Swarm Decision Table and Ensemble Search Methods in Fog Computing Environment: Case of Day-Ahead Prediction of Building Energy Demands Using IoT Sensors

Li, T; Fong, S; Li, X; Lu, Z; Gandomi, AH

Swarm Decision Table and Ensemble Search Methods in Fog Computing Environment: Case of Day-Ahead Prediction of Building Energy Demands Using IoT Sensors

Li, T Fong, S Li, X Lu, Z Gandomi, AH

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2020, 7, (3), pp. 2321-2342
Issue Date:: 2020-03-01

Closed Access

	Filename	Description	Size
	08928981.pdf		7.9 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Li, T
dc.contributor.author	Fong, S
dc.contributor.author	Li, X
dc.contributor.author	Lu, Z
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2021-03-08T05:48:50Z
dc.date.available	2021-03-08T05:48:50Z
dc.date.issued	2020-03-01
dc.identifier.citation	IEEE Internet of Things Journal, 2020, 7, (3), pp. 2321-2342
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/146923
dc.description.abstract	© 2014 IEEE. Building energy demand prediction (BEDP) concerns sensing the environment using the Internet of Things (IoT), making seamless decisions and responding and controlling certain devices automatically, intelligently, and quickly. Typically, the BEDP application can be empowered by fog computing where the sensed data are processed at the edge nodes rather than in a central cloud. The challenge is that in this decentralized IoT environment, the machine learning algorithm implemented at the fog node must learn a model from the incoming data accurately and fast. Which type of incremental learning algorithms, combined with traditional or swarm types of stochastic feature selection methods, are more suitable for BEDP? In this article, this topic is investigated in detail by introducing a new incremental learning model, the swarm decision table (SDT) in comparison with the classical decision tree. The simulation experiments using an empirical energy consumption data set that represent a typical IoT-connected BEDP scenario are tested, and the SDT shows superior results in terms of accuracy and time, demonstrating it as a suitable machine learning candidate in a fog computing environment.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2019.2958523
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	Swarm Decision Table and Ensemble Search Methods in Fog Computing Environment: Case of Day-Ahead Prediction of Building Energy Demands Using IoT Sensors
dc.type	Journal Article
utslib.citation.volume	7
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-03-08T05:48:44Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	3

Abstract:

© 2014 IEEE. Building energy demand prediction (BEDP) concerns sensing the environment using the Internet of Things (IoT), making seamless decisions and responding and controlling certain devices automatically, intelligently, and quickly. Typically, the BEDP application can be empowered by fog computing where the sensed data are processed at the edge nodes rather than in a central cloud. The challenge is that in this decentralized IoT environment, the machine learning algorithm implemented at the fog node must learn a model from the incoming data accurately and fast. Which type of incremental learning algorithms, combined with traditional or swarm types of stochastic feature selection methods, are more suitable for BEDP? In this article, this topic is investigated in detail by introducing a new incremental learning model, the swarm decision table (SDT) in comparison with the classical decision tree. The simulation experiments using an empirical energy consumption data set that represent a typical IoT-connected BEDP scenario are tested, and the SDT shows superior results in terms of accuracy and time, demonstrating it as a suitable machine learning candidate in a fog computing environment.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/146923