Information processing in Internet of Things using big data analytics

Abstract

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.