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Internet of Vehicles for Intelligent Transportation System: Current Trends and...
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Internet of Vehicles for Intelligent Transportation System: Current Trends and Future Perspectives

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6150

Special Issue Editors

Dr. Dapeng Wu

E-Mail Website
Guest Editor
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: social computing; wireless networks; big data
Dr. Boran Yang

E-Mail Website
Guest Editor
School of Artificial Intelligence, Chongqing University of Technology, Chongqing 400054, China
Interests: edge computing; edge resource sharing; network security

Special Issue Information

Dear Colleagues,

Due to the increasingly attractive driving experience, smart networked electric vehicles (EVs) are devouring the market shares of gasoline and diesel vehicles. The integration of connected electric vehicles with advanced communications technologies has paved the way for the Internet of Vehicles (IoV), offering green mobility, seamless coverage, and real-time information exchange among EVs, infrastructures, and the surrounding environments. IoV, especially that comprising EVs, has demonstrated solid potential in revolutionizing legacy transportation systems and is becoming a remarkably distributed network that supports vehicular big data analytics based on connected EVs. With the increasing investments in groundbreaking energy storage technologies and battery innovations, it is predicted that the global IoV market is expected to exceed a net worth of USD 200 billion by 2024 and the global market of smart connected EVs has seen a double-digit growth in the past few years. As an underpinning technology that co-exists with intelligent transportation systems (ITS), IoV serves as the intelligent information system to provide EV users with various types of logistics and transport services and enable collaborative, safe, efficient, and low-carbon commuting experiences.

This Special Issue aims to highlight the latest progress in addressing the newly emerging challenges in IoV for ITS along with their potential solutions, which is expected to provide a platform for both academic researchers and industry experts to exchange related technical advances and pioneering accomplishments.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Energy storage materials, technologies, and solutions for EVs in ITS;
  • Carbon footprint evaluation on EVs and legacy vehicles in IoV;
  • Energy transfer, interaction, and scheduling frameworks for EVs in ITS;
  • IoV resource management and data analytics schemes for ITS;
  • IoV algorithms and services for ITS;
  • IoV architectures and frameworks for ITS;
  • IoV image and video processing for ITS;
  • IoV security, privacy, and trust solutions for ITS;
  • IoV decision support design and prototypes for ITS;
  • Ultra-reliable and low-latency IoV protocols for ITS.

Dr. Dapeng Wu
Dr. Boran Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. World Electric Vehicle Journal is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Vehicles
  • intelligent transportation system
  • vehicular communications
  • connected cars
  • electric vehicles
  • intelligent logistics
  • vehicular big data

Published Papers (4 papers)

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Research

16 pages, 2689 KiB  
Article
Asynchronous Robust Aggregation Method with Privacy Protection for IoV Federated Learning
by Antong Zhou, Ning Jiang and Tong Tang
World Electr. Veh. J. 2024, 15(1), 18; https://doi.org/10.3390/wevj15010018 - 4 Jan 2024
Viewed by 1235
Abstract
Due to the wide connection range and open communication environment of internet of vehicle (IoV) devices, they are susceptible to Byzantine attacks and privacy inference attacks, resulting in security and privacy issues in IoV federated learning. Therefore, there is an urgent need to [...] Read more.
Due to the wide connection range and open communication environment of internet of vehicle (IoV) devices, they are susceptible to Byzantine attacks and privacy inference attacks, resulting in security and privacy issues in IoV federated learning. Therefore, there is an urgent need to study IoV federated learning methods with privacy protection. However, the heterogeneity and resource limitations of IoV devices pose significant challenges to the aggregation of federated learning model parameters. Therefore, this paper proposes an asynchronous robust aggregation method with privacy protection for federated learning in IoVs. Firstly, we design an asynchronous grouping robust aggregation algorithm based on delay perception, combines intra-group truth estimation with inter-group delay aggregation, and alleviates the impact of stragglers and Byzantine attackers. Then, we design a communication-efficient and security enhanced aggregation protocol based on homomorphic encryption, to achieve asynchronous group robust aggregation while protecting data privacy and reducing communication overhead. Finally, the simulation results indicate that the proposed scheme could achieve a maximum improvement of 41.6% in model accuracy compared to the baseline, which effectively enhances the training efficiency of the model while providing resistance to Byzantine attacks and privacy inference attacks. Full article
(This article belongs to the Special Issue Internet of Vehicles for Intelligent Transportation System: Current Trends and Future Perspectives)
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34 pages, 11051 KiB  
Article
Prototype of a System for Tracking Transit Service Based on IoV, ITS, and Machine Learning
by Camilo Andrés Sánchez Díaz, Andersson Stive Díaz Lucio, Ricardo Salazar-Cabrera, Álvaro Pachón de la Cruz and Juan Manuel Madrid Molina
World Electr. Veh. J. 2023, 14(9), 261; https://doi.org/10.3390/wevj14090261 - 14 Sep 2023
Viewed by 1179
Abstract
The transit service in a city should be the most efficient, least polluting, most accessible, and sustainable means of transportation for its citizens. However, serious shortcomings have been detected, mainly in medium-sized cities in developing countries. These shortcomings are related to a lack [...] Read more.
The transit service in a city should be the most efficient, least polluting, most accessible, and sustainable means of transportation for its citizens. However, serious shortcomings have been detected, mainly in medium-sized cities in developing countries. These shortcomings are related to a lack of user information, insecurity, low service availability, and repeated stops in inappropriate and/or unauthorized places. Some of these shortcomings contribute to high accident rates and traffic congestion. The development of tools to improve the characteristics and conditions of transit service in cities has become an imperative need to improve the quality of life of citizens and city sustainability. Transit service tracking is relevant in aspects such as online location information to travelers and control by transport companies for compliance with speed limits, schedules, routes, and stops. This research proposes a transit vehicle tracking system based on the Internet of Vehicles (IoV) in Vehicle-to-Roadside (V2R) classification. The proposed system is ideal for the use of electric vehicles due to the low power consumption of the tracking device. This system uses Intelligent Transportation Systems (ITS) tracking service architecture, Long Range (LoRa) communication technology, and its LoRa Wide Area Network (LoRaWAN) protocol. Additionally, the system offers real-time location prediction in the absence of position data. The IoV tracking device integrates a GPS-LoRa module card with an Inertial Measurement Unit (IMU). A location prediction algorithm was implemented to train and store a prediction model with previously collected data from tracking devices. To evaluate the developed model, a case study in the city of Popayán (Colombia) was implemented, using three routes for testing. The results of the system implementation were satisfactory, obtaining an average coverage of 60.4% of the routes in the final field tests through LoRa communication. For the remaining 39.6% of the routes, location data prediction was used, with an average accuracy of 177 m with respect to the real location. Considering the obtained results, a tracking system such as the one proposed in this article can be used in the transit systems of medium-sized cities in developing countries to improve service quality and fleet control. Full article
(This article belongs to the Special Issue Internet of Vehicles for Intelligent Transportation System: Current Trends and Future Perspectives)
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16 pages, 2314 KiB  
Article
Intelligent Sensing and Monitoring System for High-Voltage Transmission Line Status of Smart Grid Based on IoT Technology
by Mingzhe Hao, Nianhua Kou and Chenglin Zeng
World Electr. Veh. J. 2023, 14(8), 224; https://doi.org/10.3390/wevj14080224 - 15 Aug 2023
Viewed by 1913
Abstract
This paper integrates the Internet of Things (IoT) technology and a smart grid to build an electric power IoT architecture and analyzes the intelligent sensing technology and wireless communication technology in this electric power IoT. Through the multi-channel data collection technology in power [...] Read more.
This paper integrates the Internet of Things (IoT) technology and a smart grid to build an electric power IoT architecture and analyzes the intelligent sensing technology and wireless communication technology in this electric power IoT. Through the multi-channel data collection technology in power IoT technology and an orthogonal discrete multiwavelet transform algorithm of edge computing technology, the high-voltage transmission line status data of the smart grid are collected and processed. Then, the high-voltage transmission line condition monitoring system is designed using the node design of the high-voltage transmission line condition monitoring sensing network and the optimal sensor configuration for droop monitoring. The performance of the monitoring system is simulated and examined. The experimental results show that as the number of burst data nodes increases, the acceptance rate of the ODMT algorithm decreases from 99% to 98%, and the network survival time is over 2000. When the current exceeds 20% of the rated current, the overall measurement error is controlled at approx. 3%. At a height of 4 m, the ratio of the difference between the input voltage and output voltage sensing monitoring is approx. 5%. The error range of temperature sensing monitoring is within ±1 °C. The error rate of communication distance within 200 m is 0, and over 200 m, the error rate is approx. 7%. This system can monitor the transmission status of high-voltage lines very well. Full article
(This article belongs to the Special Issue Internet of Vehicles for Intelligent Transportation System: Current Trends and Future Perspectives)
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14 pages, 3948 KiB  
Article
Electric Vehicle Charging Transaction Model Based on Alliance Blockchain
by Dongjun Cui, Jinghan He, Xiaochun Cheng and Zhao Liu
World Electr. Veh. J. 2023, 14(7), 192; https://doi.org/10.3390/wevj14070192 - 21 Jul 2023
Cited by 1 | Viewed by 1226
Abstract
With the increasing demand for electric vehicle (EV) charging, there are cases of complicated trading between EV users and charging operators or electrical utilities. This paper proposes a new trading model based on consortium blockchain. Firstly, mutual trust interconnected transaction networks and channels [...] Read more.
With the increasing demand for electric vehicle (EV) charging, there are cases of complicated trading between EV users and charging operators or electrical utilities. This paper proposes a new trading model based on consortium blockchain. Firstly, mutual trust interconnected transaction networks and channels between charging operators are established. The PBFT consensus algorithm is used to verify EV charging transactions, and smart contracts are used to complete the process. In this model, charging station nodes of each company can verify transactions, the interconnection and independent management of charging transactions are realized, users’ charging methods are enriched, and the flexibility of charging services is improved. Finally, this paper uses hyperledger fabric to build an experimental environment. The feasibility of the above method is verified with an actual distribution scenario of some charging stations in Beijing. Full article
(This article belongs to the Special Issue Internet of Vehicles for Intelligent Transportation System: Current Trends and Future Perspectives)
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