Connectivity Enhancement of E-VANET Based on QL-mRSU Self-Learning Energy-Saving Algorithm

With the development of smart cities and smart electric vehicles (EVs), the problem of improving the performance of Vehicular Ad-hoc Networks (VANETs) is gradually being emphasized. To improve the network performance of VANETs, some scholars have considered parked vehicles as roadside units, but hav...

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Bibliographic Details
Published in:IEEE access 2023, Vol.11, p.3810-3825
Main Authors: Feng, Yuxiang, Huang, Yao, Li, Bing, Peng, Hong, Wang, Jian, Zhou, Weikai
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Batteries
Clustering algorithms
E-VANET
Electric vehicles
Energy conservation
Energy consumption
Energy efficiency
energy saving
Flow stability
Machine learning
Mobile ad hoc networks
Mobile RSU
Optimization
Parking facilities
Roadsides
Traffic flow
Vehicular ad hoc networks
Wireless sensor networks
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Summary:With the development of smart cities and smart electric vehicles (EVs), the problem of improving the performance of Vehicular Ad-hoc Networks (VANETs) is gradually being emphasized. To improve the network performance of VANETs, some scholars have considered parked vehicles as roadside units, but have not paid attention to the energy consumption characteristics of vehicles, especially electric vehicles. Therefore, in this paper, we propose a QL-mRSU series artificial intelligence energy saving method to optimize the energy consumption of parked electric vehicles during communication. The method is based on electric vehicle self-organizing networks (E-VANETs), which dynamically cluster electric vehicles parked in parking lots by parameters such as traffic flow, number of service demands, and charging index in reinforcement learning, select the most suitable vehicles as mobile roadside units (mRSUs), and adjust the working mode according to environmental changes such as the number of service demands to achieve the effects of self-learning and energy saving. The simulation experimental results show that compared with other energy-based routing algorithms, the method is able to make optimal choices through self-learning with guaranteed communication quality and is more adaptable to traffic flow changes on the road, thus ensuring the stability of energy-saving efficiency. In addition, the method significantly improves the energy structure of electric vehicle parking clusters.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3235397