Energy harvesting network with wireless distributed computing

Bulky processing tasks are expected to burden the limited resources of energy harvesters by draining the stored energy, and thereby, reaching rapidly to energy causality constraint. In such scenario, energy harvesters flip into sleep mode, and thereby, the execution time of the next task will be de...

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Main Authors:	Alfaqawi, Mohammed, Habaebi, Mohamed Hadi, Islam, Md. Rafiqul, Siddiqi, Mohammad Umar
Format:	Article
Language:	English English
Published:	IEEE Explore 2019
Subjects:	TK5101 Telecommunication. Including telegraphy, radio, radar, television
Online Access:	http://irep.iium.edu.my/70850/ http://irep.iium.edu.my/70850/ http://irep.iium.edu.my/70850/ http://irep.iium.edu.my/70850/1/2019%20IEEE%20System%20Journal.pdf http://irep.iium.edu.my/70850/7/70850_Energy%20Harvesting%20Network%20With%20Wireless%20Distributed%20Computing_Scopus.pdf

id	iium-70850
recordtype	eprints
spelling	iium-708502019-11-24T15:57:19Z http://irep.iium.edu.my/70850/ Energy harvesting network with wireless distributed computing Alfaqawi, Mohammed Habaebi, Mohamed Hadi Islam, Md. Rafiqul Siddiqi, Mohammad Umar TK5101 Telecommunication. Including telegraphy, radio, radar, television Bulky processing tasks are expected to burden the limited resources of energy harvesters by draining the stored energy, and thereby, reaching rapidly to energy causality constraint. In such scenario, energy harvesters flip into sleep mode, and thereby, the execution time of the next task will be delayed until the energy harvesters revert back into active mode. To tackle this problem, this paper proposes a novel energy harvesting network (EHN) that deploys wireless distributed computing (WDC) network within the decision making process (DMP). The DMP is formulated as constrained partially observable Markov decision process in order to enable the energy harvesters to act under uncertainty. Furthermore, various challenges of WDC networks, e.g., nominating the collaborating nodes and task allocation, have been addressed herein. Unlike conventional research works on WDC networks, a system model is proposed for WDC network based on divisible load theory instead of graph theory. In addition, an adaptive task allocation algorithm is proposed to distribute the task efficiently among the collaborating nodes. Finally, the novel EHN system is analyzed and compared against the conventional research works on WDC, offloading computing, and local computing-EHN, where the proposed system is found to outperform in terms of energy and delay. IEEE Explore 2019-02 Article PeerReviewed application/pdf en http://irep.iium.edu.my/70850/1/2019%20IEEE%20System%20Journal.pdf application/pdf en http://irep.iium.edu.my/70850/7/70850_Energy%20Harvesting%20Network%20With%20Wireless%20Distributed%20Computing_Scopus.pdf Alfaqawi, Mohammed and Habaebi, Mohamed Hadi and Islam, Md. Rafiqul and Siddiqi, Mohammad Umar (2019) Energy harvesting network with wireless distributed computing. IEEE Systems Journal, early access. pp. 1-12. ISSN 1932-8184 E-ISSN 1937-9234 https://ieeexplore.ieee.org/document/8636195 10.1109/JSYST.2019.2893248
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK5101 Telecommunication. Including telegraphy, radio, radar, television
spellingShingle	TK5101 Telecommunication. Including telegraphy, radio, radar, television Alfaqawi, Mohammed Habaebi, Mohamed Hadi Islam, Md. Rafiqul Siddiqi, Mohammad Umar Energy harvesting network with wireless distributed computing
description	Bulky processing tasks are expected to burden the limited resources of energy harvesters by draining the stored energy, and thereby, reaching rapidly to energy causality constraint. In such scenario, energy harvesters flip into sleep mode, and thereby, the execution time of the next task will be delayed until the energy harvesters revert back into active mode. To tackle this problem, this paper proposes a novel energy harvesting network (EHN) that deploys wireless distributed computing (WDC) network within the decision making process (DMP). The DMP is formulated as constrained partially observable Markov decision process in order to enable the energy harvesters to act under uncertainty. Furthermore, various challenges of WDC networks, e.g., nominating the collaborating nodes and task allocation, have been addressed herein. Unlike conventional research works on WDC networks, a system model is proposed for WDC network based on divisible load theory instead of graph theory. In addition, an adaptive task allocation algorithm is proposed to distribute the task efficiently among the collaborating nodes. Finally, the novel EHN system is analyzed and compared against the conventional research works on WDC, offloading computing, and local computing-EHN, where the proposed system is found to outperform in terms of energy and delay.
format	Article
author	Alfaqawi, Mohammed Habaebi, Mohamed Hadi Islam, Md. Rafiqul Siddiqi, Mohammad Umar
author_facet	Alfaqawi, Mohammed Habaebi, Mohamed Hadi Islam, Md. Rafiqul Siddiqi, Mohammad Umar
author_sort	Alfaqawi, Mohammed
title	Energy harvesting network with wireless distributed computing
title_short	Energy harvesting network with wireless distributed computing
title_full	Energy harvesting network with wireless distributed computing
title_fullStr	Energy harvesting network with wireless distributed computing
title_full_unstemmed	Energy harvesting network with wireless distributed computing
title_sort	energy harvesting network with wireless distributed computing
publisher	IEEE Explore
publishDate	2019
url	http://irep.iium.edu.my/70850/ http://irep.iium.edu.my/70850/ http://irep.iium.edu.my/70850/ http://irep.iium.edu.my/70850/1/2019%20IEEE%20System%20Journal.pdf http://irep.iium.edu.my/70850/7/70850_Energy%20Harvesting%20Network%20With%20Wireless%20Distributed%20Computing_Scopus.pdf
first_indexed	2023-09-18T21:40:35Z
last_indexed	2023-09-18T21:40:35Z
_version_	1777413112495865856

Energy harvesting network with wireless distributed computing

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