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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/21/8143 |
| _version_ | 1797466543220588544 |
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| author | Ollencio D’Souza Subhas Chandra Mukhopadhyay Michael Sheng |
| author_facet | Ollencio D’Souza Subhas Chandra Mukhopadhyay Michael Sheng |
| author_sort | Ollencio D’Souza |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T18:41:17Z |
| format | Article |
| id | doaj.art-a37b038f20694ca9a4d3ddff31e8ba2a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:41:17Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a37b038f20694ca9a4d3ddff31e8ba2a2023-11-24T06:43:23ZengMDPI AGSensors1424-82202022-10-012221814310.3390/s22218143Health, Security and Fire Safety Process Optimisation Using Intelligence at the EdgeOllencio D’Souza0Subhas Chandra Mukhopadhyay1Michael Sheng2School of Engineering, Faculty of Science and Engineering, North Ryde Campus, Macquarie University, Sydney, NSW 2109, AustraliaSchool of Engineering, Faculty of Science and Engineering, North Ryde Campus, Macquarie University, Sydney, NSW 2109, AustraliaDepartment of Computing, Macquarie University, Sydney, NSW 2109, AustraliaThe 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.https://www.mdpi.com/1424-8220/22/21/8143TinyMLmachine learningedge analyticsenergy harvestinghealth caresecurity |
| spellingShingle | Ollencio D’Souza Subhas Chandra Mukhopadhyay Michael Sheng Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge Sensors TinyML machine learning edge analytics energy harvesting health care security |
| 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 | TinyML machine learning edge analytics energy harvesting health care security |
| url | https://www.mdpi.com/1424-8220/22/21/8143 |
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