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Sustainable V2X Communication: Intelligent Sensing and Green Connectivity

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1291

Special Issue Editor


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Guest Editor
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Av. Carl Friedrich Gauss, 7-Edifici B4, 08860 Castelldefels, Spain
Interests: machine learning; IoT; smart cities; mmWave 5G; WSN; RFID; LoRaWAN; wireless communications; V2X; autonomous driving; cellular communication
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Special Issue Information

Dear Colleagues,

The rapid advancement of Vehicle-to-Everything (V2X) communication technologies presents unprecedented opportunities for enhancing transportation safety, efficiency, and sustainability. This Special Issue on "Sustainable V2X Communication: Intelligent Sensing and Green Connectivity" aims to explore how advanced sensor technologies and intelligent algorithms can be leveraged to create eco-friendly and robust V2X systems.

This Special Issue invites original research articles, reviews, and case studies that address various aspects of sustainable V2X communication. Topics of interest include, but are not limited to, the following:

Federated Learning: Collaborative model training across vehicles and infrastructure while preserving privacy and reducing data transfer.

Spiking Neural Networks (SNNs) and Reservoir Computing: Energy-efficient neural network architectures for real-time sensor data processing in resource-constrained V2X devices.

Physics-Informed Neural Networks (PINNs): Integrating physical models of traffic flow and vehicle dynamics into neural networks for improved prediction and control, optimizing energy consumption and emissions.

Trustworthiness and Security: Secure and reliable communication protocols for V2X, addressing data integrity, authentication, and privacy in the context of sustainability-focused algorithms.

Continual Learning: Adapting V2X systems to evolving traffic patterns and environmental conditions without catastrophic forgetting, ensuring long-term efficiency and sustainability.

Edge Computing for Sustainable V2X: Deploying intelligent algorithms at the network edge to minimize latency and reduce energy consumption associated with cloud-based processing.

Energy-Aware Communication Protocols: Designing communication protocols that minimize energy consumption in V2X networks.

Sustainable Mobility Applications: V2X applications that promote eco-friendly transportation, such as eco-routing, cooperative adaptive cruise control, and optimized traffic management.

We encourage submissions from academic researchers, industry professionals, and other stakeholders working in this dynamic field. Contributions that address real-world challenges, propose innovative solutions, or provide insightful reviews of current trends and future directions in sustainable V2X communication are particularly welcome.

Dr. Raul Parada Medina
Guest Editor

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-blind 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

  • vehicle-to-everything (V2X) communication
  • V2X systems
  • intelligent sensing
  • green connectivity

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Published Papers (1 paper)

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Research

50 pages, 7390 KB  
Article
Spiking Neural Networks with Continual Learning for Steering Angle Regression: A Sustainable AI Perspective
by Fernando S. Martínez, Sergio Costa and Raúl Parada
Sensors 2026, 26(9), 2656; https://doi.org/10.3390/s26092656 - 24 Apr 2026
Viewed by 742
Abstract
This work explores the application of Spiking Neural Networks (SNNs) and Continual Learning (CL) methodologies to the problem of steering angle regression, using autonomous driving simulation as the experimental context, with a focus on energy efficiency and alignment with sustainable computing objectives. The [...] Read more.
This work explores the application of Spiking Neural Networks (SNNs) and Continual Learning (CL) methodologies to the problem of steering angle regression, using autonomous driving simulation as the experimental context, with a focus on energy efficiency and alignment with sustainable computing objectives. The primary goal was to design and implement CL techniques in SNNs to assess the model’s ability to maintain accuracy in explored environments while reducing CO2 emissions through the optimized use of a subset of the data. This study emerges in response to the increasing energy demand of deep learning models, which poses a challenge to sustainability. SNNs, inspired by the efficiency of biological neural systems, offer significant advantages in terms of computational and energy consumption, making them a promising alternative. CL techniques, such as Elastic Weight Consolidation and replay memory, are integrated to mitigate catastrophic forgetting in sequential learning tasks. The methodology includes adapting the PilotNet architecture for SNNs, preprocessing datasets generated in the Udacity driving simulator, and evaluating models in incremental learning scenarios. The experiments compare the performance of SNNs with CL against baseline models without CL, using mean squared error (MSE), computational efficiency, and equivalent CO2 emissions as evaluation metrics. The results demonstrate that replay memory enables the retention of prior knowledge with a limited increase in energy consumption. This work concludes that SNNs with CL are a viable alternative for sustainable AI applications. Future research directions include a focus primarily on hardware-specific implementations and real-world testing. Full article
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