Next Article in Journal
Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis
Next Article in Special Issue
Introduction to the Special Issue on “State-of-the-Art Sensor Technology in Japan 2015”
Previous Article in Journal
A Fiber Bragg Grating Sensing-Based Micro-Vibration Sensor and Its Application
Previous Article in Special Issue
Quantitative Evaluation System of Soft Neurological Signs for Children with Attention Deficit Hyperactivity Disorder
Open AccessArticle

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

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.
Academic Editor: Leonhard M. Reindl
Sensors 2016, 16(4), 548; https://doi.org/10.3390/s16040548
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)
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. View Full-Text
Keywords: sensor networks; dynamic relational networks; spatiotemporal interpolation; self-recognizing networks; profiling sensor networks; dynamic relational networks; spatiotemporal interpolation; self-recognizing networks; profiling
Show Figures

Figure 1

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; Hasegawa, Keisuke; Ishida, Yoshiteru. 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

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop