A Cloud-based Analytics Architecture for the Application of Online Machine Learning Algorithms on Data Streams in Consumer-centric Internet of Things Domains

Zschörnig, Theo; Windolph, Jonah; Wehlitz, Robert; Franczyk, Bogdan

doi:10.5220/0009339501890196

Proceedings of the 5th International Conference on Internet of Things, Big Data and Security 2020

DOI: 10.5220/0009339501890196

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A Cloud-based Analytics Architecture for the Application of Online Machine Learning Algorithms on Data Streams in Consumer-centric Internet of Things Domains

Theo Zschörnig¹,

Jonah Windolph²,

Robert Wehlitz³

et al.

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2022

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A Fog-Based Multi-Purpose Internet of Things Analytics Platform

Zschörnig

Windolph²,

Wehlitz³

et al. 2022

SN COMPUT. SCI.

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Data analytics is an important component for the benefit and growth of the Internet of Things (IoT). The utilization of data generated by a variety of heterogeneous smart devices offers the possibility of gaining meaningful insights into various aspects of the daily lives of end consumers, the environment and weather, but also into value-added processes of business and industry. The potential benefits derived from analyzing IoT data can be further enhanced by advancing developments in streaming and machine learning technologies. A critical factor in the application of these technologies are the underlying analytics architectures. These must overcome a variety of different challenges that are influenced by technical, but also legal or personal constraints and differ in importance and impact depending on the IoT application domain in which such an architecture is to be deployed. Solutions presented by previous research address only a handful of these challenges. An important capability to address the variety of challenges that arise from this situation is the ability to support the hybrid deployment of analytics pipelines at different network layers. Consequently, in this work, we propose an architectural solution that enables hybrid analytics pipeline deployments, addresses the challenges described in previous scientific literature and can be deployed in various IoT application domains. Finally, we experimentally evaluate the proposed solution.

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A Fog-Based Multi-Purpose Internet of Things Analytics Platform

Zschörnig

Windolph²,

Wehlitz³

et al. 2022

SN COMPUT. SCI.

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Feature Selection Using Elephant Herd Principal Component Optimization Technique in Big Data Streams Using Internet of Things

Manikandan

2022

International Journal of E-Collaboration

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In this digital world, large volume of data is transmitted across various sectors like production industry, healthcare, IoT devices, sales, and other organizations. In this paper, an Elephant Herd Principal Component Optimization (EHPCO) technique is used as a feature selection model to analyse the features of the data that are collected from the IoT devices. The improved perturbation technique is used the privacy preserving of data streams from the IoT devices. The machine learning classifiers are used to analyse its performance based on the proposed feature selection technique. Experimental results show that the proposed HPCO technique outperforms to improve the performance of the machine learning classifiers in terms of TPR, FPR, and accuracy. The DBN classifier obtains more than 86% of accuracy when compared with other algorithms like SVM, MLP, DT, and RF. When the certain features are extracted using the proposed EHPCO technique, the performance of the classifier is improved much in terms of accuracy. The analysis is made for four datasets such as, HPMD, FRDD, EZSD, and SSTD.

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A Cloud-based Analytics Architecture for the Application of Online Machine Learning Algorithms on Data Streams in Consumer-centric Internet of Things Domains

Cited by 2 publications

References 0 publications

A Fog-Based Multi-Purpose Internet of Things Analytics Platform

A Fog-Based Multi-Purpose Internet of Things Analytics Platform

Feature Selection Using Elephant Herd Principal Component Optimization Technique in Big Data Streams Using Internet of Things

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