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 the technical, non-functional and economic challenges, which have been posing hurdles in adopting fog computing, by consolidating them across different clusters. The paper also summarizes the relevant academic and industrial contributions in addressing these challenges and provides future research directions in realizing real-time fog computing applications, also considering the emerging trends such as federated learning and quantum computing.

翻译：物联网（IoT）及实时应用的最新发展，导致了连接设备及其生成数据的空前增长。传统上，作为物联网应用的一部分，这些传感器数据被传输至云端处理，控制信号则被发送回相应的执行器。这种以云为中心的物联网模型导致了延迟增加、网络负载加重及隐私泄露问题。为解决这些问题，思科于十年前（2012年）提出了雾计算概念，该概念利用近端计算资源处理传感器数据。自提出以来，雾计算吸引了广泛关注，研究界聚焦于解决不同挑战，例如雾计算框架、模拟器、资源管理、部署策略、服务质量方面、雾计算经济学等。然而，经过十年研究，我们仍未看到能够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和小规模测试平台，以及利用模拟器展示各自技术挑战解决模型规模化的研究。造成这一现象的原因有多种，其中最重要的是雾计算尚未为企业及参与个人提供清晰的商业模式。本文通过跨不同集群整合相关挑战，总结了阻碍雾计算采用的技术性、非功能性和经济性挑战。本文还梳理了应对这些挑战的相关学术与工业贡献，并提出了实现实时雾计算应用的未来研究方向，同时考虑了联邦学习与量子计算等新兴趋势。