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Deep learning–based real-time query processing for wireless sensor network

Author

Listed:
  • Ki-Seong Lee
  • Sun-Ro Lee
  • Youngmin Kim
  • Chan-Gun Lee

Abstract

The data collected from wireless sensor network indicate the system status, the environment status, or the health condition of human being, and we can use the wireless sensor network data to carry out appropriate work by processing it. In recent years, using deep learning, it is possible to construct a more intelligent context-aware system by predicting future situations as well as monitoring the current state. In this article, we propose a monitoring framework for wireless sensor network streaming data analysis based on deep learning. In particular, in an environment where time requirements are strictly enforced, data analysis results must be derived within a deterministic time. Therefore, we conduct query refinement adaptively to enable timely analysis of wireless sensor network data in the predictor. Even if some sensor data that is not synchronized in time are included or even if some data have not arrived yet, reasonably accurate query analysis results can be obtained within the deadline by performing the proposed method.

Suggested Citation

  • Ki-Seong Lee & Sun-Ro Lee & Youngmin Kim & Chan-Gun Lee, 2017. "Deep learning–based real-time query processing for wireless sensor network," International Journal of Distributed Sensor Networks, , vol. 13(5), pages 15501477177, May.
    • Handle: RePEc:sae:intdis:v:13:y:2017:i:5:p:1550147717707896
    DOI: 10.1177/1550147717707896
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