SLR), the SPR offers detailed insights into both the thematic and temporal trajectories of innovation in any technology field. The SPR identifies 220 U.S. patents from 2018 to 2022 and classifies them into specific objective categories such as computing resources, cybersecurity, and driving safety, among others. The study notes an increasing focus on driving safety and secure wireless communications, which aligns with broader goals of enhancing safety and situational awareness in transportation. Both the methodological framework and findings address an existing gap in the literature, guide future research, policymaking, and investment in the field of connected vehicles.

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“…According to the 5G standard [ 4 ] and the future 6G vision, novel networks must provide a higher data rate, lower latency, better reliability, wider coverage, low power consumption, and low-cost communication services. Non-orthogonal multiple access (NOMA), massive multiple-input multiple-output (MIMO), mmWave, mobile edge computing (MEC), and artificial intelligence (AI) are the promising technologies that meet these requirements [ 5 ]. Specifically, orthogonal time-frequency-space (OTFS) is a recently proposed modulation technique for high-mobility communication.…”

Section: Introductionmentioning

confidence: 99%

Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems

Ding,

Deng,

Xie

et al. 2024

Sensors

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For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose a time-varying multipath channel estimation method based on distributed compressed sensing and a multi-symbol complex exponential basis expansion model (MS-CE-BEM) by exploiting the temporal correlation and the joint delay sparsity of wideband wireless channels within the duration of multiple OFDM symbols. Furthermore, in the proposed method, a sparse pilot pattern with the self-cancellation of pilot intercarrier interference (ICI) is adopted to reduce the input parameter error of the MS-CE-BEM, and a symmetrical extension technique is introduced to reduce the modeling error. Simulation results show that, compared with existing methods, this proposed method has superior performances in channel estimation and spectrum utilization for sparse time-varying channels.

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Convergence of Software-Defined Vehicular Cloud and 5G Enabling Technologies: A Survey

Cited by 4 publications

References 74 publications

Alignment of Unsupervised Machine Learning with Human Understanding: A Case Study of Connected Vehicle Patents

Alignment of Unsupervised Machine Learning with Human Understanding: A Case Study of Connected Vehicle Patents

A Systematic Patent Review of Connected Vehicle Technology Trends

Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems

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