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Intelligent Vehicular Network and Communication Systems
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Intelligent Vehicular Network and Communication Systems

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Communications".

Deadline for manuscript submissions: 20 March 2027 | Viewed by 1348

Editors

Dr. Sabur Baidya

E-Mail Website
Guest Editor
Computer Science and Engineering, University of Louisville, Louisville, KY 40292, USA
Interests: Vehicular communications, Internet-of-things involving sensing, communications, and computing systems
Special Issues, Collections and Topics in MDPI journals
Dr. Joong-Lyul Lee

E-Mail Website
Guest Editor
Department of Software Engineering, Gyeongsang National University, Jinju, Republic of Korea
Interests: VANET; blockchain; wireless networks; wireless sensor networks

Special Issue Information

Dear Colleagues,

In modern smart transportation systems, vehicles and other road users are equipped with smart sensors and are interconnected to form an Internet-of-Vehicles (IoV). This facilitates advanced vehicular applications based on multi-sensor data acquisition and processing to provide an efficient and safe transportation. Vehicles can now coordinate with other vehicles, bicyclists, pedestrians, roadside sensors, and infrastructures over vehicle-to-everything (V2X) communications. Hence, V2X communication is now bidirectional and extends to vehicle-to-infrastructure (V2I or I2V), vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P or P2V), or vehicle-to-network (V2N). V2X communications enable road users to have augmented information from other road users by incorporating various vehicular sensors and exchanging the data from those sensors over wireless.

The wireless technologies for V2X communications have evolved from Wi-Fi-based vehicular ad hoc network (VANET) to dedicated short-range communications (DSRC) to Cellular Vehicle-to-Everything (C-V2X) communications, and other wireless technologies. Along with this, the recent advancement in integrated sensing and communications (ISAC), edge/cloud computing, and artificial intelligence technologies intend to provide solutions toward intelligent vehicular networks and transportation systems. However, these technologies come with various challenges in robustness, performance efficiency, safety, and security. This Special Issue focuses on the fundamental technology, approach, and techniques for the intelligent vehicular network and communication systems. Authors are encouraged to submit their research works from theoretical, methodological, or practical focuses, such as simulation models, real-world experiments, algorithms, and applications concerning intelligent vehicular networks and communication systems.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Vehicle-to-vehicle (V2V) communications;
  • V2I, I2V, V2P, V2N Communications;
  • Internet of Vehicles;
  • V2X technologies, e.g., DSRC, WiFi, 5G mmWave;
  • Cellular vehicle-to-everything (C-V2X) communications;
  • Vehicular Edge/Fog Computing;
  • Intelligent vehicular computing with edge-cloud continuum;
  • Resource allocation and self-adaptive vehicular communications;
  • Protocols, architectures, and applications for vehicular communications;
  • Integrated sensing and communications in vehicular technologies;
  • AI-native and AI-assisted Intelligent vehicular communications.

Dr. Sabur Baidya
Dr. Joong-Lyul Lee
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • V2X Communications
  • VANET
  • vehicular/edge computing
  • V2V communications
  • internet-of-vehicles

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Published Papers (2 papers)

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Research

28 pages, 2310 KB  
Article
Online-Tuned Fuzzy Pre-Filtering with an Attention BiLSTM for Misbehavior Detection in Vehicular Named Data Networking
by Bassma Aldahlan
Sensors 2026, 26(13), 4179; https://doi.org/10.3390/s26134179 - 2 Jul 2026
Viewed by 311
Abstract
Vehicular Named Data Networking (VNDN) inherits the broadcast-oriented forwarding of NDN, which exposes safety messages to position-falsification attacks. Existing detectors rely either on static fuzzy thresholds, which drift as traffic patterns change, or on opaque deep models, which are accurate but uninterpretable to [...] Read more.
Vehicular Named Data Networking (VNDN) inherits the broadcast-oriented forwarding of NDN, which exposes safety messages to position-falsification attacks. Existing detectors rely either on static fuzzy thresholds, which drift as traffic patterns change, or on opaque deep models, which are accurate but uninterpretable to safety auditors. We propose a two-stage detector that combines an Adaptive Fuzzy Membership Tuning (AFMT) pre-filter with an attention-augmented bidirectional LSTM. AFMT is a Mamdani fuzzy classifier whose triangular membership-function parameters are updated online by gradient descent on a prediction-error feedback signal from the downstream BiLSTM, replacing offline-fixed thresholds. The BiLSTM consumes the fuzzy suspicion score as an extra feature and produces interpretable per-time-step attention weights aligned with attack onsets. On a simulator-synthesized VNDN benchmark following the five canonical VeReMi attack types, the detector attains F1-scores between 0.955 and 0.979 (macro-average 0.964), ties the strongest baselines on the hardest Random-Offset attack while achieving the highest ROC-AUC of all models (0.984), and runs in 0.44 ms per sample on a CPU. On a live OMNeT++/Veins/SUMO testbed running the five attacks on the LuST scenario, the detector attains an F1 value of 0.986. A leave-one-feature-out study shows that detection does not hinge on the Kalman plausibility feature, and on the real public VeReMi v1.0 dataset the architecture transfers to four of the five attack types at an F1 near 1.0, while the Constant Offset stays invisible to kinematics-only features, and this quantifies the value of the named-data-plane features. Every number reported here is measured from the running detector. Full article
(This article belongs to the Special Issue Intelligent Vehicular Network and Communication Systems)
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19 pages, 378 KB  
Article
Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection
by Jonggwon Kim, Hyungchul Im, Semin Kim and Seongsoo Lee
Sensors 2026, 26(12), 3964; https://doi.org/10.3390/s26123964 - 22 Jun 2026
Viewed by 557
Abstract
Modern connected vehicles rely on the controller area network (CAN) to disseminate safety-critical in-vehicle information, including sensor-related and vehicle-state signals such as engine revolutions per minute (RPM) and gear state, among electronic control units (ECUs). Because CANs lack built-in authentication and encryption, malicious [...] Read more.
Modern connected vehicles rely on the controller area network (CAN) to disseminate safety-critical in-vehicle information, including sensor-related and vehicle-state signals such as engine revolutions per minute (RPM) and gear state, among electronic control units (ECUs). Because CANs lack built-in authentication and encryption, malicious message injection and spoofing can compromise the integrity and availability of vehicular sensing and control functions. Existing deep-learning-based intrusion-detection systems (IDSs) show a clear trade-off: supervised methods perform well on known attacks but rely on costly labels, whereas unsupervised methods can identify unseen attacks but often suffer from high false-positive rates. To address these limitations, this paper proposes a semi-supervised generative adversarial network (SGAN) framework for CAN bus intrusion detection that combines image-based CAN representation with adversarial learning. Consecutive CAN messages are converted into 64×9 grayscale images, and the proposed framework is trained in three phases. First, the discriminator establishes an initial decision boundary using a small labeled subset. It then refines this boundary through distribution-level likelihood objectives and generated samples. Finally, the generator is trained to produce realistic samples capable of deceiving the discriminator. The proposed method was evaluated on the Hacking and Countermeasure Research Lab (HCRL) car-hacking dataset using leave-one-class-out experiments to simulate unknown attacks and achieved an average accuracy of 99.73% and an average F1-score of 99.63% on unknown attacks. Moreover, with only 0.21 M parameters and 3.25 M floating-point operations (FLOPs), the model is well suited for resource-constrained in-vehicle platforms. These results indicate that the proposed framework can serve as a practical cybersecurity component for protecting CAN-carried data in vehicular sensing applications. Full article
(This article belongs to the Special Issue Intelligent Vehicular Network and Communication Systems)
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