Harvesting and Energy aware Adaptive Sampling Algorithm for guaranteeing self-sustainability in Wireless Sensor Networks

Changmin Lee, Jai Yong Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Despite of continuous evolution of Wireless Sensor Networks, Energy exhaustion issue of wireless sensors is still remained. Thus, it is difficult to guarantee the self-sustainability of each sensor. Researchers in areas of energy conservation and energy harvesting have been consecutively developing new methods to increase the lifetime of a sensor. One of the methods is the Adaptive Sampling Algorithm (ASA). This is an effective algorithm to reduce the wasted sampling energy by using optimal sampling rate for monitoring. In this paper, we propose two advanced Adaptive Sampling Algorithms, Resuscitation Adaptive Sampling Algorithm (RASA), and Compensation Adaptive Sampling Algorithm (CASA). And also we propose the Adaptive Sensor Management Scheme (ASMS) to apply ASA, CASA and RASA according to the energy state of sensors. RASA is the algorithm to set low sampling rate and guarantee the self-sustainability when energy sate of sensors is too low. Sensor nodes in CASA can be recharged some energy by saving the consumption energy when the harvesting quality is good. ASMS scheme combines with these two algorithms. ASMS scheme classifies the sensors of WSN into three classes according to the Current energy state and Current energy harvesting quality. Nodes in each class can be applied different optimal sampling rate to achieve the self-sustainability. To prove the efficiency of proposed algorithms, we setup the micro dust air pollution application consisted of Arduino Uno, Zigbee and dust sensor and simulate through MATLAB. Simulation shows that ASMS can save consumption energy maximum 50% and guarantee the genuine self-sustainability of nodes. And also we compare ASMS with existing ASA.

Original languageEnglish
Title of host publication31st International Conference on Information Networking, ICOIN 2017
PublisherIEEE Computer Society
Pages57-62
Number of pages6
ISBN (Electronic)9781509051243
DOIs
Publication statusPublished - 2017 Apr 13
Event31st International Conference on Information Networking, ICOIN 2017 - Da Nang, Viet Nam
Duration: 2017 Jan 112017 Jan 13

Publication series

NameInternational Conference on Information Networking
ISSN (Print)1976-7684

Other

Other31st International Conference on Information Networking, ICOIN 2017
CountryViet Nam
CityDa Nang
Period17/1/1117/1/13

Fingerprint

Sustainable development
Wireless sensor networks
Sampling
Sensors
Resuscitation
Energy harvesting
Electron energy levels
Dust
Energy utilization
Zigbee
Set theory
Air pollution
Sensor nodes
MATLAB
Energy conservation

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems

Cite this

Lee, C., & Lee, J. Y. (2017). Harvesting and Energy aware Adaptive Sampling Algorithm for guaranteeing self-sustainability in Wireless Sensor Networks. In 31st International Conference on Information Networking, ICOIN 2017 (pp. 57-62). [7899475] (International Conference on Information Networking). IEEE Computer Society. https://doi.org/10.1109/ICOIN.2017.7899475
Lee, Changmin ; Lee, Jai Yong. / Harvesting and Energy aware Adaptive Sampling Algorithm for guaranteeing self-sustainability in Wireless Sensor Networks. 31st International Conference on Information Networking, ICOIN 2017. IEEE Computer Society, 2017. pp. 57-62 (International Conference on Information Networking).
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title = "Harvesting and Energy aware Adaptive Sampling Algorithm for guaranteeing self-sustainability in Wireless Sensor Networks",
abstract = "Despite of continuous evolution of Wireless Sensor Networks, Energy exhaustion issue of wireless sensors is still remained. Thus, it is difficult to guarantee the self-sustainability of each sensor. Researchers in areas of energy conservation and energy harvesting have been consecutively developing new methods to increase the lifetime of a sensor. One of the methods is the Adaptive Sampling Algorithm (ASA). This is an effective algorithm to reduce the wasted sampling energy by using optimal sampling rate for monitoring. In this paper, we propose two advanced Adaptive Sampling Algorithms, Resuscitation Adaptive Sampling Algorithm (RASA), and Compensation Adaptive Sampling Algorithm (CASA). And also we propose the Adaptive Sensor Management Scheme (ASMS) to apply ASA, CASA and RASA according to the energy state of sensors. RASA is the algorithm to set low sampling rate and guarantee the self-sustainability when energy sate of sensors is too low. Sensor nodes in CASA can be recharged some energy by saving the consumption energy when the harvesting quality is good. ASMS scheme combines with these two algorithms. ASMS scheme classifies the sensors of WSN into three classes according to the Current energy state and Current energy harvesting quality. Nodes in each class can be applied different optimal sampling rate to achieve the self-sustainability. To prove the efficiency of proposed algorithms, we setup the micro dust air pollution application consisted of Arduino Uno, Zigbee and dust sensor and simulate through MATLAB. Simulation shows that ASMS can save consumption energy maximum 50{\%} and guarantee the genuine self-sustainability of nodes. And also we compare ASMS with existing ASA.",
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Lee, C & Lee, JY 2017, Harvesting and Energy aware Adaptive Sampling Algorithm for guaranteeing self-sustainability in Wireless Sensor Networks. in 31st International Conference on Information Networking, ICOIN 2017., 7899475, International Conference on Information Networking, IEEE Computer Society, pp. 57-62, 31st International Conference on Information Networking, ICOIN 2017, Da Nang, Viet Nam, 17/1/11. https://doi.org/10.1109/ICOIN.2017.7899475

Harvesting and Energy aware Adaptive Sampling Algorithm for guaranteeing self-sustainability in Wireless Sensor Networks. / Lee, Changmin; Lee, Jai Yong.

31st International Conference on Information Networking, ICOIN 2017. IEEE Computer Society, 2017. p. 57-62 7899475 (International Conference on Information Networking).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Despite of continuous evolution of Wireless Sensor Networks, Energy exhaustion issue of wireless sensors is still remained. Thus, it is difficult to guarantee the self-sustainability of each sensor. Researchers in areas of energy conservation and energy harvesting have been consecutively developing new methods to increase the lifetime of a sensor. One of the methods is the Adaptive Sampling Algorithm (ASA). This is an effective algorithm to reduce the wasted sampling energy by using optimal sampling rate for monitoring. In this paper, we propose two advanced Adaptive Sampling Algorithms, Resuscitation Adaptive Sampling Algorithm (RASA), and Compensation Adaptive Sampling Algorithm (CASA). And also we propose the Adaptive Sensor Management Scheme (ASMS) to apply ASA, CASA and RASA according to the energy state of sensors. RASA is the algorithm to set low sampling rate and guarantee the self-sustainability when energy sate of sensors is too low. Sensor nodes in CASA can be recharged some energy by saving the consumption energy when the harvesting quality is good. ASMS scheme combines with these two algorithms. ASMS scheme classifies the sensors of WSN into three classes according to the Current energy state and Current energy harvesting quality. Nodes in each class can be applied different optimal sampling rate to achieve the self-sustainability. To prove the efficiency of proposed algorithms, we setup the micro dust air pollution application consisted of Arduino Uno, Zigbee and dust sensor and simulate through MATLAB. Simulation shows that ASMS can save consumption energy maximum 50% and guarantee the genuine self-sustainability of nodes. And also we compare ASMS with existing ASA.

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PB - IEEE Computer Society

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Lee C, Lee JY. Harvesting and Energy aware Adaptive Sampling Algorithm for guaranteeing self-sustainability in Wireless Sensor Networks. In 31st International Conference on Information Networking, ICOIN 2017. IEEE Computer Society. 2017. p. 57-62. 7899475. (International Conference on Information Networking). https://doi.org/10.1109/ICOIN.2017.7899475