Research Article
A Framework for IoT Sensor Data Acquisition and Analysis
@ARTICLE{10.4108/eai.21-12-2018.159410, author={Sivadi Balakrishna and M. Thirumaran and Vijender Kumar Solanki}, title={A Framework for IoT Sensor Data Acquisition and Analysis}, journal={EAI Endorsed Transactions on Internet of Things}, volume={4}, number={16}, publisher={EAI}, journal_a={IOT}, year={2018}, month={10}, keywords={Internet of Things (IoT), sensor data, data acquisition, FSDAA, ThingSpeak}, doi={10.4108/eai.21-12-2018.159410} }- Sivadi Balakrishna
M. Thirumaran
Vijender Kumar Solanki
Year: 2018
A Framework for IoT Sensor Data Acquisition and Analysis
IOT
EAI
DOI: 10.4108/eai.21-12-2018.159410
Abstract
In the current scenario, around 35 billion Internet of Things (IoT) devices is connected to the internet. By 2025, it is predicted that the number will grow between 80 and 120 billion devices connected to the internet, supporting to generate 180 trillion gigabytes of new sensor data that year. The IoT sensor data is generated from various heterogeneous devices, communication protocols, and data formats that are enormous in nature. This huge amount of sensor data is unable to acquire and analyze manually. This is a significant problem for IoT application developers to make the integration of IoT sensor data automatically. However, the large amount of data has led to the inadequacy of the manual data acquisition and stressed the urgency into the research of IoT based frameworks in automatic. In this paper, we have proposed a framework for IoT sensor data acquisition and analysis (FSDAA). The FSDAA has been implemented on the ThingSpeak IoT Cloud platform for data analysis and visualizations, and compared with the state of the art schemes. Finally, the results show that the proposed FSDAA is efficient in terms of Accuracy, Precision, Recall, and F-measure.
Copyright © 2018 Sivadi Balakrishna et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
