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Data Imputation in Wireless Sensor Network Using Deep Learning Techniques

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Data Analytics and Management

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 54))

Abstract

Missing information in the sequence of data provided by the wireless sensor network is a prevalent issue. There can be many reasons which may be responsible for this like network loss, sensor maintenance, and sensor failure. Retrieving the missing data from the time series data obtained by the wireless sensor network proves to be a difficult task. There have been many methods which try to recover this data, but limitation still exists. The proposed work discusses the use of deep learning algorithms for the time series prediction of WSN data generated from real-time sensor devices. This paper examines various techniques available in deep learning for time series forecasting and analyzes the results obtained from various methods and at the end, gives the best hybrid combination of which contains the bidirectional LSTM layer for a deep examination of the pattern in the data. The main motivation behind the work is to improve the working environment of the WSN fields, automate various processess of maintenance, and provide an effective method of data imputation in the wireless sensor network in a real-time environment when there is a scarcity of data and also, develop a method for effective forecasting. Another approach that is examined is a CNN layer to find the positional pattern in the data, attention mechanism to put more focus on the relevant part of the sequence, an LSTM layer which makes it an encoder–decoder model, and a dense layer at the end to produce the output in the desired shape. The model is trained and tested on Beijing Air Pollution PM2.5 Dataset. To overcome the problem of lower availability of data, VLSW algorithm is used, which helped in generating the large sample of training data from the limited available datasets. After analyzing and studying the various models and their results with various hyperparameters tried, it is concluded that the model with the LSTM attention mechanism with the encoder–decoder model along with VLSW works best for long missing time series data imputation. Other studied models and their results are summarized too. This work concludes based on the practical and real-time application of data imputation that the CNN model with online training works best as it takes less time and resources to train. The results obtained are better from the existing solution available. The SSIM model with VLSW performed 31% more efficient than other methods, while CNN without VLSW performed 76% more efficient than other methods.

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Authors and Affiliations

  1. Gautam Buddha University, Greater Noida, India

    Shweta Rani & Arun Solanki

Authors
  1. Shweta Rani
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  2. Arun Solanki
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Corresponding author

Correspondence to Arun Solanki .

Editor information

Editors and Affiliations

  1. Maharaja Agrasen Institute of Technology, New Delhi, India

    Ashish Khanna

  2. Maharaja Agrasen Institute of Technology, New Delhi, India

    Deepak Gupta

  3. Jan Wyzykowski University, Polkowice, Poland

    Zdzisław Pólkowski

  4. CHRIST (Deemed to be University), Bengaluru, India

    Siddhartha Bhattacharyya

  5. Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

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Rani, S., Solanki, A. (2021). Data Imputation in Wireless Sensor Network Using Deep Learning Techniques. In: Khanna, A., Gupta, D., Pólkowski, Z., Bhattacharyya, S., Castillo, O. (eds) Data Analytics and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 54. Springer, Singapore. https://doi.org/10.1007/978-981-15-8335-3_44

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