Elena, Rodríguez and Hiroshi, Tanaka and Dr. Samuel, Johnson (2022) Iot-Enabled Workforce Safety Monitoring Real-Time Compliance Optimization Using Edge AI, Predictive Analytics, and Multi-Sensor Integration in Regulated Industries. Best Journal of Innovation in Science, Research and Development, 1 (1). pp. 59-72. ISSN 2835-3579

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Abstract

Workforce safety remains a critical challenge across regulated industries such as oil and gas, mining, construction, aviation, and manufacturing, where annual global losses from workplace accidents exceed $3 trillion (ILO, 2023). Traditional safety monitoring systems often rely on manual inspections, lagging indicators, and fragmented compliance processes, leading to delayed risk detection, higher incident rates, and regulatory penalties. To address these challenges, this paper proposes an IoT-enabled workforce safety monitoring framework that leverages multi-sensor integration, edge AI, and predictive analytics to enable real-time compliance optimization in enterprise environments.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	18 Sep 2025 07:10
Last Modified:	18 Sep 2025 07:10
URI:	http://eprints.umsida.ac.id/id/eprint/16353

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