Blockchain in Big Data Security for Intelligent Transportation With 6G

Zhou, Zhili; Wang, Meimin; Huang, Jingwang; Lin, Shengliang; Lv, Zhihan

doi:10.1109/tits.2021.3107011

Cited by 37 publications

(16 citation statements)

References 45 publications

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“…Therefore, LGBM can implement multithreaded optimization. To this end, the complexity of the model is controlled automatically, and the probability of overfitting is considerably reduced [ 69 ].…”

Section: Methodsmentioning

confidence: 99%

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Naseri

Waygood

Wang

et al. 2022

IJERPH

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Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous studies have focused on TMC in adults, whereas predicting TMC in children has received less attention. On the other hand, previous children’s TMC prediction studies have generally focused on home-to-school TMC. Hence, LIGHT GRADIENT BOOSTING MACHINE (LGBM), as a robust machine learning method, is applied to predict children’s TMC and detect its determinants since it can present the relative influence of variables on children’s TMC. Nonetheless, the use of machine learning introduces its own challenges. First, these methods and their performance are highly dependent on the choice of “hyperparameters”. To solve this issue, a novel technique, called multi-objective hyperparameter tuning (MOHPT), is proposed to select hyperparameters using a multi-objective metaheuristic optimization framework. The performance of the proposed technique is compared with conventional hyperparameters tuning methods, including random search, grid search, and “Hyperopt”. Second, machine learning methods are black-box tools and hard to interpret. To overcome this deficiency, the most influential parameters on children’s TMC are determined by LGBM, and logistic regression is employed to investigate how these parameters influence children’s TMC. The results suggest that MOHPT outperforms conventional methods in tuning hyperparameters on the basis of prediction accuracy and computational cost. Trip distance, “walkability” and “bikeability” of the origin location, age, and household income are principal determinants of child mode choice. Furthermore, older children, those who live in walkable and bikeable areas, those belonging low-income groups, and short-distance travelers are more likely to travel by sustainable transportation modes.

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Section: Methodsmentioning

confidence: 99%

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Naseri

Waygood

Wang

et al. 2022

IJERPH

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“…Blockchain is encrypted and safeguarded with many independent nodes, reviewing chain updates to prevent unpleasant behaviors before upgrades. All parties involved may monitor and confirm the blockchain to increase data availability and confidence without burdensome and costly bureaucratic layers [35].…”

Section: Internet Of Things and Blockchain Benefitsmentioning

confidence: 99%

Security and Privacy of Blockchain-Based Single-Bit Cache Memory Architecture for IoT Systems

et al. 2022

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This paper provides an overview of blockchain technology's security and privacy features, as well as an overview of IoT-based cache memory and single-bit six transistor static random-access memory cell sense amplifier architecture. Each chip's memory is used for recorded as blocks, which are encrypted and used as a blockchain for other memory devices. The architectures comprise of the circuit of write driver, six transistor static random access memory cells, and sense amplifiers such as current differential sense amplifier, charge transfer differential sense amplifier, and voltage latch sense amplifier. Furthermore, different parameters such as the number of transistors, sensing delay, and power consumption have been analyzed for varying resistance values (i.e., R=42.3 and R=42.3K ). Apart from that, power reduction techniques such as dual sleep, forced stack, sleep transistor, and sleep stack are used to optimize power consumption. These power reduction techniques are applied over different blocks of architecture, such as six transistors static random access memory cell and sense amplifier to optimize power consumption of the architecture. The conclusion arises that a single-bit six transistor static random access memory cell with power reduction dual sleep technique voltage latch sense amplifier with power reduction dual sleep technique in architecture consumes 11.65µW of power and has 33 transistors which are lowest from other architectures.INDEX TERMS Circuit of write driver (CWD), sense amplifier (SA), current differential sense amplifier (CDSA), charge transfer differential sense amplifier (CTDSA), voltage latch sense amplifier (VLSA), six transistor static random-access memory (6T-SRAM).

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“…In the field of 6G and IoT, many published articles can be divided into several subgroups, which are as follows: Security of 6G in IoT [8][9][10], IoT applications of healthcare using 6G technology [11][12][13], IoT applications of the smart city using 6G technology [14][15][16], IoT applications of smart transportation using 6G technology [17][18][19], IoT applications of wearables using 6G technology [20,21], integration of machine learning and 6G-enabled IoT [22,23], 6G and IoT networks [1,24,25] and energy-efficient effect on 6G enabled IoT [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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

Hosseinzadeh¹,

Hemmati²,

Rahmani³

2022

Computer Modeling in Engineering &Amp; Sciences

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There are changes in the development of wireless technology systems every decade. 6G (sixth generation) wireless networks improve on previous generations by increasing dependability, accelerating networks, increasing available bandwidth, decreasing latency, and increasing data transmission speed to standardize communication signals. The purpose of this article is to comprehend the current directions in 6G studies and their relationship to the Internet of Things (IoT). Also, this paper discusses the impacts of 6G on IoT, critical requirements and trends for 6G-enabled IoT, new service classes of 6G and IoT technologies, and current 6G-enabled IoT studies selected by the systematic literature review (SLR) method published from 2018 to 2021. In addition, we present a technical taxonomy for the classification of 6G-enabled IoT, which includes self-organization systems, energy efficiency, channel assessment, and security. Also, according to the articles reviewed, we consider the evaluation factors in this domain, including data transmission, delay, energy consumption, and bandwidth. Finally, we focus on open issues and future research challenges in 6G-enabled IoT. To mention important future challenges and directions, we can point to

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Blockchain in Big Data Security for Intelligent Transportation With 6G

Cited by 37 publications

References 45 publications

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Security and Privacy of Blockchain-Based Single-Bit Cache Memory Architecture for IoT Systems

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

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