The sampling period estimation based adaptive sampling algorithm for a self-sustainable disaster monitoring system

Changmin Lee; Seong Lyun Kim

doi:10.1109/EuCNC48522.2020.9200960

The sampling period estimation based adaptive sampling algorithm for a self-sustainable disaster monitoring system

Changmin Lee, Seong Lyun Kim

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

To introduce the most energy-efficient adaptive sampling algorithm for the disaster monitoring system, this study proposes a novel algorithm based on sampling period estimation for gathering only valuable information. It is called an adaptive sampling algorithm for monitoring (ASA-m). This method estimates the next sampling period to get the information that is required by the monitoring system. In order to estimate this time period, the proposed algorithm uses an advanced trend estimation method considering an energy transfer mechanism, i.e heat, or wave. The sampling period prediction is based on estimating changes in energy sources from the trend of prior environmental change. Through this method, sensor nodes can predict the environmental changing velocity by using an estimated energy source. Based on this property, each sensor node estimates the sampling period for collecting the next semantic information. It has some advantages to minimize the consumed energy of sensor nodes and the network traffic by collecting meaningless data. As a result, the proposed algorithm can reduce 65.4% of the energy consumption and 50% of the sampling count.

Original language	English
Title of host publication	2020 European Conference on Networks and Communications, EuCNC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	165-169
Number of pages	5
ISBN (Electronic)	9781728143552
DOIs	https://doi.org/10.1109/EuCNC48522.2020.9200960
Publication status	Published - 2020 Jun
Event	29th European Conference on Networks and Communications, EuCNC 2020 - Virtual, Dubrovnik, Croatia Duration: 2020 Jun 15 → 2020 Jun 18

Publication series

Name	2020 European Conference on Networks and Communications, EuCNC 2020

Conference

Conference	29th European Conference on Networks and Communications, EuCNC 2020
Country/Territory	Croatia
City	Virtual, Dubrovnik
Period	20/6/15 → 20/6/18

Bibliographical note

Funding Information:
This research was supported by a grant to Bio-Mimetic Robot Research Center Funded by Defense Acquisition Program Administration, and by Agency for Defense Development (UD190018ID).

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Hardware and Architecture
Signal Processing
Instrumentation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/EuCNC48522.2020.9200960

Cite this

Lee, C., & Kim, S. L. (2020). The sampling period estimation based adaptive sampling algorithm for a self-sustainable disaster monitoring system. In 2020 European Conference on Networks and Communications, EuCNC 2020 (pp. 165-169). [9200960] (2020 European Conference on Networks and Communications, EuCNC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EuCNC48522.2020.9200960

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abstract = "To introduce the most energy-efficient adaptive sampling algorithm for the disaster monitoring system, this study proposes a novel algorithm based on sampling period estimation for gathering only valuable information. It is called an adaptive sampling algorithm for monitoring (ASA-m). This method estimates the next sampling period to get the information that is required by the monitoring system. In order to estimate this time period, the proposed algorithm uses an advanced trend estimation method considering an energy transfer mechanism, i.e heat, or wave. The sampling period prediction is based on estimating changes in energy sources from the trend of prior environmental change. Through this method, sensor nodes can predict the environmental changing velocity by using an estimated energy source. Based on this property, each sensor node estimates the sampling period for collecting the next semantic information. It has some advantages to minimize the consumed energy of sensor nodes and the network traffic by collecting meaningless data. As a result, the proposed algorithm can reduce 65.4% of the energy consumption and 50% of the sampling count.",

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Lee, C & Kim, SL 2020, The sampling period estimation based adaptive sampling algorithm for a self-sustainable disaster monitoring system. in 2020 European Conference on Networks and Communications, EuCNC 2020., 9200960, 2020 European Conference on Networks and Communications, EuCNC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 165-169, 29th European Conference on Networks and Communications, EuCNC 2020, Virtual, Dubrovnik, Croatia, 20/6/15. https://doi.org/10.1109/EuCNC48522.2020.9200960

The sampling period estimation based adaptive sampling algorithm for a self-sustainable disaster monitoring system. / Lee, Changmin; Kim, Seong Lyun.

2020 European Conference on Networks and Communications, EuCNC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 165-169 9200960 (2020 European Conference on Networks and Communications, EuCNC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: This research was supported by a grant to Bio-Mimetic Robot Research Center Funded by Defense Acquisition Program Administration, and by Agency for Defense Development (UD190018ID). Publisher Copyright: © 2020 IEEE.

PY - 2020/6

Y1 - 2020/6

N2 - To introduce the most energy-efficient adaptive sampling algorithm for the disaster monitoring system, this study proposes a novel algorithm based on sampling period estimation for gathering only valuable information. It is called an adaptive sampling algorithm for monitoring (ASA-m). This method estimates the next sampling period to get the information that is required by the monitoring system. In order to estimate this time period, the proposed algorithm uses an advanced trend estimation method considering an energy transfer mechanism, i.e heat, or wave. The sampling period prediction is based on estimating changes in energy sources from the trend of prior environmental change. Through this method, sensor nodes can predict the environmental changing velocity by using an estimated energy source. Based on this property, each sensor node estimates the sampling period for collecting the next semantic information. It has some advantages to minimize the consumed energy of sensor nodes and the network traffic by collecting meaningless data. As a result, the proposed algorithm can reduce 65.4% of the energy consumption and 50% of the sampling count.

AB - To introduce the most energy-efficient adaptive sampling algorithm for the disaster monitoring system, this study proposes a novel algorithm based on sampling period estimation for gathering only valuable information. It is called an adaptive sampling algorithm for monitoring (ASA-m). This method estimates the next sampling period to get the information that is required by the monitoring system. In order to estimate this time period, the proposed algorithm uses an advanced trend estimation method considering an energy transfer mechanism, i.e heat, or wave. The sampling period prediction is based on estimating changes in energy sources from the trend of prior environmental change. Through this method, sensor nodes can predict the environmental changing velocity by using an estimated energy source. Based on this property, each sensor node estimates the sampling period for collecting the next semantic information. It has some advantages to minimize the consumed energy of sensor nodes and the network traffic by collecting meaningless data. As a result, the proposed algorithm can reduce 65.4% of the energy consumption and 50% of the sampling count.

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Lee C, Kim SL. The sampling period estimation based adaptive sampling algorithm for a self-sustainable disaster monitoring system. In 2020 European Conference on Networks and Communications, EuCNC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 165-169. 9200960. (2020 European Conference on Networks and Communications, EuCNC 2020). doi: 10.1109/EuCNC48522.2020.9200960

The sampling period estimation based adaptive sampling algorithm for a self-sustainable disaster monitoring system

Abstract

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Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

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