Blockchain-Enabled Internet of Vehicles Applications

Gao, Junting; Peng, Chunrong; Yoshinaga, Tsutomu; Han, Guorong; Guleng, Siri; Wu, Celimuge

doi:10.3390/electronics12061335

Cited by 8 publications

(4 citation statements)

References 150 publications

(216 reference statements)

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“…By exploring relevant works [5], [6], [7], [8], [9], [10] and case studies, we highlight the potential of blockchain in revolutionizing vehicle security and theft prevention.…”

Section: Related Workmentioning

confidence: 99%

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A Framework Solution for Vehicle Theft Detection by Integrating NFC With a Blockchain-Based System

Leka,

Lamani,

Hamzallari

2023

TEM Journal

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Vehicle theft is a widespread and costly problem with thousands of cars stolen annually, impacting privacy, the economy, and security. Conventional database systems have proven unreliable, enabling a thriving market for stolen vehicles. In response, our study presents an innovative solution, leveraging the security of blockchain technology. By utilizing the Ethereum Virtual Machine (EVM) and Smart Contracts, our system is designed to thwart attacks, including Distributed Denial of Services (DDoS) attacks. Notably, Near Field Communication (NFC) enables the secure transmission of sensitive data for verification. Our research aims to evaluate the real-world impact of this technology, considering factors such as improvements in data security and the efficiency of the reporting process. One of the most notable outcomes of our proposal is the substantial reduction in reporting time for car theft. While conventional reporting typically takes several hours, our system streamlines the process to less than 1 minute. This innovation offers significant benefits to vehicle owners, law enforcement agencies, and the broader community.

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“…By exploring relevant works [5], [6], [7], [8], [9], [10] and case studies, we highlight the potential of blockchain in revolutionizing vehicle security and theft prevention.…”

Section: Related Workmentioning

confidence: 99%

“…A platform based on blockchain technology that aids in identifying, authenticating, and monitoring vehicles that may be involved in criminal activities is discussed in article [9]. This platform utilizes a shared blockchain database across devices to verify if a car has been stolen and employs cameras to track suspicious vehicles.…”

Section: Related Workmentioning

confidence: 99%

A Framework Solution for Vehicle Theft Detection by Integrating NFC With a Blockchain-Based System

Leka,

Lamani,

Hamzallari

2023

TEM Journal

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“…[ 5 ] IoV acts to interconnect the vehicle with its surroundings so that it can control and monitor the movement of the vehicle to achieve intelligent mobility and vehicle network interaction. [ 6 ] In response to the demand for green development and smart cities, new energy vehicles have gradually replaced fuel vehicles as the main means of transport due to their renewable energy and zero emissions. Currently, lithium‐ion battery is considered an essential candidate for power battery of new energy electric vehicles due to its advantages of high energy density and low polluting nature.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data‐Driven Approach to Lithium‐ion Battery Dynamic Charge State Capture for New Energy Electric Vehicles

Zheng,

Huang,

Liu

et al. 2024

Advcd Theory and Sims

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As lithium‐ion batteries are the main power source of new energy vehicles, making accurate predictions of unknown State of Charge (SOC) during vehicle operation for vehicle data monitoring is vital to the advancement of intelligent new energy vehicles. In this manuscript, an expression tree‐based genetic programming regression model (ETGPR) is proposed to estimate the real‐time SOC of lithium‐ion batteries. The proposed model mainly adopts the symbolic regression technique. In addition to the current–voltage curves being fed into the model, an additional approach is designed to ensure real‐time model predictions in dynamic situations, which includes the previous moment's power in the input parameters. Different seed hyperparameters in the model are set, and the model automatically performs evolutionary calculations. Subsequently, each parameter of the model is optimally adjusted to obtain a set of regression expressions that accurately reflect the relationship between the SOC and each parameter after a specified number of iterations. Finally, the generated expression is proven to perform better in terms of its ability to capture the nonlinear relationship between SOC and battery variables. Also, the model demonstrates excellent robustness in the presence of notable noise from input‐independent features compared to other models, a root mean square error (RMSE) of less than 0.3% and a mean absolute error (MAE) of less than 0.2% are achieved. Furthermore, the potential of the model's implement‐ability under variable temperature and real driving data conditions is verified.

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“…In recent years, with the rapid development of 5G and the Internet of Things (IoT), the Internet of Vehicles (IoV) [1] has attracted increasing attention. IoV refers to the application of internet and communication technologies in the automotive field, enabling the network system for information exchange and data transmission between vehicles, vehicle-to-infrastructure, and vehicle-to-user interactions.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin-Assisted Vehicles Edge Network Computing Offloading Scheme

Zhu,

Tan,

2023

Preprint

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Traditional vehicle edge computing research has not fully considered the differences between vehicle tasks and edge server computing resources while often ignoring the use of deep reinforcement learning (DRL), which requires a large amount of training data, and DRL is easy to fall into the problem of local optimality. Therefore, this paper proposed a digital twin (DT) -assisted vehicle edge network computation offloading method. For the problem of DRL requiring a large amount of training data, a real-time data acquisition method based on DT is proposed for vehicle tasks and edge nodes. The differentiability of computing resources proposed a computing model based on deep neural network (DNN) partitioning and early exit mechanism, making full use of the computing resources of edge servers and vehicles, reducing the computation delay and transmission delay of tasks, Aiming at the problem that the asynchronous dominant actor-critic (A3C) algorithm in DRL is easy to fall into the local optimal solution, the dynamic baseline and ε-greedy policy are introduced based on A3C and used to select the channel to reduce the transmission delay further. Experimental results show that this scheme outperforms other comparative methods and can effectively reduce the delay of task offloading in vehicular edge computing.

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Blockchain-Enabled Internet of Vehicles Applications

Cited by 8 publications

References 150 publications

A Framework Solution for Vehicle Theft Detection by Integrating NFC With a Blockchain-Based System

A Framework Solution for Vehicle Theft Detection by Integrating NFC With a Blockchain-Based System

A Novel Data‐Driven Approach to Lithium‐ion Battery Dynamic Charge State Capture for New Energy Electric Vehicles

Digital Twin-Assisted Vehicles Edge Network Computing Offloading Scheme

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