6G-Enabled Internet of Things: Vision, Techniques, and Open Issues

Hosseinzadeh, Mehdi; Hemmati, Atefeh; Rahmani, Amir Masoud

doi:10.32604/cmes.2022.021094

Cited by 6 publications

(5 citation statements)

References 162 publications

(190 reference statements)

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“…Blockchain technology may be used to address significant security flaws relating to data privacy breaches, cyberattacks, and access control for highly sophisticated information theft. 19,20 The issues raised motivated the writing of this article; therefore, we organized a systematic literature review (SLR) article to investigate the latest review and survey articles in terms of their aspects, contributions, findings, limitations, and strong points to address unsolved problems of Blockchain-based IoV (BIoV) networks.…”

Section: Introductionmentioning

confidence: 99%

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Blockchain‐based internet of vehicles (BIoV): A systematic review of surveys and reviews

Hemmati

Zarei

Souri

2023

Security and Privacy

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The Internet of vehicles (IoV) has appeared as an effective method of obtaining an intelligent transportation system able to deliver various innovative solutions and enable several applications as a replacement for vehicular ad‐hoc networks (VANETs). To help IoV contexts, enormous quantities of information are created and transmitted between various communication components wirelessly across multiple channels, which may entice attackers and put the network at risk. Security is one of the vital concerns and critical issues in VANETs and IoV networks. Blockchain is employed to create a distributed and secure IoV to overcome some centralized concerns and enhance the network architecture. This article presents a systematic and detailed review by selecting 28 review articles from 2018 to 2022 on blockchain‐based IoV (BIoV). We investigate the latest review and survey articles regarding their aspects, contributions, findings, limitations, and strong points to address unsolved problems of BIoV. In this article, the review/survey articles are systematically reviewed to resolve taxonomy aspects intended for BIoV. We provide a new taxonomy and discuss security concerns, open issues, and future directions so that researchers in this field can quickly and easily access recent content and do not need to read numerous review articles.

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Section: Introductionmentioning

confidence: 99%

“…Blockchain technology is therefore used to assure honesty and integrity among network users. Blockchain technology may be used to address significant security flaws relating to data privacy breaches, cyberattacks, and access control for highly sophisticated information theft 19,20 …”

Section: Introductionmentioning

confidence: 99%

Blockchain‐based internet of vehicles (BIoV): A systematic review of surveys and reviews

Hemmati

Zarei

Souri

2023

Security and Privacy

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“…Alternatively, social media may be considered a collection of web‐based tools that promote connectedness and social interactions. The development and sharing of user‐generated content have been encouraged by the widespread usage of modern software and cutting‐edge technology to access social media platforms online 1,6–8 …”

Section: Introductionmentioning

confidence: 99%

“…The development and sharing of user-generated content have been encouraged by the widespread usage of modern software and cutting-edge technology to access social media platforms online. 1,[6][7][8] Social media frameworks are currently considered to be one of the major global data producers. Big data is among the most important data generated by modern media.…”

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confidence: 99%

A taxonomy and survey of big data in social media

Hemmati,

Arzanagh,

Rahmani

2023

Concurrency and Computation

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SummaryExamining the particular value of each platform for big data would be difficult because of the variety of social media forms and sizes. Using social media to objectively and subjectively analyze large groups of individuals makes it the most effective tool for this task. There are numerous sources of big data within the organization. Social media can be identified by the interaction and communication it facilitates. Utilizing social media has become a daily occurrence in modern society. In addition, this frequent use generates data demonstrating the importance of researching the relationship between big data and social media. It is because so many internet users are also active on social media. We conducted a systematic literature review (SLR) to identify 42 articles published between 2018 and 2022 that examined the significance of big data in social media and upcoming issues in this field. We also discuss the potential benefits of utilizing big data in social media. Our analysis discovered open problems and future challenges, such as high‐quality data, information accessibility, speed, natural language processing (NLP), and enhancing prediction approaches. As proven by our investigations of evaluation metrics for big data in social media, the distribution reveals that 24% is related to data‐trace, 12% is related to execution time, 21% to accuracy, 6% to cost, 10% to recall, 11% to precision, 11% to F1‐score, and 5% run time complexity.

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“…On the one hand, big data provides massive amounts of information in the training phase of ML, which improves the accuracy and applicability of model training. On the other hand, with the further development of 5G IoT technology, privacy and security have become critical points for the industry to consider [1]. FL [2] dramatically guarantees data privacy in the model training process [3] and provides an effective solution to the islanding effect in ML.…”

Section: Introductionmentioning

confidence: 99%

A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning

Zhen¹,

Wu²,

Liu³

et al. 2023

Computers, Materials &Amp; Continua

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Asynchronous federated learning (AsynFL) can effectively mitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security. However, the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning (FL) algorithm uses synchronous or asynchronous aggregation. Therefore, there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction. This paper proposes a novel Fixed-point Asynchronous Federated Learning (FixedAsynFL) algorithm, which could mitigate the resource consumption caused by frequent data communication while alleviating the effect of device heterogeneity. FixedAsynFL uses fixed-point quantization to compress the local and global models in AsynFL. In order to balance energy consumption and learning accuracy, this paper proposed a quantization scale selection mechanism. This paper examines the mathematical relationship between the quantization scale and energy consumption of the computation/communication process in the FixedAsynFL. Based on considering the upper bound of quantization noise, this paper optimizes the quantization scale by minimizing communication and computation consumption. This paper performs pertinent experiments on the MNIST dataset with several edge nodes of different computing efficiency. The results show that the FixedAsynFL algorithm with an 8-bit quantization can significantly reduce the communication data size by 81.3% and save the computation energy in the training phase by 74.9% without significant loss of accuracy. According to the experimental results, we can see that the proposed AsynFixedFL algorithm can effectively solve the problem of device heterogeneity and energy consumption limitation of edge nodes.

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6G-Enabled Internet of Things: Vision, Techniques, and Open Issues

Cited by 6 publications

References 162 publications

Blockchain‐based internet of vehicles (BIoV): A systematic review of surveys and reviews

Blockchain‐based internet of vehicles (BIoV): A systematic review of surveys and reviews

A taxonomy and survey of big data in social media

A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning

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