Temporal Data Scheduling in Internet of Vehicles Using an Improved Decomposition-Based Multi-Objective Evolutionary Algorithm

Nowadays, Internet of Vehicles plays an important role in the emerging intelligent transportation systems. In Internet of Vehicles, it is crucial to make appropriate temporal data scheduling to ensure the service quality and the high service ratio to meet the requests from vehicles. However, service...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 24; no. 5; pp. 5282 - 5295
Main Authors: Li, Ya-Lun, Chai, Zheng-Yi, Tan, Fei, Liu, Xu
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
chain segmentation
Data models
Decomposition
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Heuristic algorithms
Hybrid vehicles
Intelligent transportation systems
Internet
Internet of Vehicles
Job shop scheduling
multi-objective evolutionary optimization
Multiple objective analysis
Optimization
Quality of service
Roadsides
Scheduling
temporal data scheduling
Traffic delay
Vehicle dynamics
Vehicle-to-infrastructure
Vehicles
weight vector adjustment
Workload
Workloads
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:Nowadays, Internet of Vehicles plays an important role in the emerging intelligent transportation systems. In Internet of Vehicles, it is crucial to make appropriate temporal data scheduling to ensure the service quality and the high service ratio to meet the requests from vehicles. However, service quality and service ratio are two conflict goals because of the limited bandwidth and the vehicle mobility in Internet of Vehicles. In order to optimize these two conflict objectives simultaneously, we present an improved decomposition based multi-objective evolutionary algorithm for the temporal data scheduling (I-MOEA/D-TDS) in Internet of Vehicles. Based on the MOEA/D framework, we integrate a self-adaptive weight vector adjustment method based on chain segmentation to improve the performances of temporal data scheduling. For verifying the availability of presented algorithm, under the hybrid Vehicle-to-Infrastructure / Vehicle-to-Vehicle (V2I/V2V) communications and multiple Roadside Units (RSUs) scenario, we compare the proposed algorithm with several related algorithms under the effects of data valid periods, service workloads, maximum tolerated delay, and traffic workloads. Experimental results suggest that the presented algorithm can achieve better data service quality and service ratio.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3237387