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Vehicular fog gateways selection on the internet of vehicles: A fuzzy logic with ant colony optimization based approach

机译:车辆互联网上的车辆雾网关选择:基于蚁群优化的方法模糊逻辑

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Fog computing (FC) and multi-access edge computing (MEC) are two promising technologies that have been emerged to solve problems related to the access to the cloud computing (CC), mainly high latency and high bandwidth consumption. These two paradigms consist in enabling the cloud closer to users at the edge of the network. The pool of vehicular resources provided by the vehicular cloud (VC) can be exploited to process and store end users data instead of accessing remote servers. The combination of these three concepts can considerably augment the edge resources. In this context, we propose a multi-access edge based vehicular fog computing architecture on the internet of vehicles where vehicles are the fog nodes. In this paper, we present a detailed description of our suggested architecture and its modules. Then, we focus on a particular module which is the gateways selection module. The role of this module is the election of suitable fog nodes (i.e. vehicles) to access the MEC servers and the conventional cloud in order to reduce communication costs (e.g. bandwidth use, delay). The proposed selection approach has two steps. The first step consists in selecting a set of candidate gateways based on fuzzy logic. The second step allows the optimization of the number of selected gateways. We formulate it as a multi objective optimization problem, and we solve it using ant colony optimization. The obtained simulation results show the efficiency of our proposed approach in terms of the number of selected gateways and connected fog nodes. In both static and mobile scenarios, the number of selected gateways is reduced up to 82% and 92%, respectively, compared to the fuzzy step. The ratio of connected vehicles is more than 94% in the static scenario. (C) 2019 Elsevier B.V. All rights reserved.
机译:雾计算(FC)和多访问边缘计算(MEC)是两个有希望的技术,以解决与对云计算(CC)的访问相关的问题,主要是高延迟和高带宽消耗。这两个范式包括使云更接近网络边缘的用户。由车云(VC)提供的车辆资源池可以利用来处理和存储最终用户数据而不是访问远程服务器。这三个概念的组合可以相当长地增加边缘资源。在这种情况下,我们提出了一种基于车辆的车辆互联网上基于远程的车辆雾计算架构,其中车辆是雾节点。在本文中,我们提供了我们建议的架构及其模块的详细描述。然后,我们专注于特定模块,该模块是网关选择模块。该模块的作用是选择合适的雾节点(即车辆)来访问MEC服务器和传统云以降低通信成本(例如,带宽使用,延迟)。建议的选择方法有两个步骤。第一步包括基于模糊逻辑选择一组候选网关。第二步允许优化所选网关的数量。我们将其作为一种多目标优化问题,我们使用蚁群优化来解决它。所获得的仿真结果表明,在所选网关的数量和连接的雾节点方面,我们提出了我们所提出的方法的效率。在静态和移动方案中,与模糊步骤相比,所选网关的数量分别降低至82%和92%。在静态场景中,连接的车辆的比例超过94%。 (c)2019 Elsevier B.v.保留所有权利。

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