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A novel data fusion scheme using grey model and extreme learning machine in wireless sensor networks

  • Special Section on Advanced Control Theory and Techniques based on Data Fusion
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Abstract

With the increasing presence and adoption of wireless sensor networks (WSNs), the demand of data acquisition and data fusion are becoming stronger and stronger. In WSN, sensor nodes periodically sense data and send them to the sink node. Since the network consists of plenty of low-cost sensor nodes with limited battery power and the sensed data usually are of high temporal redundancy, prediction- based data fusion has been put forward as an important issue to reduce the number of transmissions and save the energy of the sensor nodes. Considering the fact that the sensor node usually has limited capabilities of data processing and storage, a novel prediction-based data fusion scheme using grey model (GM) and optimally pruned extreme learning machine (OP-ELM) is proposed. The proposed data fusion scheme called GM-OP-ELM uses a dual prediction mechanism to keep the prediction data series at the sink node and sensor node synchronous. During the data fusion process, GM is introduced to initially predict the data of next period with a small number of data items, and an OPELM- based single-hidden layer feedforward network (SLFN) is used to make the initial predicted value approximate its true value with extremely fast speed. As a robust and fast neural network learning algorithm, OP-ELM can adaptively adjust the structure of the SLFN. Then, GM-OP-ELM can provide high prediction accuracy, low communication overhead, and good scalability. We evaluate the performance of GM-OP-ELM on three actual data sets that collected from 54 sensors deployed in the Intel Berkeley Research lab. Simulation results have shown that the proposed data fusion scheme can significantly reduce redundant transmissions and extend the lifetime of the whole network with low computational cost.

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Authors and Affiliations

  1. School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing, 100083, China

    Xiong Luo & Xiaohui Chang

  2. Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China

    Xiong Luo & Xiaohui Chang

Authors
  1. Xiong Luo
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  2. Xiaohui Chang
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Corresponding author

Correspondence to Xiong Luo.

Additional information

Xiong Luo received his Ph.D. degree from Central South University, Changsha, China, in 2004. From 2005 to 2006, he was with the Department of Computer Science and Technology, Tsinghua University, China, as a Postdoctoral Fellow. From 2012 to 2013, he was with the School of Electrical, Computer and Energy Engineering, Arizona State University, USA, as a Visiting Scholar. He currently works as an Associate Professor in the School of Computer and Communication Engineering, University of Science and Technology Beijing, China. His research interests include computational intelligence, intelligent control, and machine learning.

Xiaohui Chang is currently working toward a Master’s degree at the School of Computer and Communication Engineering, University of Science and Technology Beijing, China. Her research interests include wireless sensor network analysis and computational intelligence.

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Luo, X., Chang, X. A novel data fusion scheme using grey model and extreme learning machine in wireless sensor networks. Int. J. Control Autom. Syst. 13, 539–546 (2015). https://doi.org/10.1007/s12555-014-0309-8

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  • DOI: https://doi.org/10.1007/s12555-014-0309-8

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