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Temporal Data Scheduling in Internet of Vehicles Using an Improved Decomposition-Based Multi-Objective Evolutionary Algorithm
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AI
DOI：10.1109/TITS.2023.3237387.png)
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Abstract
En 中文
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.
Keywords:
Internet of Vehicles
Evolutionary computation
Heuristic algorithms
Data models
Vehicle dynamics
Optimization
Job shop scheduling
Internet of Vehicles
temporal data scheduling
multi-objective evolutionary optimization
weight vector adjustment
chain segmentation
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