Options
A Comparative Analysis of different Vehicular Fog Computing Scheduling Algorithms
Khan, Iqbal Uddin; Saleem, Muhammad Aamer; Hussain, Faizan (2024): A Comparative Analysis of different Vehicular Fog Computing Scheduling Algorithms, in: Journal of Independent Studies and Research Computing, Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology, Jg. 22, Nr. 1, S. 89–102, doi: 10.31645/jisrc.24.22.1.9.
Faculty/Chair:
Author:
Title of the Journal:
Journal of Independent Studies and Research Computing
ISSN:
1998-4154
2412-0448
Publisher Information:
Year of publication:
2024
Volume:
22
Issue:
1
Pages:
Language:
English
Abstract:
Fog computing (FC) is considered one of the smart and effective solutions for service provisioning to the Internet of Everything (IoE) layer. IoE layer means the platforms including homes, vehicles, infrastructures, and alike others. FC supports the IoE layer in a smart and charismatic manner by providing services near the smart devices, from vehicles to other mobilities, and fitting the response and delay time requirements. In the proposed paper, the authors focused on discussing vehicular fog computing (VFC) and how FC supports smart vehicles operating on the IoT layer. In the previous decade, scheduling algorithms are proposed by different scholars such as RTFRS, FCFS, ILP, and others to improve the working and efficacy of VFC. In the proposed paper, an analysis is done on the VFC scheduling algorithms published in the last three years in accordance with areas such as traditional, meta-heuristic, deep reinforcement learning, fuzzy logic, heuristic, and integer linear programming. The analysis is done to examine the areas of scheduling such as task scheduling, resource allocation, and others on which the existing solutions are working. This helps in inspecting the gap across which in future further work can be done.
Keywords: ;  ; 
Cloud Computing
Internet of Things
Autonomous Vehicles
Type:
Article
Activation date:
March 6, 2026
Versioning
Question on publication
Permalink
https://fis.uni-bamberg.de/handle/uniba/114118