Internet of Things (IoT) emerged as one of the leading technological advancements of our days. IoT generates enormous quantities of valuable data that need on time processing, resulting in reliable, and accurate decisions based on the Internet of Things vision. The quality of the generated data is inadequate, incomplete, uncertain, and produced from multiple sources. Although cloud servers can analyze and store enormous data, they need a lot of time to send full-size data for storage and analysis as well as the high overhead they have that not satisfactory in many applications. This article provides a systematic way to review the IoT environment according to big data analytics together with limitations and challenges. Moreover, a cloud-fog-mist combination for handling IoT data concerning centralized and distributed data mining is explained. A proposed hybrid real-time remote patient monitoring framework introduced that consists of the integration among the mist, fog, and cloud for healthcare treatment, which remote-monitors patients continuously. In addition, Reduced-Error Pruning tree (REPtree), MultiLayer Perceptron, naïve Bayes, and Sequential Minimal Optimization algorithms have applied to “Gas sensors for home activity monitoring” dataset to demonstrate the feasibility of traditional data mining algorithms to IoT data. The results showed that the REPtree algorithm achieved better accuracy against others with accuracy ranged between 90.66% and 93.6% according to the size of the data used in the study. Still, for the time metric, naive Bayes outperformed them with the lowest time between 1 and 18 seconds for building the model.

中文翻译：

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在物联网环境中利用雾和雾进行大数据分析

物联网 (IoT) 成为当今领先的技术进步之一。物联网会生成大量需要及时处理的有价值数据，从而基于物联网愿景做出可靠、准确的决策。生成的数据质量不充分、不完整、不确定，并且来自多个来源。云服务器虽然可以分析和存储海量数据，但需要大量时间发送全尺寸数据进行存储和分析，而且开销大，在很多应用中都不尽如人意。本文提供了一种根据大数据分析以及局限性和挑战来审查 IoT 环境的系统方法。此外，还解释了用于处理涉及集中式和分布式数据挖掘的物联网数据的云-雾-雾组合。提出了一种提议的混合实时远程患者监控框架，该框架由雾、雾和云之间的集成组成，用于医疗保健，可连续远程监控患者。此外，Reduced-Error Pruning tree (REPtree)、MultiLayer Perceptron、朴素贝叶斯和顺序最小优化算法已应用于“用于家庭活动监测的气体传感器”数据集，以证明传统数据挖掘算法对物联网数据的可行性。结果表明，根据研究中使用的数据大小，REPtree 算法相对于其他算法取得了更好的准确率，准确率在 90.66% 到 93.6% 之间。尽管如此，就时间指标而言，朴素贝叶斯以 1 到 18 秒之间的最短时间构建模型优于他们。