Runtime Adaptive IoMT Node on Multi-Core Processor Platform

The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. Thanks to innovative technologies, latest-generation communication networks, and state-of-the-art portable devices, IoTM opens up new scenarios for data collection and continu...

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Autores principales:	Matteo Antonio Scrugli, Paolo Meloni, Carlo Sau, Luigi Raffo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	adaptive system health information management Internet of Things low-power electronics multi-core processing neural networks Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/65fa09a836284bcab9b772d6db4c9c26
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id	oai:doaj.org-article:65fa09a836284bcab9b772d6db4c9c26
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spelling	oai:doaj.org-article:65fa09a836284bcab9b772d6db4c9c262021-11-11T15:36:43ZRuntime Adaptive IoMT Node on Multi-Core Processor Platform10.3390/electronics102125722079-9292https://doaj.org/article/65fa09a836284bcab9b772d6db4c9c262021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2572https://doaj.org/toc/2079-9292The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. Thanks to innovative technologies, latest-generation communication networks, and state-of-the-art portable devices, IoTM opens up new scenarios for data collection and continuous patient monitoring. Two very important aspects should be considered to make the most of this paradigm. For the first aspect, moving the processing task from the cloud to the edge leads to several advantages, such as responsiveness, portability, scalability, and reliability of the sensor node. For the second aspect, in order to increase the accuracy of the system, state-of-the-art cognitive algorithms based on artificial intelligence and deep learning must be integrated. Sensory nodes often need to be battery powered and need to remain active for a long time without a different power source. Therefore, one of the challenges to be addressed during the design and development of IoMT devices concerns energy optimization. Our work proposes an implementation of cognitive data analysis based on deep learning techniques on resource-constrained computing platform. To handle power efficiency, we introduced a component called Adaptive runtime Manager (ADAM). This component takes care of reconfiguring the hardware and software of the device dynamically during the execution, in order to better adapt it to the workload and the required operating mode. To test the high computational load on a multi-core system, the Orlando prototype board by STMicroelectronics, cognitive analysis of Electrocardiogram (ECG) traces have been adopted, considering single-channel and six-channel simultaneous cases. Experimental results show that by managing the sensory node configuration at runtime, energy savings of at least 15% can be achieved.Matteo Antonio ScrugliPaolo MeloniCarlo SauLuigi RaffoMDPI AGarticleadaptive systemhealth information managementInternet of Thingslow-power electronicsmulti-core processingneural networksElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2572, p 2572 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	adaptive system health information management Internet of Things low-power electronics multi-core processing neural networks Electronics TK7800-8360
spellingShingle	adaptive system health information management Internet of Things low-power electronics multi-core processing neural networks Electronics TK7800-8360 Matteo Antonio Scrugli Paolo Meloni Carlo Sau Luigi Raffo Runtime Adaptive IoMT Node on Multi-Core Processor Platform
description	The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. Thanks to innovative technologies, latest-generation communication networks, and state-of-the-art portable devices, IoTM opens up new scenarios for data collection and continuous patient monitoring. Two very important aspects should be considered to make the most of this paradigm. For the first aspect, moving the processing task from the cloud to the edge leads to several advantages, such as responsiveness, portability, scalability, and reliability of the sensor node. For the second aspect, in order to increase the accuracy of the system, state-of-the-art cognitive algorithms based on artificial intelligence and deep learning must be integrated. Sensory nodes often need to be battery powered and need to remain active for a long time without a different power source. Therefore, one of the challenges to be addressed during the design and development of IoMT devices concerns energy optimization. Our work proposes an implementation of cognitive data analysis based on deep learning techniques on resource-constrained computing platform. To handle power efficiency, we introduced a component called Adaptive runtime Manager (ADAM). This component takes care of reconfiguring the hardware and software of the device dynamically during the execution, in order to better adapt it to the workload and the required operating mode. To test the high computational load on a multi-core system, the Orlando prototype board by STMicroelectronics, cognitive analysis of Electrocardiogram (ECG) traces have been adopted, considering single-channel and six-channel simultaneous cases. Experimental results show that by managing the sensory node configuration at runtime, energy savings of at least 15% can be achieved.
format	article
author	Matteo Antonio Scrugli Paolo Meloni Carlo Sau Luigi Raffo
author_facet	Matteo Antonio Scrugli Paolo Meloni Carlo Sau Luigi Raffo
author_sort	Matteo Antonio Scrugli
title	Runtime Adaptive IoMT Node on Multi-Core Processor Platform
title_short	Runtime Adaptive IoMT Node on Multi-Core Processor Platform
title_full	Runtime Adaptive IoMT Node on Multi-Core Processor Platform
title_fullStr	Runtime Adaptive IoMT Node on Multi-Core Processor Platform
title_full_unstemmed	Runtime Adaptive IoMT Node on Multi-Core Processor Platform
title_sort	runtime adaptive iomt node on multi-core processor platform
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/65fa09a836284bcab9b772d6db4c9c26
work_keys_str_mv	AT matteoantonioscrugli runtimeadaptiveiomtnodeonmulticoreprocessorplatform AT paolomeloni runtimeadaptiveiomtnodeonmulticoreprocessorplatform AT carlosau runtimeadaptiveiomtnodeonmulticoreprocessorplatform AT luigiraffo runtimeadaptiveiomtnodeonmulticoreprocessorplatform
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Runtime Adaptive IoMT Node on Multi-Core Processor Platform

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