A Novel Dual Prediction Scheme for Data Communication Reduction in IoT-Based Monitoring Systems

Fathalla, A; Salah, A; Mohamed, MA; Lestari, NI; Bekhit, M

A Novel Dual Prediction Scheme for Data Communication Reduction in IoT-Based Monitoring Systems

Fathalla, A Salah, A Mohamed, MA Lestari, NI Bekhit, M

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: IoT as a Service, 2022, 421 LNICST, pp. 208-220
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Fathalla, A
dc.contributor.author	Salah, A
dc.contributor.author	Mohamed, MA
dc.contributor.author	Lestari, NI
dc.contributor.author	Bekhit, M https://orcid.org/0000-0002-8115-4233
dc.date	2021-12-13
dc.date.accessioned	2023-02-08T01:24:48Z
dc.date.available	2023-02-08T01:24:48Z
dc.date.issued	2022-01-01
dc.identifier.citation	IoT as a Service, 2022, 421 LNICST, pp. 208-220
dc.identifier.isbn	9783030959869
dc.identifier.issn	1867-8211
dc.identifier.issn	1867-822X
dc.identifier.uri	http://hdl.handle.net/10453/165991
dc.description.abstract	Internet of things (IoT) based monitoring systems became commonplace. These systems are built upon a large number of devices and sensors. The data collection task of a large number of sensors and devices in an IoT system includes a massive number of data communications. The more the number of devices, the critical is the network bottleneck. In this context, the dual prediction scheme was proposed as a solution for mitigating the large size of communication volumes. The dual prediction scheme consists of a model for predicting future measurements based on historical data. This model is duplicated on both sides, the edge side (i.e., sensor) and the data collection device (i.e., cluster head). The literature includes several works which proposed many dual prediction schemes based on several techniques such as filters and moving average. The literature does not include utilizing the ensemble learning models. This motivates this work to investigate the gradient boosting regression model’s performance compared to the existing solutions. The proposed and state-of-the-art models are evaluated on a realistic dataset. The obtained results show that the proposed model outperforms the existing dual prediction schemes in terms of communication reduction.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	IoT as a Service
dc.relation.ispartof	EAI International Conference on Internet of Things as a Service
dc.relation.ispartofseries	Lecture Notes of the Institute for Computer Sciences
dc.relation.isbasedon	10.1007/978-3-030-95987-6_15
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Novel Dual Prediction Scheme for Data Communication Reduction in IoT-Based Monitoring Systems
dc.type	Conference Proceeding
utslib.citation.volume	421 LNICST
utslib.location.activity	Sydney, Australia
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-02-08T01:24:47Z
pubs.finish-date	2021-12-14
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2021-12-13
pubs.volume	421 LNICST
dc.location	Switzerland

Abstract:

Internet of things (IoT) based monitoring systems became commonplace. These systems are built upon a large number of devices and sensors. The data collection task of a large number of sensors and devices in an IoT system includes a massive number of data communications. The more the number of devices, the critical is the network bottleneck. In this context, the dual prediction scheme was proposed as a solution for mitigating the large size of communication volumes. The dual prediction scheme consists of a model for predicting future measurements based on historical data. This model is duplicated on both sides, the edge side (i.e., sensor) and the data collection device (i.e., cluster head). The literature includes several works which proposed many dual prediction schemes based on several techniques such as filters and moving average. The literature does not include utilizing the ensemble learning models. This motivates this work to investigate the gradient boosting regression model’s performance compared to the existing solutions. The proposed and state-of-the-art models are evaluated on a realistic dataset. The obtained results show that the proposed model outperforms the existing dual prediction schemes in terms of communication reduction.

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http://hdl.handle.net/10453/165991