Presentation 2015-01-26
Optimal Sampling for Kalman Filtering by Sensor Network Based on Energy Consumption and Information Entropy
Ken IMAI, Toshimitsu USHIO,
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Abstract(in English) We consider a sensor network that consists of a fusion center and a group of sensor nodes. The fusion center estimates the system state based on the data from the sensor nodes. As the number of sampling data increases, the entropy that is a measure of the uncertainty of the system state decreases. But, the number of sampling data increases, the energy consumption of the sensor network increases. There is a trade-off between the minimization of the entropy and the minimization of the energy consumption. We employs a weighted sum method to formulate this multi objective optimization problem as a single objective optimization problem. We solve the sampling schedule that achieves the optimal value of the single objective optimization problem.
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Keyword(in English) Sensor networks / sampling schedule / Kalman filter / entropy / energy consumption
Paper # MSS2014-74,SS2014-38
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Committee MSS
Conference Date 2015/1/19(1days)
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Registration To Mathematical Systems Science and its applications(MSS)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Optimal Sampling for Kalman Filtering by Sensor Network Based on Energy Consumption and Information Entropy
Sub Title (in English)
Keyword(1) Sensor networks
Keyword(2) sampling schedule
Keyword(3) Kalman filter
Keyword(4) entropy
Keyword(5) energy consumption
1st Author's Name Ken IMAI
1st Author's Affiliation Graduate school of Engineering Science, Osaka university()
2nd Author's Name Toshimitsu USHIO
2nd Author's Affiliation Graduate school of Engineering Science, Osaka university
Date 2015-01-26
Paper # MSS2014-74,SS2014-38
Volume (vol) vol.114
Number (no) 415
Page pp.pp.-
#Pages 6
Date of Issue