Hash-chain-based IoT authentication scheme suitable for small and medium enterprises

Jeong, Yoon-Su; Yon, Yong-Ho; Ku, Jin-Hee

doi:10.22156/cs4smb.2017.7.4.105

Cited by 5 publications

(4 citation statements)

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“…Equation (2) shows that the proposed model interleaves IIoT unique identification information I IoT_I i to incorporate IIoT unique identification in the produced bit block (1).…”

Section: Iiot Block Generationmentioning

confidence: 99%

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Secure IIoT Information Reinforcement Model Based on IIoT Information Platform Using Blockchain

Jeong

2022

Sensors

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Data created at industrial sites through industrial internet of things devices are now being processed automatically or in real-time in the industrial structure, due to the application of artificial intelligence technology to industrial sites. However, the expenses of autonomous or real-time data processing and steady data processing (analysis, prediction, prescription, and implementation) necessitate a new processing method. We propose a blockchain-based industrial internet of things information reinforcement model in this work that may reliably ensure the integrity of industrial internet of things data produced at industrial locations. The proposed model processes industrial internet of things data that may occur at endpoints at industrial sites into the blockchain by processing data generated by the same industrial internet of things device independently. As a result, the IIoT data sent to the industrial internet of things server can be evaluated more readily, and production accuracy may be enhanced. The proposed model optimizes industrial internet of things information linkage by stochastically reflecting the information based on attribute value frequency. By dynamically aggregating the related data of industrial internet of things information acquired as a seed through hierarchical subnets, the proposed model increases stability and accuracy. Furthermore, the proposed model may be used to enhance an organizations’ operational efficiency (consulting and training, for example) and strategic decision-making by utilizing fundamental knowledge about items produced at industrial locations. Furthermore, the proposed model allows for information sharing and system connectivity between industrial locations, allowing for close collaboration between industrial internet of things features. As a result of the performance evaluation, the proposed model included an industrial internet of things sensor to the blockchain, eliminating the need for an extra function in the manufacturing process and reducing the time required to validate the integrity of industrial internet of things data. In addition, as a result of analyzing industrial internet of things data by an algorithm according to the number of simulated clouds, the accuracy of industrial internet of things information was improved by 2.5% to 3%, on average.

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“…Equation (2) shows that the proposed model interleaves IIoT unique identification information I IoT_I i to incorporate IIoT unique identification in the produced bit block (1).…”

Section: Iiot Block Generationmentioning

confidence: 99%

“…IIoT follows the same principles as IoT, but it adds automated layers and reporting capabilities. However, due to a lack of standards, integration with legacy technologies, pre-investment expenditures, and knowledge, it is not effectively employed in the industrial context [2].…”

Section: Introductionmentioning

confidence: 99%

Secure IIoT Information Reinforcement Model Based on IIoT Information Platform Using Blockchain

Jeong

2022

Sensors

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“…Driver fatigue is indeed a significant factor in road accidents, leading to numerous mishaps and fatalities worldwide [1]. The World Health Organization reported approximately 1.4 million deaths annually due to vehicle crashes.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an IoT Drowsiness Detection System for Drivers

KALLEL

2023

Preprint

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Drowsiness stands as a significant peril to road safety, manifesting as a prominent contributor to severe injuries, fatalities, and substantial economic ramifications within the realm of road accidents. The presence of drowsiness substantially diminishes driving performance, fostering a decline in attentiveness and reaction times. This, in turn, exacerbates the potential for accidents and underscores the criticality of addressing drowsiness-related issues to mitigate the adverse consequences on road safety. The objective of this research work is to design and implement an IoT based intelligent alert system for vehicles, capable of automatically mitigating the risks associated with drowsy driving. Indeed, we propose a real time drowsy driver alert system including a hardware and a software parts. The hardware part includes a camera for face image acquisition and a Raspberry Pi 4 platform for real time face image processing to analyze eye blinks and drowsiness detection. The software part includes a web application for drivers’ management and a mobile application for drowsiness detection and notification management. In fact, once the driver's drowsiness is detected, the system instantaneously sends all details to a wireless connected real-time database and the mobile application module issues a warning message, while a Raspberry Pi monitoring system delivers an audible alert to the driver.

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“…According to the statistics of Korea on traffic accidents in the last 5 years, driving while drowsy is one of the most important factors in traffic accidents, and its related mortality rate is more than 2 times higher than other causes of traffic accidents [1]. As a solution to resolve these problems, it is possible to reduce the mortality rate of such traffic accidents by detecting and preventing drivers from driving while drowsy.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Detection System for Drowsy Driving Prevention Using Image Recognition and IoT

Jang

Ahn

2020

Sustainability

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In recent years, the casualties of traffic accidents caused by driving cars have been gradually increasing. In particular, there are more serious injuries and deaths than minor injuries, and the damage due to major accidents is increasing. In particular, heavy cargo trucks and high-speed bus accidents that occur during driving in the middle of the night have emerged as serious social problems. Therefore, in this study, a drowsiness prevention system was developed to prevent large-scale disasters caused by traffic accidents. In this study, machine learning was applied to predict drowsiness and improve drowsiness prediction using facial recognition technology and eye-blink recognition technology. Additionally, a CO2 sensor chip was used to detect additional drowsiness. Speech recognition technology can also be used to apply Speech to Text (STT), allowing a driver to request their desired music or make a call to avoid drowsiness while driving.

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Hash-chain-based IoT authentication scheme suitable for small and medium enterprises

Cited by 5 publications

References 0 publications

Secure IIoT Information Reinforcement Model Based on IIoT Information Platform Using Blockchain

Secure IIoT Information Reinforcement Model Based on IIoT Information Platform Using Blockchain

Design and Implementation of an IoT Drowsiness Detection System for Drivers

Implementation of Detection System for Drowsy Driving Prevention Using Image Recognition and IoT

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