IoT 18(16): e4

Research Article

A Framework for IoT Sensor Data Acquisition and Analysis

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  • @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
Sivadi Balakrishna1,*, M. Thirumaran1, Vijender Kumar Solanki2
  • 1: Department of Computer Science and Engineering, Pondicherry Engineering College, Pondicherry, India
  • 2: Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, India
*Contact email: balakrishna.sivadi@pec.edu

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.