Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites.
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.
Department of Electrical and Control Engineering, National Institute of Technology, Yonago College, Hikonacho 4448, Yonago 683-0854, Japan. tokumitsu@yonago-k.ac.jp.
Masahiro
M
Tokumitsu
Department of Electrical and Control Engineering, National Institute of Technology, Yonago College, Hikonacho 4448, Yonago 683-0854, Japan. tokumitsu@yonago.kosen-ac.jp.
Keisuke
K
Hasegawa
Department of Computer Science and Engineering, Toyohashi University of Technology, Hibarigaoka 1-1, Toyohashi 441-8580, Japan. ishida@cs.tut.ac.jp.
Yoshiteru
Y
Ishida
dynamic relational networks; profiling; self-recognizing networks; sensor networks; spatiotemporal interpolation