Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites

Tokumitsu, Masahiro; Hasegawa, Keisuke; Ishida, Yoshiteru

doi:10.3390/s16040548

Open AccessArticle

Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites

by

Masahiro Tokumitsu

^1,*,

Keisuke Hasegawa

¹ and

Yoshiteru Ishida

²

¹

Department of Electrical and Control Engineering, National Institute of Technology, Yonago College, Hikonacho 4448, Yonago 683-0854, Japan

²

Department of Computer Science and Engineering, Toyohashi University of Technology, Hibarigaoka 1-1, Toyohashi 441-8580, Japan

^*

Author to whom correspondence should be addressed.

Sensors 2016, 16(4), 548; https://doi.org/10.3390/s16040548

Submission received: 31 December 2015 / Revised: 22 March 2016 / Accepted: 1 April 2016 / Published: 15 April 2016

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Japan 2015)

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Abstract

This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons.

Keywords: sensor networks; dynamic relational networks; spatiotemporal interpolation; self-recognizing networks; profiling

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MDPI and ACS Style

Tokumitsu, M.; Hasegawa, K.; Ishida, Y. Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites. Sensors 2016, 16, 548. https://doi.org/10.3390/s16040548

AMA Style

Tokumitsu M, Hasegawa K, Ishida Y. Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites. Sensors. 2016; 16(4):548. https://doi.org/10.3390/s16040548

Chicago/Turabian Style

Tokumitsu, Masahiro, Keisuke Hasegawa, and Yoshiteru Ishida. 2016. "Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites" Sensors 16, no. 4: 548. https://doi.org/10.3390/s16040548

APA Style

Tokumitsu, M., Hasegawa, K., & Ishida, Y. (2016). Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites. Sensors, 16(4), 548. https://doi.org/10.3390/s16040548

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites

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