A Tamper-Resistant Algorithm Using Blockchain for the Digital Tachograph

Kim, Yongbae; Back, Juyong; Kim, Jongweon

doi:10.3390/electronics10050581

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…Several researchers use DTG data for many purposes. For example, Kim et al [3] presented an algorithm that used blockchain technology to improve the security and reliability of the data from a DTG device. M.-H. Jeong et al [4] proposed a model that utilized GRUs to consider historical traffic speed data and weather conditions for forecasting highway speeds.…”

Section: Related Workmentioning

confidence: 99%

“…South Korea has implemented comprehensive measures known as the "Management Guidelines for Automobile Operation Records and Devices" to efficiently manage automobile operation records. These guidelines, enforced through the Traffic Safety Act Article 55, the Enforcement Ordinance Article 45, and the Enforcement Regulation Articles 29 and 30, encompass various aspects such as storage, submission, inspection, analysis, and utilization [3].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Bus Travel Time in Cheonan City through Deep Learning Utilizing Digital Tachograph Data

Mustafa,

Hwang,

Cho

2024

Electronics

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Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge is the accurate prediction of bus travel times, which is essential for mitigating congestion and improving the experience of public transport users. To tackle this issue, this study introduces the Hybrid Temporal Forecasting Network (HTF-NET) model, a framework that integrates machine learning techniques. The model combines an attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, enhancing its predictive capabilities. Further refinement is achieved through a Support Vector Regressor (SVR), enabling the generation of precise bus travel time predictions. To evaluate the performance of the HTF-NET model, comparative analyses are conducted with six deep learning models using real-world digital tachograph (DTG) data obtained from intracity buses in Cheonan City, South Korea. These models includes various architectures, including different configurations of LSTM and GRU, such as bidirectional and stacked architectures. The primary focus of the study is on predicting travel times from the Namchang Village bus stop to the Dongnam-gu Public Health Center, a crucial route in the urban transport network. Various experimental scenarios are explored, incorporating overall test data, and weekday and weekend data, with and without weather information, and considering different route lengths. Comparative evaluations against a baseline ARIMA model underscore the performance of the HTF-NET model. Particularly noteworthy is the significant improvement in prediction accuracy achieved through the incorporation of weather data. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), consistently highlight the superiority of the HTF-NET model, outperforming the baseline ARIMA model by a margin of 63.27% in terms of the RMSE. These findings provide valuable insights for transit agencies and policymakers, facilitating informed decisions regarding the management and optimization of public transportation systems.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Bus Travel Time in Cheonan City through Deep Learning Utilizing Digital Tachograph Data

Mustafa,

Hwang,

Cho

2024

Electronics

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“…Furthermore, tamper-resistant technology based on blockchain for data in online and offline environments has been proposed by Kim et al (2021). The suggested algorithm projected a new data recording instrument that operates in low-level hardware of digital tachographs for tamper-resistance in light blockchains and on/offline situations.…”

Section: Related Workmentioning

confidence: 99%

A Light Weight Temper Resistance Client File in an External Memory for Remote User Authentication and Access Control

Buhari

Obiniyi

Junaidu

et al. 2023

International Journal of Systems and Software Security and Protection

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This research proposes a lightweight tamper resistant client file in an external memory as an alternative to smart card for remote user authentication and access control. The benefit of using this special client file is portability and ease of acquirement, especially in school online portals, online resources portals, and e-commerce portals. The characteristics and design considerations that make smart card tamper resistant are reviewed. Techniques and characteristics to make a client file in an external memory to exhibit a lightweight tamper resistant property has been formulated. The Kumari et al.'s scheme, which is the latest research that uses external memory for remote user authentication, has been reviewed. The basic system design and software design of the proposed client file is presented and modeled. This will enable implementation of the proposed system using any prepared programming or scripting language of one's choice. The proposed scheme and reviewed scheme are also evaluated for efficiency, tamper resistance, and impersonation attack.

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A Tamper-Resistant Algorithm Using Blockchain for the Digital Tachograph

Cited by 2 publications

References 17 publications

Predicting Bus Travel Time in Cheonan City through Deep Learning Utilizing Digital Tachograph Data

Predicting Bus Travel Time in Cheonan City through Deep Learning Utilizing Digital Tachograph Data

A Light Weight Temper Resistance Client File in an External Memory for Remote User Authentication and Access Control

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