With innovation in persistent technologies, such as wearable sensor gadgets, sensor devices, and wireless ad-hoc communication networks connect everyday life things to the Internet, normally referred to as Internet of Things (IoT). IoT is observed as an active entity for design and development of smart and context awareness services and applications in the area of business, science and engineering discipline. These applications and services could vigorously respond to the surroundings transformation and users’ preference. Developing a scalable system for data analysis, processing and mining of enormous real world based datasets has turned into one of the demanding problems that faces both system research scholars and data management research scholars. Employing big data analytics with IoT technologies is one of the ways for handling the timely analyzing information (i.e., data, events) streams. In this paper, we propose an integrated approach that coalesce IoT systems with big data tools into a holistic platform for real-time and continuous data monitoring and processing. We propose Fog assisted IoT based Smart and real time healthcare information processing (SRHIP) system in which large amounts of data generated by IoT sensor devices are offloaded at Fog cloud form data analytics and processing with minimum delay. The processed data is then transferred to a centralized cloud system for further analysis and storage. In this work, we introduce a Fog-assisted model with big data environment for data analytic of real time data with remote monitoring and discuss our plan for evaluating its efficacy in terms of several performance metrics such as transmission cost, storage cost, accuracy, specificity, sensitivity and F-measure. The proposed SRHIP system needs less transmission cost of 40.10% in comparison to SPPDA, 100% fewer bytes are compromised in comparison to GCEDA. Our proposed system data size reduction of 60% reduction due to proposed compression scheme in comparison to other benchmark strategies that offer 40% of reduction.

随着可持久技术的创新，例如可穿戴式传感器，传感器设备和无线ad-hoc通信网络，人们将日常生活的物品连接到了Internet（通常称为物联网（IoT））。物联网被视为在商业，科学和工程学科领域设计和开发智能和情境感知服务及应用程序的活跃实体。这些应用程序和服务可以强烈响应周围环境的变化和用户的喜好。开发可扩展的系统以用于基于现实世界的巨大数据集的数据分析，处理和挖掘已经变成系统研究学者和数据管理研究学者都面临的严峻问题之一。利用物联网技术进行大数据分析是处理及时分析信息（即数据，事件）流的方法之一。在本文中，我们提出了一种集成方法，将物联网系统与大数据工具整合为一个实时和连续数据监控和处理的整体平台。我们提出了基于Fog辅助的基于IoT的智能实时医疗信息处理（SRHIP）系统，其中以最小的延迟在Fog云上卸载了由IoT传感器设备生成的大量数据，从而进行了数据分析和处理。然后将处理后的数据传输到集中式云系统，以进行进一步的分析和存储。在这项工作中 我们介绍了一种具有大数据环境的Fog辅助模型，用于通过远程监控对实时数据进行数据分析，并讨论了我们的计划，以评估其有效性，如传输成本，存储成本，准确性，特异性，敏感性和F -测量。与SPPDA相比，拟议的SRHIP系统所需的传输成本更低，为40.10％，与GCEDA相比，所损失的字节减少了100％。我们提出的系统数据大小减少了60％，这是由于提出了压缩方案，而其他基准策略却减少了40％。与GCEDA相比，字节损失减少了100％。我们提出的系统数据大小减少了60％，这是由于提出了压缩方案，而其他基准策略却减少了40％。与GCEDA相比，字节损失减少了100％。我们提出的系统数据大小减少了60％，这是由于提出了压缩方案，而其他基准策略却减少了40％。