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Distributed Data Mining Based on Deep Neural Network for Wireless Sensor Network

Author

Listed:
  • Chunlin Li
  • Xiaofu Xie
  • Yuejiang Huang
  • Hong Wang
  • Changxi Niu

Abstract

As the sample data of wireless sensor network (WSN) has increased rapidly with more and more sensors, a centralized data mining solution in a fusion center has encountered the challenges of reducing the fusion center's calculating load and saving the WSN's transmitting power consumption. Rising to these challenges, this paper proposes a distributed data mining method based on deep neural network (DNN), by dividing the deep neural network into different layers and putting them into sensors. By the proposed solution, the distributed data mining calculating units in WSN share much of fusion center's calculating burden. And the power consumption of transmitting the data processed by DNN is much less than transmitting the raw data. Also, a fault detection scenario is built to verify the validity of this method. Results show that the detection rate is 99%, and WSN shares 64.06% of the data mining calculating task with 58.31% reduction of power consumption.

Suggested Citation

  • Chunlin Li & Xiaofu Xie & Yuejiang Huang & Hong Wang & Changxi Niu, 2015. "Distributed Data Mining Based on Deep Neural Network for Wireless Sensor Network," International Journal of Distributed Sensor Networks, , vol. 11(7), pages 157453-1574, July.
    • Handle: RePEc:sae:intdis:v:11:y:2015:i:7:p:157453
    DOI: 10.1155/2015/157453
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