Special Issues
Table of Content

Cooperative ML/DL and Intelligent Networks towards Vehicle Health Monitoring System (VHMS)

Submission Deadline: 15 July 2023 (closed)

Guest Editors

Dr. Md Arafatur Rahman, University of Wolverhampton, United Kingdom
Dr. Arif Reza Anwary, Edinburgh Napier University, United Kingdom
Dr. Nour Moustafa, University of New South Wales, Australia


The dependency on vehicles is increasing tremendously due to its excellent transport capacity, fast, efficient, flexible, pleasant journey, minimal physical effort, and substantial economic impact. As a result, the demand for smart and intelligent feature enhancement is growing and becoming a prime concern for maximum productivity based on the current perspective. In this case, the Internet of Everything (IoE) is an emerging concept that can play an essential role in the automotive industry by integrating the stakeholders, process, data, and things via networked connections. But the unavailability of intelligent features leads to negligence about proper maintenance of vehicle vulnerable parts, reckless driving and severe accident, lack of instructive driving, and improper decision, which incurred extra expenses for maintenance besides hindering national economic growth.

It is therefore a challenge to choose the best methodologies for a central VHMS exploiting IoE-driven Multi-Layer Heterogeneous Networks (HetNet) and AI techniques to oversee individual vehicle health conditions, notify the respective owner driver real-timely and store the information for further necessary action. Another challenge is to find methodologies for VHMS, IoE-driven Multi-tire HetNet, with a secure and trustworthy data collection and analytics system. The aim of this Special Issue is to show recent and novel applications of VHMS in the field of automotive sector. This special issue aims to share and exchange innovative theories, practices, and approaches in Vehicular technologies and communications to unveil the challenging issues associated in deploying the VHMS applications. It may motivate the researcher to develop a central intelligent and secure vehicular condition diagnostic system to move this sector towards Industry 4.0.

List of potential topics include, but are not limited to:

· Innovative Technologies for VHMS

· VHMS framework/architecture towards Industry 4.0

· Technology support of essential VHMS operations

· Interventions and disaster risk reduction technologies

· VHMS science, participatory surveillance, and crowdsourcing

· Intelligent Networks for VHMS

· Internet of Things/sensors for VHMS

· Heterogeneous Networks infrastructure and/or architecture for VHMS

· Big data modeling and machine learning on VHMS

· VHMS response to emergencies

· Technology in community engagement for VHMS

· Methodological and technological challenges in VHMS

· Methodological and technological challenges in the implementation, transfer, and/or scaling up of VHMS

· Studies on the effectiveness and safety for VHMS

· Economic impact on VHMS– cost-effectiveness, budgetary impact, and opportunity costs  

· Micro-service for VHMS

· Security for VHMS

· Essential security protocols for VHMS

· Secure data analytics for VHMS

· Intrusion detection and resiliency for VHMS

· Secure development of VHMS

· Privacy-enhanced technologies for VHMS

· Relation of security and safety for VHMS

· Data Analytics for VHMS

· VHMS decision-support systems

· Artificial intelligence, machine/deep learning and other computational techniques in VHMS

· Application of Robotics in VHMS

· Psycho-social and behavioral aspects of VHMS

· Augmented, virtual and mixed reality in VHMS

· Governance, ethical and legal challenges of VHMS data and technologies

· Cybersecurity in VHMS

Published Papers

  • Open Access


    Secure and Reliable Routing in the Internet of Vehicles Network: AODV-RL with BHA Attack Defense

    Nadeem Ahmed, Khalid Mohammadani, Ali Kashif Bashir, Marwan Omar, Angel Jones, Fayaz Hassan
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 633-659, 2024, DOI:10.32604/cmes.2023.031342
    (This article belongs to the Special Issue: Cooperative ML/DL and Intelligent Networks towards Vehicle Health Monitoring System (VHMS))
    Abstract Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles (IoV). However, intricate security challenges are intertwined with technological progress: Vehicular ad hoc Networks (VANETs), a core component of IoV, face security issues, particularly the Black Hole Attack (BHA). This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability; also, BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes altogether. Recognizing the importance of this challenge, we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier… More >

  • Open Access


    Deep Learning Based Automatic Charging Identification and Positioning Method for Electric Vehicle

    Hao Zhu, Chao Sun, Qunfeng Zheng, Qinghai Zhao
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 3265-3283, 2023, DOI:10.32604/cmes.2023.025777
    (This article belongs to the Special Issue: Cooperative ML/DL and Intelligent Networks towards Vehicle Health Monitoring System (VHMS))
    Abstract Electric vehicle charging identification and positioning is critically important to achieving automatic charging. In terms of the problem of automatic charging for electric vehicles, a dual recognition and positioning method based on deep learning is proposed. The method is divided into two parts: global recognition and localization and local recognition and localization. In the specific implementation process, the collected pictures of electric vehicle charging attitude are classified and labeled. It is trained with the improved YOLOv4 network model and the corresponding detection model is obtained. The contour of the electric vehicle is extracted by the… More >

    Graphic Abstract

    Deep Learning Based Automatic Charging Identification and Positioning Method for Electric Vehicle

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