Smart Energy Harvesting for Internet of Things

Farzad H. Panahi; Sogol Moshirvaziri; Yasin Mihemmedi; Fereidoun H. Panahi; Tomoaki Ohtsuki

doi:10.1109/SGC.2018.8777889

Smart Energy Harvesting for Internet of Things

Farzad H. Panahi, Sogol Moshirvaziri, Yasin Mihemmedi, Fereidoun H. Panahi, Tomoaki Ohtsuki

Department of Information and Computer Science

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

3 Citations (Scopus)

Abstract

In this paper, we study the performance of smart networks in terms of energy efficiency (EE) based on the internet of things (IoT) technology. Energy Harvesting (EH) from surroundings is a promising solution for compensating the limitation of batteries lifetime. Fuzzy Q-Learning algorithm has been proposed to determine the sensor movement strategy toward the dedicated power stations (PSs) as energy charger nodes for mobile sensors in order to improve network lifetime via acceleration of the battery charging process of mobile sensors. Simulation results for evaluation of the proposed strategy in term of normalized network lifetime compared to the random sensor motions, confirm the effectiveness of this approach as a practical smart EH for IoT networks.

Original language	English
Title of host publication	Proceedings - 2018 Smart Grid Conference, SGC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728111384
DOIs	https://doi.org/10.1109/SGC.2018.8777889
Publication status	Published - 2018 Nov
Event	2018 Smart Grid Conference, SGC 2018 - Sanandaj, Iran, Islamic Republic of Duration: 2018 Nov 28 → 2018 Nov 29

Publication series

Name	Proceedings - 2018 Smart Grid Conference, SGC 2018

Conference

Conference	2018 Smart Grid Conference, SGC 2018
Country/Territory	Iran, Islamic Republic of
City	Sanandaj
Period	18/11/28 → 18/11/29

Keywords

Smart sensor network
energy harvesting
internet of things
mobile sensors
network lifetime
power station

ASJC Scopus subject areas

Artificial Intelligence
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Control and Optimization

UN SDGs

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

Access to Document

10.1109/SGC.2018.8777889

Cite this

Smart Energy Harvesting for Internet of Things. / Panahi, Farzad H.; Moshirvaziri, Sogol; Mihemmedi, Yasin et al.

Proceedings - 2018 Smart Grid Conference, SGC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8777889 (Proceedings - 2018 Smart Grid Conference, SGC 2018).

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

Panahi, FH, Moshirvaziri, S, Mihemmedi, Y, Panahi, FH & Ohtsuki, T 2018, Smart Energy Harvesting for Internet of Things. in Proceedings - 2018 Smart Grid Conference, SGC 2018., 8777889, Proceedings - 2018 Smart Grid Conference, SGC 2018, Institute of Electrical and Electronics Engineers Inc., 2018 Smart Grid Conference, SGC 2018, Sanandaj, Iran, Islamic Republic of, 18/11/28. https://doi.org/10.1109/SGC.2018.8777889

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abstract = "In this paper, we study the performance of smart networks in terms of energy efficiency (EE) based on the internet of things (IoT) technology. Energy Harvesting (EH) from surroundings is a promising solution for compensating the limitation of batteries lifetime. Fuzzy Q-Learning algorithm has been proposed to determine the sensor movement strategy toward the dedicated power stations (PSs) as energy charger nodes for mobile sensors in order to improve network lifetime via acceleration of the battery charging process of mobile sensors. Simulation results for evaluation of the proposed strategy in term of normalized network lifetime compared to the random sensor motions, confirm the effectiveness of this approach as a practical smart EH for IoT networks.",

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AU - Moshirvaziri, Sogol

AU - Mihemmedi, Yasin

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AU - Ohtsuki, Tomoaki

PY - 2018/11

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AB - In this paper, we study the performance of smart networks in terms of energy efficiency (EE) based on the internet of things (IoT) technology. Energy Harvesting (EH) from surroundings is a promising solution for compensating the limitation of batteries lifetime. Fuzzy Q-Learning algorithm has been proposed to determine the sensor movement strategy toward the dedicated power stations (PSs) as energy charger nodes for mobile sensors in order to improve network lifetime via acceleration of the battery charging process of mobile sensors. Simulation results for evaluation of the proposed strategy in term of normalized network lifetime compared to the random sensor motions, confirm the effectiveness of this approach as a practical smart EH for IoT networks.

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Smart Energy Harvesting for Internet of Things

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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