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Power Consumption Prediction Method for Train‐Health Monitoring Wireless Sensor Networks
Author(s) -
Kawamura Tomoki,
Ryuo Satoko,
Iwasawa Nagateru
Publication year - 2018
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.12070
Subject(s) - wireless sensor network , decoupling (probability) , key distribution in wireless sensor networks , node (physics) , real time computing , computer science , energy consumption , power consumption , wireless , sensor node , power (physics) , engineering , computer network , wireless network , control engineering , telecommunications , electrical engineering , physics , quantum mechanics , structural engineering
SUMMARY In recent years, much research has been conducted on the use of wireless sensor networks (WSN) for monitoring the conditions of railway vehicles. In WSNs for vehicle condition monitoring, the changes in the network configuration caused by both the coupling and decoupling of vehicles and communication environment changes resulting from vehicular motion make the power consumption of the sensor node unpredictable based on the existing approaches. In this paper, we propose a method of predicting the sensor node power consumption for WSNs used for vehicle condition monitoring based on the time‐series Monte Carlo method.