Vehicular Communications

Scope & Guideline

Driving Innovations in Vehicular Communication

Introduction

Delve into the academic richness of Vehicular Communications with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN2214-2096
PublisherELSEVIER
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2014 to 2024
AbbreviationVEH COMMUN / Veh. Commun.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

Aims and Scopes

The journal "Vehicular Communications" primarily focuses on the interdisciplinary aspects of vehicular networks and communications technologies. It aims to provide a platform for researchers and practitioners to share innovative ideas and advancements in the field, addressing both theoretical foundations and practical applications.
  1. Vehicular Networking and Communication Protocols:
    Research on protocols designed for communication between vehicles (V2V), between vehicles and infrastructure (V2I), and between vehicles and the cloud (V2C). This includes the development of new communication standards, enhancements in existing protocols, and performance evaluations.
  2. Security and Privacy in Vehicular Communications:
    The journal emphasizes the importance of cybersecurity measures and privacy-preserving techniques in vehicular networks. This includes authentication schemes, intrusion detection systems, and the use of blockchain technologies to secure communication.
  3. Edge and Cloud Computing for Intelligent Transportation Systems:
    Exploration of edge and cloud computing solutions that enhance vehicular networks through improved data processing, storage, and resource management, facilitating real-time decision-making in applications like traffic management and autonomous driving.
  4. Machine Learning and AI Applications:
    Application of AI and machine learning techniques to optimize vehicular communication, enhance decision-making processes, and improve traffic efficiency and safety.
  5. UAV Integration in Vehicular Networks:
    Research on the integration of unmanned aerial vehicles (UAVs) into vehicular networks, focusing on communication strategies, resource allocation, and collaborative systems for enhanced operational capabilities.
  6. Innovative Networking Technologies:
    Investigation of new networking paradigms, including 5G and beyond, as well as the use of intelligent reflecting surfaces (IRS) and other advanced technologies to improve vehicular communication performance.
The scope of research within "Vehicular Communications" is evolving, with several emerging themes gaining momentum. These trends highlight the journal's commitment to addressing contemporary challenges and innovations in vehicular networks.
  1. Federated Learning and Collaborative Intelligence:
    There is a growing interest in federated learning approaches that allow for collaborative intelligence across vehicles, enhancing data privacy while improving machine learning models for traffic prediction and anomaly detection.
  2. Blockchain Applications in Vehicular Networks:
    Blockchain technology is increasingly being explored for securing vehicular communications, enabling decentralized trust mechanisms, and enhancing data integrity and privacy in various applications.
  3. Integration of UAVs in Transportation Systems:
    The integration of UAVs into vehicular networks is a trending theme, focusing on collaborative scenarios where UAVs assist in traffic management, data collection, and communication relay.
  4. Energy-Efficient Solutions and Green Technologies:
    Research is increasingly focused on energy-efficient communication protocols and green technologies in vehicular networks, addressing the need for sustainability in transportation.
  5. AI-Driven Traffic Management and Safety Systems:
    Artificial intelligence is being leveraged to develop advanced traffic management systems that enhance safety, efficiency, and the overall user experience in smart transportation environments.
  6. Advanced Security Mechanisms and Privacy-Preserving Techniques:
    There is an increasing trend toward developing sophisticated security mechanisms that not only protect vehicular communications but also ensure user privacy through advanced cryptographic techniques.

Declining or Waning

While "Vehicular Communications" continues to advance in several areas, certain themes have started to decline in prominence over recent years. These waning scopes reflect shifts in research focus and evolving challenges in the field.
  1. Traditional Vehicle-to-Vehicle Communication Models:
    The reliance on conventional V2V communication models has decreased as researchers explore more complex and integrated systems that incorporate AI, edge computing, and advanced networking technologies.
  2. Basic Security Protocols without Advanced Mechanisms:
    Research focusing solely on traditional security protocols without incorporating modern advancements such as blockchain or machine learning is becoming less frequent, as the field demands more sophisticated solutions.
  3. Static Traffic Management Solutions:
    Older approaches to traffic management that do not leverage real-time data analytics or adaptive systems are losing traction in favor of dynamic and intelligent systems that can respond to changing traffic conditions.
  4. Single-Technology Solutions:
    There is a noticeable decline in research that focuses solely on one technology (e.g., only focusing on Wi-Fi or only on cellular networks) as the field moves towards more integrated multi-technology approaches.
  5. Generic Surveys without Specific Applications:
    Surveys that do not provide in-depth insights into specific applications or emerging technologies are becoming less relevant, as researchers seek more targeted and detailed analyses.

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