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Toward efficient vehicular-based virtual network infrastructure for smart cities

delete2023-08-01
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Islam Tharwat Abdel Halim *
H
Hossam Mahmoud Ahmed Fahmy
DOI:10.1016/j.jestch.2023.101456delete
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摘要

摘要

En 中文
Future smart cities will undoubtedly generate and process an immense volume of data. Developing a smart network infrastructure is thus becoming a key component for the better development and efficient governance of smart cities. The main objective of this article is to examine the feasibility of utilizing moving vehicles to develop an efficient Vehicular-based Virtual Network Infrastructure (VVNI) for smart cities. The basic idea here is to divide the whole city into distinct cells, then predict and evaluate the vehicle eligibility to act efficiently as a centralized communication and information hub for each cell. Specifically, we present the basic framework of the proposed VVNI, and identify the corresponding efficiency requirements in terms of service availability, cost, and quality; then we introduce novel metrics to characterize the vehicle's ability to cope with these requirements. Several simulation scenarios were conducted to assess the efficiency of the proposed VVNI. The simulation results demonstrate how the proposed model outperforms existing models and improves system performance in three benchmark areas: throughput, packet delivery ratio, and latency, especially in medium- and high-density network scenarios with slower vehicle velocities. The simulation results are promising to further enhance the development of the proposed network infrastructure. (c) 2023 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keyword:
Heterogeneous vehicular networks
Infrastructure stability
Mobility prediction
Network overhead
Traffic offloading
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期刊

E
Engineering Science and Technology-An International Journal-JESTECH
IF:
5.4
论文数:
1.4K
被引数:
6.3K

机构

E
egyptian knowledge bank (ekb)
学者数:
11.6W
论文数: 9.3W
被引数: 84
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