Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/30109
Title: MEC Empowered Internet of Vehicles for Smart City Optimisations
Authors: Filiposka, Sonja 
Bernad, Cristina
Kjorveziroski, Vojdan 
Gilly, Katja
Roig, J. Pedro
Alcaraz, Salvador
Keywords: Internet of Vehicles
Multi-Access Edge Computing
Smart Cities Simulation
Issue Date: 1-Mar-2024
Publisher: IEEE
Source: S. Filiposka, C. Bernad, V. Kjorveziroski, K. Gilly, P. J. Roig and S. Alcaraz, "MEC Empowered Internet of Vehicles for Smart City Optimisations," 2023 IEEE Smart World Congress (SWC), Portsmouth, United Kingdom, 2023, pp. 828-833, doi: 10.1109/SWC57546.2023.10449152.
Conference: 2023 IEEE Smart World Congress (SWC)
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.
URI: http://hdl.handle.net/20.500.12188/30109
DOI: 10.1109/SWC57546.2023.10449152
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers

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