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Efficient data aggregation with node clustering and extreme learning machine for WSN

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Abstract

Wireless sensor network is effective for data aggregation and transmission in IoT environment. Here, the sensor data often contain a significant amount of noises or redundancy exists, and thus, the data are aggregated to extract meaningful information and reduce the transmission cost. In this paper, a novel data aggregation scheme is proposed based on clustering of the nodes and extreme learning machine (ELM) which efficiently reduces redundant and erroneous data. Mahalanobis distance-based radial basis function is applied to the projection stage of the ELM to reduce the instability of the training process. Kalman filter is also used to filter the data at each sensor node before transmitted to the cluster head. Computer simulation with real datasets shows that the proposed scheme consistently outperforms the existing schemes in terms of clustering accuracy of the data and energy efficiency of WSN.

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Acknowledgements

This work was partly supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2016-0-00133, Research on Edge computing via collective intelligence of hyperconnection IoT nodes), Korea, under the National Program for Excellence in SW supervised by the IITP(Institute for Information & communications Technology Promotion)(2015-0-00914), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2017R1A2B2009095, Research on SDN-based WSN Supporting Real-time Stream Data Processing and Multiconnectivity, 2019R1I1A1A01058780, Efficient Management of SDN-based Wireless Sensor Network Using Machine Learning Technique), the second Brain Korea 21 PLUS project.

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Authors and Affiliations

  1. Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea

    Ihsan Ullah

  2. College of Software, Sungkyunkwan University, Suwon, Korea

    Hee Yong Youn

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  1. Ihsan Ullah
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  2. Hee Yong Youn
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Correspondence to Hee Yong Youn.

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Ullah, I., Youn, H.Y. Efficient data aggregation with node clustering and extreme learning machine for WSN. J Supercomput 76, 10009–10035 (2020). https://doi.org/10.1007/s11227-020-03236-8

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