Collaborative Worker Safety Prediction Mechanism Using Federated Learning Assisted Edge Intelligence in Outdoor Construction Environment
Monitoring construction site safety through physical observations is inherently flawed due to the complex and dynamic nature of construction sites. To overcome these challenges and enhance worker safety management, decentralized model training-assisted edge intelligence emerges as a promising soluti...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10267959/ |
| _version_ | 1797661512901328896 |
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| author | Sa Jim Soe Moe Bong Wan Kim Anam Nawaz Khan Xu Rongxu Nguyen Anh Tuan Kwangsoo Kim Do Hyeun Kim |
| author_facet | Sa Jim Soe Moe Bong Wan Kim Anam Nawaz Khan Xu Rongxu Nguyen Anh Tuan Kwangsoo Kim Do Hyeun Kim |
| author_sort | Sa Jim Soe Moe |
| collection | DOAJ |
| description | Monitoring construction site safety through physical observations is inherently flawed due to the complex and dynamic nature of construction sites. To overcome these challenges and enhance worker safety management, decentralized model training-assisted edge intelligence emerges as a promising solution. However, despite the potential benefits, our investigation reveals that no research for worker safety prediction has been grounded in the Federated Learning (FL) approach. In this context, we present a novel approach to worker safety prediction, leveraging FL in outdoor construction environments while preserving the privacy and security of sensitive data. Our methodology involves deploying sensor-based IoT devices at construction sites to collect highly granular spatial and temporal weather, building, and worker data. This data is then collaboratively utilized for training Deep Neural Network (DNN) models on the edge nodes in a cross-silos manner. To implement our approach, we establish a test-bed utilizing the EdgeX framework and constrained devices such as Raspberry Pi 4Bs, acting as edge nodes. Following the collaborative training, the resultant global model is deployed on participating nodes for edge inference, ensuring optimal network resource utilization and data privacy. The experimental results demonstrate the efficacy of the proposed approach in improving the utilization of construction safety management systems and reducing the risk of accidents and fatalities in the future. The outcome is a system that exhibits enhanced speed and responsiveness, a crucial aspect for time-sensitive applications such as the prediction of worker safety. |
| first_indexed | 2024-03-11T18:45:39Z |
| format | Article |
| id | doaj.art-627d53d8e3134cde85e8d16f16a939b0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T18:45:39Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-627d53d8e3134cde85e8d16f16a939b02023-10-11T23:00:24ZengIEEEIEEE Access2169-35362023-01-011110901010902610.1109/ACCESS.2023.332071610267959Collaborative Worker Safety Prediction Mechanism Using Federated Learning Assisted Edge Intelligence in Outdoor Construction EnvironmentSa Jim Soe Moe0https://orcid.org/0009-0007-6013-1239Bong Wan Kim1https://orcid.org/0000-0003-2688-5823Anam Nawaz Khan2https://orcid.org/0000-0001-6260-5820Xu Rongxu3https://orcid.org/0000-0002-4902-0681Nguyen Anh Tuan4https://orcid.org/0000-0003-0294-4884Kwangsoo Kim5Do Hyeun Kim6https://orcid.org/0000-0002-3457-2301Department of Computer Engineering, Jeju National University, Jeju-si, Republic of KoreaElectronics and Telecommunications Research Institute (ETRI), Daejeon, Republic of KoreaDepartment of Computer Engineering, Jeju National University, Jeju-si, Republic of KoreaBig Data Research Center, Jeju National University, Jeju, Republic of KoreaDepartment of Computer Engineering, Jeju National University, Jeju-si, Republic of KoreaElectronics and Telecommunications Research Institute (ETRI), Daejeon, Republic of KoreaDepartment of Computer Engineering, Jeju National University, Jeju-si, Republic of KoreaMonitoring construction site safety through physical observations is inherently flawed due to the complex and dynamic nature of construction sites. To overcome these challenges and enhance worker safety management, decentralized model training-assisted edge intelligence emerges as a promising solution. However, despite the potential benefits, our investigation reveals that no research for worker safety prediction has been grounded in the Federated Learning (FL) approach. In this context, we present a novel approach to worker safety prediction, leveraging FL in outdoor construction environments while preserving the privacy and security of sensitive data. Our methodology involves deploying sensor-based IoT devices at construction sites to collect highly granular spatial and temporal weather, building, and worker data. This data is then collaboratively utilized for training Deep Neural Network (DNN) models on the edge nodes in a cross-silos manner. To implement our approach, we establish a test-bed utilizing the EdgeX framework and constrained devices such as Raspberry Pi 4Bs, acting as edge nodes. Following the collaborative training, the resultant global model is deployed on participating nodes for edge inference, ensuring optimal network resource utilization and data privacy. The experimental results demonstrate the efficacy of the proposed approach in improving the utilization of construction safety management systems and reducing the risk of accidents and fatalities in the future. The outcome is a system that exhibits enhanced speed and responsiveness, a crucial aspect for time-sensitive applications such as the prediction of worker safety.https://ieeexplore.ieee.org/document/10267959/Worker safetyoutdoor construction sitefederated learningedge computingEdgeXInternet of Things |
| spellingShingle | Sa Jim Soe Moe Bong Wan Kim Anam Nawaz Khan Xu Rongxu Nguyen Anh Tuan Kwangsoo Kim Do Hyeun Kim Collaborative Worker Safety Prediction Mechanism Using Federated Learning Assisted Edge Intelligence in Outdoor Construction Environment IEEE Access Worker safety outdoor construction site federated learning edge computing EdgeX Internet of Things |
| title | Collaborative Worker Safety Prediction Mechanism Using Federated Learning Assisted Edge Intelligence in Outdoor Construction Environment |
| title_full | Collaborative Worker Safety Prediction Mechanism Using Federated Learning Assisted Edge Intelligence in Outdoor Construction Environment |
| title_fullStr | Collaborative Worker Safety Prediction Mechanism Using Federated Learning Assisted Edge Intelligence in Outdoor Construction Environment |
| title_full_unstemmed | Collaborative Worker Safety Prediction Mechanism Using Federated Learning Assisted Edge Intelligence in Outdoor Construction Environment |
| title_short | Collaborative Worker Safety Prediction Mechanism Using Federated Learning Assisted Edge Intelligence in Outdoor Construction Environment |
| title_sort | collaborative worker safety prediction mechanism using federated learning assisted edge intelligence in outdoor construction environment |
| topic | Worker safety outdoor construction site federated learning edge computing EdgeX Internet of Things |
| url | https://ieeexplore.ieee.org/document/10267959/ |
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