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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...

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Detalles Bibliográficos
Autores principales: D’Souza, Ollencio, Mukhopadhyay, Subhas Chandra, Sheng, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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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