Artificial Intelligence based QoS optimization for multimedia communication in IoV systems

Sodhro, Ali Hassan; Luo, Zongwei; Sodhro, Gul Hassan; Muzamal, Muhammad; Rodrigues, Joel J. P. C.; Albuquerque, Victor Hugo C. de

doi:10.1016/j.future.2018.12.008

Cited by 80 publications

(34 citation statements)

References 15 publications

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“…Waiker (2020) used deep learning methods to review and critically appraise published and preprint reports of prediction models for COVID-19 patients. In particular, several study works ( Afshar et al, 2020 ; Asnaoui et al, 2020 ; Corman et al, 2020 ; Fomsgaard and Rosenstierne, 2020 ; Forbes, 2020 ; Ghoshal and Tucker, 2020 ; Gozes et al, 2020 ; Hall et al, 2020 ; Healthitanalytics, 2020 ; Hu et al, 2020a ; Hu et al, 2020b ; IBM, 2020 ; Loey et al, 2020 ; Maghdid et al, 2020 ; Narin et al, 2020 ; Pal et al, 2020 ; Pham et al, 2020 ; Qi et al, 2020 ; Rao and Vazquez, 2020 ; Satu et al, 2020 ; Sodhro et al, 2019 ; Yan et al, 2020 ; Zhang et al, 2020 ; Zheng et al, 2020 ) have used machine learning techniques, including big data techniques, to process COVID-19 data to determine the spread of disease, predict the risk of disease, and to assess the diagnosis of disease, number of incidences, and healthcare facilities.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches Reveal That the Number of Tests Do Not Matter to the Prediction of Global Confirmed COVID-19 Cases

Khan

Hossain

2020

Front. Artif. Intell.

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Coronavirus disease 2019 (COVID-19) has developed into a global pandemic, affecting every nation and territory in the world. Machine learning-based approaches are useful when trying to understand the complexity behind the spread of the disease and how to contain its spread effectively. The unsupervised learning method could be useful to evaluate the shortcomings of health facilities in areas of increased infection as well as what strategies are necessary to prevent disease spread within or outside of the country. To contribute toward the well-being of society, this paper focusses on the implementation of machine learning techniques for identifying common prevailing public health care facilities and concerns related to COVID-19 as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation. Regression tree, random forest, cluster analysis and principal component machine learning techniques are used to analyze the global COVID-19 data of 133 countries obtained from the Worldometer website as of April 17, 2020. The analysis revealed that there are four major clusters among the countries. Eight countries having the highest cumulative infected cases and deaths, forming the first cluster. Seven countries, United States, Spain, Italy, France, Germany, United Kingdom, and Iran, play a vital role in explaining the 60% variation of the total variations by us of the first component characterized by all variables except for the rate variables. The remaining countries explain only 20% of the variation of the total variation by use of the second component characterized by only rate variables. Most strikingly, the analysis found that the variable number of tests by the country did not play a vital role in the prediction of the cumulative number of confirmed cases.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches Reveal That the Number of Tests Do Not Matter to the Prediction of Global Confirmed COVID-19 Cases

Khan

Hossain

2020

Front. Artif. Intell.

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“…Due to the current trend to connect vehicular networks to information centers and the need to exchange data, IoV allows enabling Internet access among on-road vehicles. One of the essential IoV applications is to improve the features of VANETs to reduce various issues in urban traffic and accident environments [6]. IoV enables the vehicular road networks to interconnect with different wireless network technologies i.e., Wi-Fi and 4G/LTE for V2I, IEEE WAVE for V2V and V2R, MOST/Wi-Fi for V2S, and CarPlay NCF for V2P.…”

Section: Background and Motivationmentioning

confidence: 99%

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Ali

Hasan

Hassan

et al. 2021

Security and Communication Networks

120

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Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.

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“…The Internet of Vehicles (IoV) is a paradigm that has attracted growing interest from the scientific community, government agencies and the automotive industries. This paradigm has been proposed to provide collaboration between vehicles and reliable Internet services, and to improve the experience of drivers and passengers [2], [69], [70]. To enable these communications, different technologies have been proposed, as seen in Figure 4 The WAVE architecture is a family of protocols standardized by the IEEE for communications in vehicular networks.…”

Section: ) Technologymentioning

confidence: 99%

Computation Offloading for Vehicular Environments: A Survey

et al. 2020

Self Cite

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With significant advances in communication and computing, modern day vehicles are becoming increasingly intelligent. This gives them the ability to contribute to safer roads and passenger comfort through network devices, cameras, sensors, and computational storage and processing capabilities. However, to run new and popular applications, and to enable vehicles operating autonomously requires massive computational resources. Computational resources available with the current day vehicles are not sufficient to process all these demands. In this situation, other vehicles, edge servers, and servers in remote data centers can help the vehicles by lending their computing resources. However, to take advantage of these computing resources, computation offloading techniques have to be leveraged to transfer tasks or entire applications to run on other devices. Such computation offloading can lead to improved performance and Quality of Service (QoS) for applications and for the network. However, computation offloading in a highly dynamic environment such as vehicular networks is a major challenge. Therefore, this survey aims to review and organize the computation offloading literature in vehicular environments. In addition, we demystify some concepts, propose a taxonomy with the most important aspects and classify most works in the area according to each category. We also present the main tools, scenarios, subjects, strategies, objectives, etc., used in the works. Finally, we present the main challenges and future directions to guide future research in this active research area.

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Artificial Intelligence based QoS optimization for multimedia communication in IoV systems

Cited by 80 publications

References 15 publications

Machine Learning Approaches Reveal That the Number of Tests Do Not Matter to the Prediction of Global Confirmed COVID-19 Cases

Machine Learning Approaches Reveal That the Number of Tests Do Not Matter to the Prediction of Global Confirmed COVID-19 Cases

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Computation Offloading for Vehicular Environments: A Survey

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