@article{ref1,
title="Health, security and fire safety process optimisation using intelligence at the edge",
journal="Sensors (Basel)",
year="2022",
author="D'Souza, Ollencio and Mukhopadhyay, Subhas Chandra and Sheng, Michael",
volume="22",
number="21",
pages="e8143-e8143",
abstract="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 &quot;wake up&quot; 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 &quot;edge&quot;, 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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22218143",
url="http://dx.doi.org/10.3390/s22218143"
}