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A Framework for Data Prediction and Forecasting in WSN with Auto ARIMA

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Abstract

Data collection is the key task of wireless sensing nodes. Efficient data collection is very important for precise decision making. The decision taken can be very crucial in the context of environment, human health, military applications, etc. With the advances in technology, the sensing nodes have become more reliable. But still, there is a possibility that the data might get missed or corrupted. This may be due to various reasons like node failure, communication failure or may be due to a regular maintenance schedule. Missing data may lead to inappropriate decision making which may be fatal and thus might lead to harsh consequences. Thus, there arises a need to handle the missed data efficiently. Here in this paper, a framework for data prediction and forecasting is proposed using Auto ARIMA in R. The framework is implemented first by imputing missing data and then by without imputing missing data. It is observed that the proposed model with imputation of missing data demonstrates a better fit than without imputation of missing data. Hence, proper imputation leads to better forecasting which is important for taking appropriate decisions in various applications. In addition, the proposed framework has also been compared with three state-of-art models, and the results demonstrate that the proposed model exhibits a better fit.

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Data Availability

The dataset named “Air Quality Data Set” analysed during the current study is available in the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/Air+quality.

Code Availability

Auto ARIMA in R is used to conduct this study.

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Funding

This research received no external funding support.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Graphic Era Deemed to be University, 566/6 Bell Road, Clement Town, Dehradun, 248002, India

    Ankur Choudhary, Santosh Kumar & Manish Sharma

  2. Dr. B.R. Ambedkar, National Institute of Technology, Jalandhar, India

    K. P. Sharma

Authors
  1. Ankur Choudhary
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  2. Santosh Kumar
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  3. Manish Sharma
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  4. K. P. Sharma
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Contributions

Conceptualization: Ankur Choudhary and Santosh Kumar; Formal analysis: Ankur Choudhary; Investigation: Ankur Choudhary and Manish Sharma; Methodology: Ankur Choudhary and Santosh Kumar; Project administration: Santosh Kumar; Supervision: Santosh Kumar; Validation: Ankur Choudhary, Manish Sharma and KP Sharma; Visualization: Ankur Choudhary; Writing-original draft: Ankur Choudhary; Writing-review & editing: Santosh Kumar and KP Sharma.

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Correspondence to Santosh Kumar.

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Choudhary, A., Kumar, S., Sharma, M. et al. A Framework for Data Prediction and Forecasting in WSN with Auto ARIMA. Wireless Pers Commun 123, 2245–2259 (2022). https://doi.org/10.1007/s11277-021-09237-x

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  • DOI: https://doi.org/10.1007/s11277-021-09237-x

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