Orchestration‐based mechanism for sampling adaptation in sensing‐based applications

Abstract Currently, the world witnesses a boom in the sensing‐based applications where the number of connected devices is becoming higher than the people. Such small sensing devices are now deployed in billions around the world, collecting data about the surroundings and reporting them to the data a...

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Autores principales:	H. Harb, H. Baalbaki, C. Abou Jaoude, A. Jaber
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 City planning HT165.5-169.9
Acceso en línea:	https://doaj.org/article/c990dcc28bd24c928d9c1fbd4d379e56
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spelling	oai:doaj.org-article:c990dcc28bd24c928d9c1fbd4d379e562021-11-22T16:30:42ZOrchestration‐based mechanism for sampling adaptation in sensing‐based applications2631-768010.1049/smc2.12002https://doaj.org/article/c990dcc28bd24c928d9c1fbd4d379e562021-09-01T00:00:00Zhttps://doi.org/10.1049/smc2.12002https://doaj.org/toc/2631-7680Abstract Currently, the world witnesses a boom in the sensing‐based applications where the number of connected devices is becoming higher than the people. Such small sensing devices are now deployed in billions around the world, collecting data about the surroundings and reporting them to the data analysis centres. This fact allows a better understanding of the world and helps to reduce the effects of potential risks. However, while the benefits of such devices are real and significant, sensing‐based applications face two major challenges: big data collection and restricted power of sensor battery. In order to overcome these challenges, data reduction and sampling sensor adaptation techniques have been proposed to reduce data collection and to save the sensor energy. The authors propose an orchestration‐based mechanism (OM) for adapting the sampling rate of the sensors in the network. OM is two‐fold: first, it proposes a data transmission model at the sensor level, based on the clustering and Spearman coefficient, in order to reduce the amount of data transmitted to the sink; second, it proposes a sampling rate mechanism at the cluster‐head level that allows searching the similarity between data collected by the neighbouring sensors, and then to adapt their sensing frequencies accordingly. A set of simulations on real sensor data have been conducted to evaluate the efficiency of OM, in terms of data reduction and energy conservation, compared to other exiting techniques.H. HarbH. BaalbakiC. Abou JaoudeA. JaberWileyarticleEngineering (General). Civil engineering (General)TA1-2040City planningHT165.5-169.9ENIET Smart Cities, Vol 3, Iss 3, Pp 158-170 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 City planning HT165.5-169.9
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 City planning HT165.5-169.9 H. Harb H. Baalbaki C. Abou Jaoude A. Jaber Orchestration‐based mechanism for sampling adaptation in sensing‐based applications
description	Abstract Currently, the world witnesses a boom in the sensing‐based applications where the number of connected devices is becoming higher than the people. Such small sensing devices are now deployed in billions around the world, collecting data about the surroundings and reporting them to the data analysis centres. This fact allows a better understanding of the world and helps to reduce the effects of potential risks. However, while the benefits of such devices are real and significant, sensing‐based applications face two major challenges: big data collection and restricted power of sensor battery. In order to overcome these challenges, data reduction and sampling sensor adaptation techniques have been proposed to reduce data collection and to save the sensor energy. The authors propose an orchestration‐based mechanism (OM) for adapting the sampling rate of the sensors in the network. OM is two‐fold: first, it proposes a data transmission model at the sensor level, based on the clustering and Spearman coefficient, in order to reduce the amount of data transmitted to the sink; second, it proposes a sampling rate mechanism at the cluster‐head level that allows searching the similarity between data collected by the neighbouring sensors, and then to adapt their sensing frequencies accordingly. A set of simulations on real sensor data have been conducted to evaluate the efficiency of OM, in terms of data reduction and energy conservation, compared to other exiting techniques.
format	article
author	H. Harb H. Baalbaki C. Abou Jaoude A. Jaber
author_facet	H. Harb H. Baalbaki C. Abou Jaoude A. Jaber
author_sort	H. Harb
title	Orchestration‐based mechanism for sampling adaptation in sensing‐based applications
title_short	Orchestration‐based mechanism for sampling adaptation in sensing‐based applications
title_full	Orchestration‐based mechanism for sampling adaptation in sensing‐based applications
title_fullStr	Orchestration‐based mechanism for sampling adaptation in sensing‐based applications
title_full_unstemmed	Orchestration‐based mechanism for sampling adaptation in sensing‐based applications
title_sort	orchestration‐based mechanism for sampling adaptation in sensing‐based applications
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/c990dcc28bd24c928d9c1fbd4d379e56
work_keys_str_mv	AT hharb orchestrationbasedmechanismforsamplingadaptationinsensingbasedapplications AT hbaalbaki orchestrationbasedmechanismforsamplingadaptationinsensingbasedapplications AT caboujaoude orchestrationbasedmechanismforsamplingadaptationinsensingbasedapplications AT ajaber orchestrationbasedmechanismforsamplingadaptationinsensingbasedapplications
_version_	1718417523048710144

Orchestration‐based mechanism for sampling adaptation in sensing‐based applications

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