A Secure and Intelligent Framework for Vehicle Health Monitoring Exploiting Big-Data Analytics

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. For this, the authors proposed a conceptual framework for a central VHMS exploiting IoE-driven Multi-Layer Heterogeneous Networks (HetNet) and a machine learning technique to oversee individual vehicle health conditions, notify the respective owner-driver real-timely and store the information for further necessary action. This article transparently portrayed an overview of central VHMS and proposed the taxonomy to achieve such an objective. Subsequently, they unveiled the framework for central VHMS, IoE-driven Multi-tire HetNet, with a secure and trustworthy data collection and analytics system. Finally, anticipating this proposition’s outcome is immense in the automotive sector. It may motivate the researcher to develop a central intelligent and secure vehicular condition diagnostic system to move this sector towards Industry 4.0.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2021.3138255
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Rahman, Md Arafatur
  • Rahim, Md Abdur
  • Rahman, Md Mustafizur
  • Moustafa, Nour
  • Razzak, Imran
  • Ahmad, Tanvir
  • Patwary, Mohammad N
• Publication Date: 2022-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 19727-19742
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 10
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Internet of things; Machine learning; Monitoring; Vehicle maintenance
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Vehicles and Equipment;

Filing Info

• Accession Number: 01870964
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 24 2023 9:29AM