## 2 Safety Autonomous Platform for Data-Driven Risk Management  
Figure 1 represents the proposed SAP architecture that cooperates with SMGs to provide data-driven risk management. It mainly comprises a data visualization component to manage workplace and worker safety based on a geographic information system (GIS), a main engine as AI modeling tools to manage multimodal ensemble AI models for on-site AI engines, and a safety data management component designed by the practical Hadoop ecosystem. The safety data management component supports four functions of SMG status management: data management, video stream management, and AI modeling and engine management. The GIS-based data visualization component is an integrated dashboard to comprehensively monitor and manage the safety status of workplaces and workers based on spatial information on the map. It consists of six management units: inventory and configuration, analytics, statistics, deployment, video, and fault. For instance, the deployment management unit connects and exchanges deployment information with the Ansible in the safety data management component, and it helps configure systems, deploy software, and orchestrate advanced workflows to support application deployment, system updates, and more. The video management unit transmits video processing uniform resource locators (URLs) to the real-time streaming protocol (RTSP) stream unit in the safety data management component, and it receives live video streams from the RTSP server in the SMG. In particular, the fault management unit receives the alarm notification from an on-site AI engine in the SMG and minimizes the risks of accidents at work by taking appropriate action to keep their workers safe and healthy. The main engine creates, reads, updates, and deletes multimodal ensemble AI models, running on an on-site AI engine in the SMG, suitable for different workplaces and workers, by interworking between spark and visualization/AI common database (DB) units in the safety data management component. The safety data management component forms the basis of the proposed SAP, which was designed and implemented using the Apache Hadoop ecosystem, where big data processing and analysis involve several stages, including data collection, cleaning, integration, analytics, and visualization [15]. It collects live video streams from the camera installed on the SMG to inspect and identify workplace hazards for risk recognition or assessment, and it conducts live video streams from the camera on a worker's helmet and biometric index (BI) data from a worker's wristband for safety situation logging to keep him or her safe and healthy. All live video streams are temporarily stored on the RTSP server in the SMG. After that, they are transmitted to the RTSP stream unit and stored on the video storage. The BI data, such as body temperature, electrocardiogram, heart rate,  
Figure 1: Safety autonomous platform architecture and its overall cooperation with safety mobility gateways on providing data-driven risk management. etc., are transmitted to the long-range wide-area network (LoRaWAN) server specifically designed for Internet of Things (IoT) applications via the long-range (LoRa) proxy built on top of the LoRa modulation technique [16]. In addition, the safety data management component collects the SMG status information such as text-based log files from the filebeat unit and numeric metrics and statistical data (CPU usage, memory utilization, network statistics, etc.) from the metricbeat unit. Actually, Kafka is in charge of collecting and aggregating data streams such as the BI data and the SMG status information. It is well known as a distributed data streaming platform that can publish, subscribe to, store, and process data streams in real time [17], and it can handle data streams from multiple sources (LoRa proxy/LoRaWAN server and filebeats/metricbeats) and deliver them to multiple consumers (NiFi/Hive/HUE and Logstash/Elasticsearch/Kibana).