Title: FastIoT: an efficient and very fast compression model for displaying a huge volume of IoT data in web environments

Authors: Mateus Begnini Melchiades; César David Paredes Crovato; Everton Nedel; Lincoln Vinicius Schreiber; Rodigo Da Rosa Righi

Addresses: Unisinos University, São Leopoldo, Rio Grande do Sul, Brazil ' Unisinos University, São Leopoldo, Rio Grande do Sul, Brazil ' Unisinos University, São Leopoldo, Rio Grande do Sul, Brazil ' Unisinos University, São Leopoldo, Rio Grande do Sul, Brazil ' Unisinos University, São Leopoldo, Rio Grande do Sul, Brazil

Abstract: Industry 4.0 concept leads to a generation of massive data sets that supervisors must appropriately analyse for an effective decision-making process. A data set, however, can be excessively large, causing troubles when trying to visualise its content entirety. Thus, the present work introduces FastIoT as a novel compression model that focuses on the visual representation of Industry 4.0 data through web environments. As a client-server proposal, FastIoT brings the idea of: (i) speed in data preparation at the server-side, since the proposed method is very simple, and; (ii) efficiency, because we consider the target client plotting area so generating an optimised data set fitted especially for such visual region. Even adding a short time at the server-side for data preparation, with FastIoT we have data ready in the client's display up to 97% faster when compared to traditional plotting methods.

Keywords: Industry 4.0; IoT; Internet of Things; compression; data visualisation; web environment.

DOI: 10.1504/IJGUC.2021.120096

International Journal of Grid and Utility Computing, 2021 Vol.12 No.5/6, pp.605 - 617

Received: 19 Jun 2020
Accepted: 29 Oct 2020
Published online: 07 Jan 2022 *

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