An efficient FLI-KDMSSA framework for computing resource allocation of IoV in edge computing

The combination of Mobile Edge Computing (MEC) and Internet of Vehicles (IoV) can effectively improve the network performance. However, the mobility of vehicles and the diversity of tasks make the allocation of computing resources more complex. When the vehicle is in motion, its position can change...

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Bibliographic Details
Published in:Computing Vol. 107; no. 7; p. 154
Main Authors: Hsieh, Chao-Hsien, Xu, Fengya, Yao, Xinyu
Format: Journal Article
Language:English
Published: Wien Springer Nature B.V 01.07.2025
Subjects:
Bandwidths
Cloud computing
Decision making
Edge computing
Energy consumption
Fuzzy logic
Fuzzy sets
Information storage
Internet
Internet of Vehicles
Mobile computing
Network latency
Optimization algorithms
Resource allocation
Scheduling
Search algorithms
Servers
Task complexity
Vehicles
Virtual environments
ISSN:0010-485X, 1436-5057
Online Access:Get full text
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Summary:The combination of Mobile Edge Computing (MEC) and Internet of Vehicles (IoV) can effectively improve the network performance. However, the mobility of vehicles and the diversity of tasks make the allocation of computing resources more complex. When the vehicle is in motion, its position can change at any time. This can result in overload of the edge servers. Meanwhile, vehicle tasks are sensitive to latency. It makes resource allocation within edge servers more difficult. In order to solve the above problems, this article proposes a FLI-KDMSSA framework for rational allocation of computing resources in the Internet of Vehicles. First, Fuzzy Logic Inference (FLI) algorithm is used in this framework to determine the computing nodes of IoV tasks in edge computing scenarios. This algorithm uses task length, edge server virtual machine utilization, and cloud bandwidth as parameters to establish fuzzy rules. Then, with the objective function of minimizing latency and load balancing values, this paper proposes a Discrete Multi-objective Sparrow Search Algorithm based on K-means (KDMSSA) to solve the virtual machine resource allocation scheme. The experiment is simulated on the iFogSim platform. The experiment compares the performance of the system in terms of cost, energy consumption and delay in three different optimization algorithms. To compare with PSO algorithm, the performance of KDMSSA is improved by 12.7%. To compare with SSA, the performance of KDMSSA is improved by 7.7%.
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ISSN:0010-485X
1436-5057
DOI:10.1007/s00607-025-01501-3