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Fog Computing Architecture for 5G-Compliant IoT Applications in Underserved Communities

机译:雾化计算架构,在不可提供的社区中的5G标准的IOT应用程序

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The fifth generation (5G) of wireless network ecosystem which is expected to be deployed in 2020 promises to provide higher speeds, higher capacity and lower latency than the current mobile networks. 5G is also envisioned to revolutionise the telecoms industry in unprecedented ways by enabling new applications and services that could change the way we live and do things. However, in areas with poor or lack of infrastructure, the deployment of 5G is a challenge for mobile operators because of the potential low return on investment. One of the promising technologies for low cost computing networks is fog computing, which selectively moves resources and services of computing, storage, control and networking at the edge of the network closer to the users, thereby improving the speed of decision-making, network cost, and the performance of the system. In this paper, we aim to investigate how fog computing can enable cost-efficient solutions in underserved areas to compensate for the economic drawback of low Average Revenue Per User (ARPU), and at the same time provide good quality service for users that meets essential performance requirements. We propose a fog-based architecture that exploits local computation, storage, communication, control, and decision making as a means to reduce communication costs and energy consumption and thus overcome the cost issue in 5G deployment. Preliminary simulation results show that the architecture performs better than cloud-only deployments in terms of energy consumption and delay in a smart agriculture system.
机译:预计将于2020年部署的无线网络生态系统的第五代(5G)承诺提供比当前移动网络更高的速度,更高容量和更低的延迟。 5G也设想通过启用可能改变我们的生活方式和做事的方式来彻底改变电信行业以前所未有的方式。然而,在基础设施差或缺乏基础设施的地区,由于潜在的投资回报率,部署5G是移动运营商的挑战。低成本计算网络的有希望技术之一是雾计算,其选择性地移动了在网络边缘的计算,存储,控制和网络的资源和服务,从而提高了决策,网络成本的速度以及系统的性能。在本文中,我们的目的是调查雾化计算如何在所提供的地区可以实现高效的解决方案,以补偿每个用户(ARPU)的低平均收入的经济缺点,同时为满足必要的用户提供优质的服务性能要求。我们提出了一种基于FOG的架构,用于利用本地计算,存储,通信,控制和决策,作为降低通信成本和能耗的方法,从而克服5G部署中的成本问题。初步仿真结果表明,该架构在智能农业系统中的能耗和延迟方面表现优于唯一的云部署。

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