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Intelligent Sensors and Sensing Technologies in Vehicle Networks
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Intelligent Sensors and Sensing Technologies in Vehicle Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (20 January 2025) | Viewed by 6646

Special Issue Editor

Prof. Dr. Jordi Mongay Batalla

E-Mail Website
Guest Editor
Faculty of Electronics and Informatics Technologies, Warsaw University of Technology, Warsaw, Poland
Interests: cybersecurity (risk assessment, security enforcement, vulnerabilities management); IP technologies (radio: 5G and 6G, core: network services chain, SDN, AI); applications (DLT and blockchain, Internet of Things, smart cities, multimedia) for the future internet
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vehicle technologies are always in need of more sensing technologies in order to increase safety on the road and the comfort of passengers. Novel and more-precise sensors are needed both inside and outside of the car.

Outside, the sensors are mainly used for understanding the external environment and for increasing safety on the road. They are based on visual and wireless radio-frequency technologies and include more immediate communications of sensing information among the sensory infrastructure elements. The challenges that sensors face are related to the impossibility of foreseeing the conditions on the road, including unpredictable human behavior and weather conditions.

On the other hand, the comfort and well-being of the passengers, especially the driver, are the main concern of sensing technologies inside the car. These technologies are normally based on both contact sensing (e.g., vibrations) and contactless sensing, including visual (e.g., analysis of gestures) and acoustic sensors. The main challenges are the efficiency of the sensors and their capacity to describe with precision health and comfort situations.

Despite the significant technological advances in vehicle sensors in recent years, there is still room for the improvement of sensing technologies for enhancing the transport experience. Therefore, the research community is searching for solutions on the following topics:

  • Advances in radar and image recognition for vehicle transportation;
  • Wireless radio-frequency sensing in bad-weather conditions;
  • Opensource sensing technologies for safety on the road;
  • Enhancements of visual and acoustic contactless sensing;
  • Contact and contactless sensing for driver’s healthcare;
  • Monitoring sensors for the well-being of passengers;
  • Analysis of movements of living beings for prediction of unsafe situations;
  • Methods for measuring and comparing the efficiency and performance of sensing technologies.

Prof. Dr. Jordi Mongay Batalla
Guest Editor

Manuscript Submission Information

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Published Papers (4 papers)

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Research

14 pages, 1057 KiB  
Article
Data-Driven Clustering Analysis for Representative Electric Vehicle Charging Profile in South Korea
by Kangsan Kim, Geumbee Kim, Jiwon Yoo, Jungeun Heo, Jaeyoung Cho, Seunghyoung Ryu and Jangkyum Kim
Sensors 2024, 24(21), 6800; https://doi.org/10.3390/s24216800 - 23 Oct 2024
Viewed by 1157
Abstract
As the penetration of electric vehicles (EVs) increases, an understanding of EV operation characteristics becomes crucial in various aspects, e.g., grid stability and battery degradation. This can be achieved through analyzing large amounts of EV operation data; however, the variability in EV data [...] Read more.
As the penetration of electric vehicles (EVs) increases, an understanding of EV operation characteristics becomes crucial in various aspects, e.g., grid stability and battery degradation. This can be achieved through analyzing large amounts of EV operation data; however, the variability in EV data according to the user complicates unified data analysis and identification of representative patterns. In this research, a framework that captures EV charging characteristics in terms of charge–discharge area is proposed using actual field data. In order to illustrate EV operation characteristics in a unified format, an individual EV operation profile is modeled by the probability distribution of the charging start and end states of charge (SoCs).Then, hierarchical clustering analysis is employed to derive representative charging profiles. Using large amounts of real-world, vehicle-specific EV data in South Korea, the analysis results reveal that EV charging characteristics in terms of the battery charge–discharge area can be summarized into seven representative profiles. Full article
(This article belongs to the Special Issue Intelligent Sensors and Sensing Technologies in Vehicle Networks)
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26 pages, 6870 KiB  
Article
Optimizing Indoor Airport Navigation with Advanced Visible Light Communication Systems
by Manuela Vieira, Manuel Augusto Vieira, Gonçalo Galvão, Paula Louro, Pedro Vieira and Alessandro Fantoni
Sensors 2024, 24(16), 5445; https://doi.org/10.3390/s24165445 - 22 Aug 2024
Cited by 1 | Viewed by 1145
Abstract
This study presents a novel approach to enhancing indoor navigation in crowded multi-terminal airports using visible light communication (VLC) technology. By leveraging existing luminaires as transmission points, encoded messages are conveyed through modulated light signals to provide location-specific guidance. The objectives are to [...] Read more.
This study presents a novel approach to enhancing indoor navigation in crowded multi-terminal airports using visible light communication (VLC) technology. By leveraging existing luminaires as transmission points, encoded messages are conveyed through modulated light signals to provide location-specific guidance. The objectives are to facilitate navigation, optimize routes, and improve system performance through Edge/Fog integration. The methodology includes the use of tetrachromatic LED-equipped luminaires with On–Off Keying (OOK) modulation and a mesh cellular hybrid structure. Detailed airport modeling and user analysis (pedestrians and luggage/passenger carriers) equipped with PINPIN optical sensors are conducted. A VLC-specific communication protocol with coding and decoding techniques ensures reliable data transmission, while wayfinding algorithms offer real-time guidance. The results show effective data transmission and localization, enabling self-localization, travel direction inference, and route optimization. Agent-based simulations demonstrate improved traffic control, with analyses of user halting and average speed. This approach provides reliable indoor navigation independent of GPS signals, enhancing accessibility and convenience for airport users. The integration of VLC with Edge/Fog architecture ensures efficient movement through complex airport layouts. Full article
(This article belongs to the Special Issue Intelligent Sensors and Sensing Technologies in Vehicle Networks)
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17 pages, 2409 KiB  
Article
Graph Attention Informer for Long-Term Traffic Flow Prediction under the Impact of Sports Events
by Yaofeng Song, Ruikang Luo, Tianchen Zhou, Changgen Zhou and Rong Su
Sensors 2024, 24(15), 4796; https://doi.org/10.3390/s24154796 - 24 Jul 2024
Cited by 5 | Viewed by 1860
Abstract
Traffic flow prediction is one of the challenges in the development of an Intelligent Transportation System (ITS). Accurate traffic flow prediction helps to alleviate urban traffic congestion and improve urban traffic efficiency, which is crucial for promoting the synergistic development of smart transportation [...] Read more.
Traffic flow prediction is one of the challenges in the development of an Intelligent Transportation System (ITS). Accurate traffic flow prediction helps to alleviate urban traffic congestion and improve urban traffic efficiency, which is crucial for promoting the synergistic development of smart transportation and smart cities. With the development of deep learning, many deep neural networks have been proposed to address this problem. However, due to the complexity of traffic maps and external factors, such as sports events, these models cannot perform well in long-term prediction. In order to enhance the accuracy and robustness of the model on long-term time series prediction, a Graph Attention Informer (GAT-Informer) structure is proposed by combining the graph attention layer and informer layer to capture the intrinsic features and external factors in spatial–temporal correlation. The external factors are represented as sports events impact factors. The GAT-Informer model was tested on real-world data collected in London, and the experimental results showed that our model has better performance in long-term traffic flow prediction compared to other baseline models. Full article
(This article belongs to the Special Issue Intelligent Sensors and Sensing Technologies in Vehicle Networks)
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18 pages, 445 KiB  
Article
Joint Optimization of Age of Information and Energy Consumption in NR-V2X System Based on Deep Reinforcement Learning
by Shulin Song, Zheng Zhang, Qiong Wu, Pingyi Fan and Qiang Fan
Sensors 2024, 24(13), 4338; https://doi.org/10.3390/s24134338 - 4 Jul 2024
Cited by 3 | Viewed by 1735
Abstract
As autonomous driving may be the most important application scenario of the next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) [...] Read more.
As autonomous driving may be the most important application scenario of the next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in LTE-V2X, allowing direct communication between vehicles. This supplements SL communication in LTE-V2X and represents the latest advancements in cellular V2X (C-V2X) with the improved performance of NR-V2X. However, in NR-V2X Mode 2, resource collisions still occur and thus degrade the age of information (AOI). Therefore, an interference cancellation method is employed to mitigate this impact by combining NR-V2X with Non-Orthogonal multiple access (NOMA) technology. In NR-V2X, when vehicles select smaller resource reservation intervals (RRIs), higher-frequency transmissions use more energy to reduce AoI. Hence, it is important to jointly considerAoI and communication energy consumption based on NR-V2X communication. Then, we formulate such an optimization problem and employ the Deep Reinforcement Learning (DRL) algorithm to compute the optimal transmission RRI and transmission power for each transmitting vehicle to reduce the energy consumption of each transmitting vehicle and the AoI of each receiving vehicle. Extensive simulations demonstrate the performance of our proposed algorithm. Full article
(This article belongs to the Special Issue Intelligent Sensors and Sensing Technologies in Vehicle Networks)
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