A data-driven approach to increasing the lifetime of IoT sensor nodes

Abstract Data transmission accounts for significant energy consumption in wireless sensor networks where streaming data is generated by the sensors. This impedes their use in many settings, including livestock monitoring over large pastures (which forms our target application). We present Ambrosia,...

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Autores principales:	Shikhar Suryavansh, Abu Benna, Chris Guest, Somali Chaterji
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f980b56254124a82a592967f8cc56989
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spelling	oai:doaj.org-article:f980b56254124a82a592967f8cc569892021-11-21T12:18:42ZA data-driven approach to increasing the lifetime of IoT sensor nodes10.1038/s41598-021-01431-y2045-2322https://doaj.org/article/f980b56254124a82a592967f8cc569892021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01431-yhttps://doaj.org/toc/2045-2322Abstract Data transmission accounts for significant energy consumption in wireless sensor networks where streaming data is generated by the sensors. This impedes their use in many settings, including livestock monitoring over large pastures (which forms our target application). We present Ambrosia, a lightweight protocol that utilizes a window-based timeseries forecasting mechanism for data reduction. Ambrosia employs a configurable error threshold to ensure that the accuracy of end applications is unaffected by the data transfer reduction. Experimental evaluations using LoRa and BLE on a real livestock monitoring deployment demonstrate 60% reduction in data transmission and a 2 $$\times$$ × increase in battery lifetime.Shikhar SuryavanshAbu BennaChris GuestSomali ChaterjiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shikhar Suryavansh Abu Benna Chris Guest Somali Chaterji A data-driven approach to increasing the lifetime of IoT sensor nodes
description	Abstract Data transmission accounts for significant energy consumption in wireless sensor networks where streaming data is generated by the sensors. This impedes their use in many settings, including livestock monitoring over large pastures (which forms our target application). We present Ambrosia, a lightweight protocol that utilizes a window-based timeseries forecasting mechanism for data reduction. Ambrosia employs a configurable error threshold to ensure that the accuracy of end applications is unaffected by the data transfer reduction. Experimental evaluations using LoRa and BLE on a real livestock monitoring deployment demonstrate 60% reduction in data transmission and a 2 $$\times$$ × increase in battery lifetime.
format	article
author	Shikhar Suryavansh Abu Benna Chris Guest Somali Chaterji
author_facet	Shikhar Suryavansh Abu Benna Chris Guest Somali Chaterji
author_sort	Shikhar Suryavansh
title	A data-driven approach to increasing the lifetime of IoT sensor nodes
title_short	A data-driven approach to increasing the lifetime of IoT sensor nodes
title_full	A data-driven approach to increasing the lifetime of IoT sensor nodes
title_fullStr	A data-driven approach to increasing the lifetime of IoT sensor nodes
title_full_unstemmed	A data-driven approach to increasing the lifetime of IoT sensor nodes
title_sort	data-driven approach to increasing the lifetime of iot sensor nodes
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f980b56254124a82a592967f8cc56989
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A data-driven approach to increasing the lifetime of IoT sensor nodes

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