基于模糊的sdn - vanet边缘层处理能力评估方法:试验台与仿真系统结果对比研究

Ermioni Qafzezi, Kevin Bylykbashi, Shunya Higashi, Phudit Ampririt, Keita Matsuo, L. Barolli
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引用次数: 1

摘要

车辆自组织网络(VANETs)因其通过车对车和车对基础设施的通信提高道路安全、交通效率和乘客舒适度的潜力而受到广泛关注。然而,由于车辆环境的动态性和资源约束性,VANETs面临着资源管理方面的挑战。将云雾边缘计算和软件定义网络(SDN)与VANETs集成,可以利用不同层的计算能力和可用资源,有效地处理和管理车辆数据。在这项工作中,我们使用这一范式,并提出了一种基于模糊逻辑(FL)的智能方法来评估车辆的处理和存储能力,以帮助需要额外资源的其他车辆。通过大量的仿真和试验台对所提出系统的有效性进行了评估。仿真结果与试验台之间的性能分析使人们对所提出的系统及其性能和可行性有了全面的了解。
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A Fuzzy-Based Approach for the Assessment of the Edge Layer Processing Capability in SDN-VANETs: A Comparation Study of Testbed and Simulation System Results
Vehicular Ad Hoc Networks (VANETs) have gained significant attention due to their potential to enhance road safety, traffic efficiency, and passenger comfort through vehicle-to-vehicle and vehicle-to-infrastructure communication. However, VANETs face resource management challenges due to the dynamic and resource constrained nature of vehicular environments. Integrating cloud-fog-edge computing and Software-Defined Networking (SDN) with VANETs can harness the computational capabilities and resources available at different tiers to efficiently process and manage vehicular data. In this work, we used this paradigm and proposed an intelligent approach based on Fuzzy Logic (FL) to evaluate the processing and storage capability of vehicles for helping other vehicles in need of additional resources. The effectiveness of the proposed system is evaluated through extensive simulations and a testbed. Performance analysis between the simulation results and the testbed offers a comprehensive understanding of the proposed system and its performance and feasibility.
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