Repository logo
Communities & Collections
Research Outputs
Fundings & Projects
People
Statistics
User Manual
Have you forgotten your password?
  1. Home
  2. Faculty of Computer Science and Engineering
  3. Faculty of Computer Science and Engineering: Conference papers
  4. MEC Empowered Internet of Vehicles for Smart City Optimisations
Details

MEC Empowered Internet of Vehicles for Smart City Optimisations

Date Issued
2024-03-01
Author(s)
Bernad, Cristina
Gilly, Katja
Roig, J. Pedro
Alcaraz, Salvador
DOI
10.1109/SWC57546.2023.10449152
Abstract
With the growth of data driven services, vehicles are moving one step beyond their connected nature by becoming an intelligent part of the Internet of Things ecosystem. The concept of Internet of Vehicles (IoV) stems from this development describing the network of humans, vehicles and things as a means of achieving an intelligent urban infrastructure. With the next-generation communication technologies promise of providing ultra low latencies, one of the major obstacles in achieving highly-performing smart city is the distribution of computing power where the vehicles computing power is no longer sufficient for the increasing multimedia based services and the cloud introducing unacceptable latency for real time scenarios. As multi-access edge computing (MEC) can be employed to overcome these issues by co-locating additional computing power with the communication access points, the main goal of this paper is to investigate the performances of this complex MEC empowered IoV ecosystem to support the development of smart cities. Our analysis shows that large scale urban scenarios can be created to simulate all relevant aspects of the ecosystem as a whole, but more work is needed in optimising the underlying technologies and their performances.
Subjects

Internet of Vehicles

Multi-Access Edge Com...

Smart Cities Simulati...

File(s)
Loading...
Thumbnail Image
Name

MEC_Empowered_Internet_of_Vehicles_for_Smart_City_Optimisations.pdf

Size

1.97 MB

Format

Adobe PDF

Checksum

(MD5):7feddefa236dfb740f52a0838ca5a44b

⠀

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Accessibility settings
  • Privacy policy
  • End User Agreement
  • Send Feedback
Repository logo COAR Notify