A Low-Cost Resource Re-Allocation Scheme for Increasing the Number of Guaranteed Services in Resource-Limited Vehicular Networks

Author:

Meng YunORCID,Dong Yuan,Wu Chunling,Liu Xinyi

Abstract

Vehicular networks are becoming increasingly dense due to expanding wireless services and platooning has been regarded as a promising technology to improve road capacity and on-road safety. Constrained by limited resources, not all communication links in platoons can be allocated to the resources without suffering interference. To guarantee the quality of service, it is required to determine the set of served services at which the scale of demand exceeds the capability of the network. To increase the number of guaranteed services, the resource allocation has to be adjusted to adapt to the dynamic environment of the vehicular network. However, resource re-allocation results in additional costs, including signal overhead and latency. To increase the number of guaranteed services at a low-cost in a resource-limited vehicular network, we propose a time dynamic optimization method that constrains the network re-allocation rate. To decrease the computational complexity, the time dynamic optimization problem is converted into a deterministic optimization problem using the Lyapunov optimization theory. The simulation indicates that the analytical results do approximate the reality, and that the proposed scheme results in a higher number of guaranteed services as compared to the results of a similar algorithm.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Profit Maximization of Independent Task Offloading in MEC-Enabled 5G Internet of Vehicles;IEEE Transactions on Intelligent Transportation Systems;2024

2. Comprehensive Assessment of Resource Allocation Techniques for Leading V2V Network Technologies;2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES);2023-04-28

3. Improving the quality of service by continuous traffic monitoring using reinforcement learning model in VANET;International Journal of Modeling, Simulation, and Scientific Computing;2022-12

4. Improving the Quality of Service (QoS) and Resource Allocation in Vehicular Platoon Using Meta-Heuristic Optimization Algorithm;International Journal of Foundations of Computer Science;2022-08-30

5. A Survey on Resource Allocation in Vehicular Networks;IEEE Transactions on Intelligent Transportation Systems;2022-02

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