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Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge
The proliferation of sensors to capture parametric measures or event data over a myriad of networking topologies is growing exponentially to improve our daily lives. Large amounts of data must be shared on constrained network infrastructure, increasing delays and loss of valuable real-time informati...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9659114/ https://www.ncbi.nlm.nih.gov/pubmed/36365840 http://dx.doi.org/10.3390/s22218143 |
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author | D’Souza, Ollencio Mukhopadhyay, Subhas Chandra Sheng, Michael |
author_facet | D’Souza, Ollencio Mukhopadhyay, Subhas Chandra Sheng, Michael |
author_sort | D’Souza, Ollencio |
collection | PubMed |
description | The proliferation of sensors to capture parametric measures or event data over a myriad of networking topologies is growing exponentially to improve our daily lives. Large amounts of data must be shared on constrained network infrastructure, increasing delays and loss of valuable real-time information. Our research presents a solution for the health, security, safety, and fire domains to obtain temporally synchronous, credible and high-resolution data from sensors to maintain the temporal hierarchy of reported events. We developed a multisensor fusion framework with energy conservation via domain-specific “wake up” triggers that turn on low-power model-driven microcontrollers using machine learning (TinyML) models. We investigated optimisation techniques using anomaly detection modes to deliver real-time insights in demanding life-saving situations. Using energy-efficient methods to analyse sensor data at the point of creation, we facilitated a pathway to provide sensor customisation at the “edge”, where and when it is most needed. We present the application and generalised results in a real-life health care scenario and explain its application and benefits in other named researched domains. |
format | Online Article Text |
id | pubmed-9659114 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96591142022-11-15 Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge D’Souza, Ollencio Mukhopadhyay, Subhas Chandra Sheng, Michael Sensors (Basel) Article The proliferation of sensors to capture parametric measures or event data over a myriad of networking topologies is growing exponentially to improve our daily lives. Large amounts of data must be shared on constrained network infrastructure, increasing delays and loss of valuable real-time information. Our research presents a solution for the health, security, safety, and fire domains to obtain temporally synchronous, credible and high-resolution data from sensors to maintain the temporal hierarchy of reported events. We developed a multisensor fusion framework with energy conservation via domain-specific “wake up” triggers that turn on low-power model-driven microcontrollers using machine learning (TinyML) models. We investigated optimisation techniques using anomaly detection modes to deliver real-time insights in demanding life-saving situations. Using energy-efficient methods to analyse sensor data at the point of creation, we facilitated a pathway to provide sensor customisation at the “edge”, where and when it is most needed. We present the application and generalised results in a real-life health care scenario and explain its application and benefits in other named researched domains. MDPI 2022-10-24 /pmc/articles/PMC9659114/ /pubmed/36365840 http://dx.doi.org/10.3390/s22218143 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article D’Souza, Ollencio Mukhopadhyay, Subhas Chandra Sheng, Michael Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge |
title | Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge |
title_full | Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge |
title_fullStr | Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge |
title_full_unstemmed | Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge |
title_short | Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge |
title_sort | health, security and fire safety process optimisation using intelligence at the edge |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9659114/ https://www.ncbi.nlm.nih.gov/pubmed/36365840 http://dx.doi.org/10.3390/s22218143 |
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