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• W4307268604 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 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." @default.
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• W4307268604 date "2022-10-24" @default.
• W4307268604 modified "2023-10-16" @default.
• W4307268604 title "Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge" @default.
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• W4307268604 doi "https://doi.org/10.3390/s22218143" @default.
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