Recent developments in the Internet of Things (IoT) and real-time applications, have led to the unprecedented growth in the connected devices and their generated data. Traditionally, this sensor data is transferred and processed at the cloud, and the control signals are sent back to the relevant actuators, as part of the IoT applications. This cloud-centric IoT model, resulted in increased latencies and network load, and compromised privacy. To address these problems, Fog Computing was coined by Cisco in 2012, a decade ago, which utilizes proximal computational resources for processing the sensor data. Ever since its proposal, fog computing has attracted significant attention and the research fraternity focused at addressing different challenges such as fog frameworks, simulators, resource management, placement strategies, quality of service aspects, fog economics etc. However, after a decade of research, we still do not see large-scale deployments of public/private fog networks, which can be utilized in realizing interesting IoT applications. In the literature, we only see pilot case studies and small-scale testbeds, and utilization of simulators for demonstrating scale of the specified models addressing the respective technical challenges. There are several reasons for this, and most importantly, fog computing did not present a clear business case for the companies and participating individuals yet. This paper summarizes challenges, state-of-the-art and future research directions in realizing real-time fog computing applications. Contrary to other survey papers, that exhaustively address a specific set of aspects of fog computing, this work discusses the fog research challenges and solutions in much broader scope and thus provides a thorough opinion about progressing the research and quickly adapting fog computing in real-world applications.

翻译：物联网（IoT）及实时应用的最新发展，推动联网设备及其生成数据呈现前所未有的增长。传统上，作为物联网应用的一部分，传感器数据被传输至云端进行处理，控制信号则回传至相应执行器。这种以云为中心的物联网模型导致了延迟增加、网络负载加重并引发隐私问题。为解决这些问题，思科公司于十年前（2012年）提出了雾计算概念，利用邻近计算资源处理传感器数据。自提出以来，雾计算吸引了大量关注，研究界聚焦于解决不同挑战，如雾框架、模拟器、资源管理、部署策略、服务质量保障、雾经济学等。然而，经过十年研究，我们仍未看到可用于实现有趣物联网应用的公共/私有雾网络的大规模部署。文献中仅见试点案例研究、小规模测试平台，以及利用模拟器展示解决相应技术问题的特定模型规模。这背后有多种原因，最重要的是雾计算尚未为企业及参与个体提供明确的商业案例。本文总结了实现实时雾计算应用中的挑战、研究现状及未来方向。与其他详尽讨论雾计算特定方面问题的综述论文不同，本工作从更广泛视角探讨雾计算研究挑战与解决方案，为推进研究及快速在现实应用中部署雾计算提供了深入见解。